id
stringlengths 36
36
| status
stringclasses 2
values | inserted_at
timestamp[us]date 2025-04-09 16:14:38
2025-04-09 16:14:38
| updated_at
timestamp[us]date 2025-04-09 16:14:38
2025-05-26 14:27:41
| _server_id
stringlengths 36
36
| text
stringlengths 90
8.92k
| links
stringlengths 4
804
| span_label.responses
listlengths 1
1
| span_label.responses.users
sequencelengths 1
1
| span_label.responses.status
sequencelengths 1
1
| assess_ner.responses
sequencelengths 1
1
| assess_ner.responses.users
sequencelengths 1
1
| assess_ner.responses.status
sequencelengths 1
1
| assess_nel.responses
sequencelengths 1
1
| assess_nel.responses.users
sequencelengths 1
1
| assess_nel.responses.status
sequencelengths 1
1
| comments.responses
sequencelengths 1
1
| comments.responses.users
sequencelengths 1
1
| comments.responses.status
sequencelengths 1
1
| span_label.suggestion
listlengths 0
74
| span_label.suggestion.agent
null | span_label.suggestion.score
null |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
531264b9-ed7e-4e80-acde-defd72b78cf3
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:03:58.880000 |
0845658c-dc87-41d3-9811-b337d608522a
|
Short-term, automated car rental services, i.e., car sharing, are a solution that has been improving in urban transportation systems over the past few years. Due to the intensive expansion of the systems, service providers face increasing challenges in their competitiveness. One of them is to meet the customer expectations for the fleet of vehicles offered in the system. Although this aspect is noted primarily in the literature review on fleet optimization and management, there is a gap in research on the appropriate selection of vehicle models. In response, the article aimed to identify the vehicles best suited for car-sharing systems from the customer’s point of view. The selection of suitable vehicles was treated as a multi-criteria decision-making issue; therefore, the study used ELECTRE III—one of the multi-criteria decision-making methods. The work focuses on researching the opinions of users who rarely use car-sharing services in Poland. The most popular car models in 2021, equipped with internal combustion, hybrid, and electric engines, were selected for the analysis. The results indicate that the best suited cars are relatively large, spacious, and equipped with electric drive and represent the D segment of vehicles in Europe. In addition, these vehicles are to be equipped with a powerful engine, a spacious boot, and a fast battery charging time. Interestingly, small city cars, so far associated with car sharing, ranked the worst in the classification method. In addition, factors such as the warranty period associated with the quality of the vehicles, or the number of car doors, are not very important to users. The results support car-sharing operators in the process of selecting or modernizing a fleet of vehicles.
|
<li> <b>car:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>cars:</b> Car
|
[
[
{
"end": 25,
"label": "vehicleType",
"start": 22
},
{
"end": 52,
"label": "vehicleType",
"start": 49
},
{
"end": 627,
"label": "vehicleType",
"start": 624
},
{
"end": 930,
"label": "vehicleType",
"start": 927
},
{
"end": 979,
"label": "vehicleType",
"start": 976
},
{
"end": 1436,
"label": "vehicleType",
"start": 1433
},
{
"end": 1607,
"label": "vehicleType",
"start": 1604
},
{
"end": 1671,
"label": "vehicleType",
"start": 1668
},
{
"end": 350,
"label": "vehicleType",
"start": 342
},
{
"end": 607,
"label": "vehicleType",
"start": 599
},
{
"end": 713,
"label": "vehicleType",
"start": 705
},
{
"end": 1244,
"label": "vehicleType",
"start": 1236
},
{
"end": 1283,
"label": "vehicleType",
"start": 1275
},
{
"end": 1585,
"label": "vehicleType",
"start": 1577
},
{
"end": 1752,
"label": "vehicleType",
"start": 1744
},
{
"end": 543,
"label": "vehicleType",
"start": 536
},
{
"end": 1139,
"label": "vehicleType",
"start": 1135
},
{
"end": 1408,
"label": "vehicleType",
"start": 1404
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 25,
"label": "vehicleType",
"start": 22
},
{
"end": 52,
"label": "vehicleType",
"start": 49
},
{
"end": 627,
"label": "vehicleType",
"start": 624
},
{
"end": 930,
"label": "vehicleType",
"start": 927
},
{
"end": 979,
"label": "vehicleType",
"start": 976
},
{
"end": 1436,
"label": "vehicleType",
"start": 1433
},
{
"end": 1607,
"label": "vehicleType",
"start": 1604
},
{
"end": 1671,
"label": "vehicleType",
"start": 1668
},
{
"end": 350,
"label": "vehicleType",
"start": 342
},
{
"end": 607,
"label": "vehicleType",
"start": 599
},
{
"end": 713,
"label": "vehicleType",
"start": 705
},
{
"end": 1244,
"label": "vehicleType",
"start": 1236
},
{
"end": 1283,
"label": "vehicleType",
"start": 1275
},
{
"end": 1585,
"label": "vehicleType",
"start": 1577
},
{
"end": 1752,
"label": "vehicleType",
"start": 1744
},
{
"end": 543,
"label": "vehicleType",
"start": 536
},
{
"end": 25,
"label": "vehicleType",
"start": 22
},
{
"end": 52,
"label": "vehicleType",
"start": 49
},
{
"end": 627,
"label": "vehicleType",
"start": 624
},
{
"end": 930,
"label": "vehicleType",
"start": 927
},
{
"end": 979,
"label": "vehicleType",
"start": 976
},
{
"end": 1436,
"label": "vehicleType",
"start": 1433
},
{
"end": 1607,
"label": "vehicleType",
"start": 1604
},
{
"end": 1671,
"label": "vehicleType",
"start": 1668
},
{
"end": 1139,
"label": "vehicleType",
"start": 1135
},
{
"end": 1408,
"label": "vehicleType",
"start": 1404
}
] | null | null |
7b0a8d3c-5055-47de-a1c6-0cc9a29e4ab9
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:49:36.651000 |
a073ee97-a8c5-4dce-9361-42d7d8eac25c
|
Minutes needed to top up fuel/electricity to maximum fuel tank capacity or car battery capacity.
|
<li> <b>car:</b> Car
|
[
[
{
"end": 78,
"label": "vehicleType",
"start": 75
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 78,
"label": "vehicleType",
"start": 75
}
] | null | null |
1c7912e7-ddfd-495f-bc40-3c574f8f3c47
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:47:23.030000 |
18edd7f4-074f-4d32-9a40-32bae2f53bca
|
A smarter transport system that caters for social, economic and environmental sustainability is arguably one of the most critical prerequisites for creating pathways to more livable urban futures. This paper aims to provide a state-of-the-art analysis of a selection of mobility initiatives that may dictate the future of urban transportation and make cities smarter. These are mechanisms either recently introduced with encouraging uptake so far and much greater potential to contribute in a shift to a better transport paradigm or still in an embryonic stage of their development and yet to be embraced as powerful mechanisms that could change travel behaviour norms. Autonomous and connected vehicles are set to revolutionise the urban landscape by allowing machines to take over driving that for over a century has been exclusively a human activity, while electrical vehicles are already helping decarbonising the transport sector. Bus rapid transit has been steadily reinventing and rebranding conventional bus services revitalising the use of the humblest form of public transport, while hyperloop is an entirely new, disruptive, and somewhat provocative, travel mode proposition based on the use of sealed tube systems through which pods could travel free of air resistance with speeds exceeding 1000 km/h. Shared use mobility mechanisms like car-sharing, ride-sharing, ride-sourcing and public bicycles can help establishing a culture for using mobility resources on an as-needed basis, while mobility-as-a-service will take this sharing culture a step further, offering tailored mobility and trip planning packages that could entirely replace the need for privately owned modes of transport.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>Bus:</b> Bus<li> <b>bus:</b> Bus<li> <b>bicycles:</b> Cyclist
|
[
[
{
"end": 703,
"label": "vehicleType",
"start": 695
},
{
"end": 879,
"label": "vehicleType",
"start": 871
},
{
"end": 939,
"label": "vehicleType",
"start": 936
},
{
"end": 1015,
"label": "vehicleType",
"start": 1012
},
{
"end": 1410,
"label": "VRUType",
"start": 1402
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 703,
"label": "vehicleType",
"start": 695
},
{
"end": 879,
"label": "vehicleType",
"start": 871
},
{
"end": 939,
"label": "vehicleType",
"start": 936
},
{
"end": 1015,
"label": "vehicleType",
"start": 1012
},
{
"end": 1410,
"label": "VRUType",
"start": 1402
}
] | null | null |
f4ba3bb0-001f-4f00-8e5e-788f6ed00e7f
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:34:06.769000 |
17f9e1f1-de46-4bfa-ab01-d836424d5657
|
Ride-sharing (or carpooling) refers to a mode of transportation in which individual travellers share a vehicle for a trip and split travel costs such as gas, toll, and parking fees with others that have similar itineraries and time schedules [70].This sharing approach has an immediate and potentially easily measurable impact on mobility patterns since if three potential drivers, people that are not susceptible to shift to another mode of transportation, decide to share a ride this means that only one car will be used instead of three.In theory, ride-sharing is a system which combines the flexibility and speed of private cars with the reduced cost of fixed-line systems and is directly battling the negative externalities of single occupant car travel, which is the most unsustainable form of travel behaviour.Ride-sharing is relevant, and if presented in a potent way could be also particularly attractive, for commuters that want to go to work in a cost-effective and flexible way; there are many employers that promote and organise ride-sharing programmes for their staff.Advantages of ride-sharing for participants (both drivers and passengers), to society, and to the environment include saving travel costs, reducing travel time, mitigating traffic congestion, conserving fuel, and reducing air pollution [71,72]. Today, dedicated platforms allow drivers to post their rides online helping to mitigate many issues, which previously limited ride-sharing.These digital platforms help by establishing trust among strangers through rating and review systems, meaningful profiles, user verification, and automated booking and payment processes and by dramatically decreasing transactional cost for ride listing and search [73].These technological advancements, which will only continue to improve as IoT evolves and real-time monitoring and live matching capabilities become better, have already enabled the establishment of large ride-sharing initiatives like RelayRides, BlaBlaCar, or Carpooling.com that facilitate millions of trips per day.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>cars:</b> Car
|
[
[
{
"end": 110,
"label": "vehicleType",
"start": 103
},
{
"end": 509,
"label": "vehicleType",
"start": 506
},
{
"end": 751,
"label": "vehicleType",
"start": 748
},
{
"end": 632,
"label": "vehicleType",
"start": 628
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 110,
"label": "vehicleType",
"start": 103
},
{
"end": 509,
"label": "vehicleType",
"start": 506
},
{
"end": 751,
"label": "vehicleType",
"start": 748
},
{
"end": 632,
"label": "vehicleType",
"start": 628
}
] | null | null |
54e6e90e-2598-4a2e-84ec-27590d8c9bc3
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-23T21:39:26.002000 |
16cd1f80-e386-4351-9d69-653cc8140222
|
Industrial and transport activities are the two major sources of noise pollution in any metropolitan city. Lucknow city, the capital of the largest populated state Uttar Pradesh in India has an area of 310 sq. km and is rapidly growing as a commercial, industrial and trading centre of northern India. The population of Lucknow city as per census 2001 is 22.45 Lacs. It is expected that by the year 2021 it will make 45 Lacs. The total vehicle population in Lucknow city on 31 March 2008, was nearly 1 million with almost 80% two wheelers, 12% cars, 1.36% three wheelers, 0.45% buses etc. A study was carried out to assess the existing status of noise levels and its impacts on the environment with a possibility of further expansion of the city. Ambient noise levels were measured at different locations selected on the basis of land use such as silence, heavy traffic and residential and commercial zones. It was found that noise levels at all selected locations were much higher (75–90 dB) than the prescribed limits. The observed traffic volume and data on road geometry were used to predict noise levels using Federal Highway Administration Agency (FHWA) model and the calculated noise levels were compared with the observed levels for checking the suitability of this model for predicting the future levels. It was established that the results obtained by FHWA model were very close to the observed noise levels and that the model was suitable to be used for other similar metropolitan cities in India.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>two wheelers:</b> Motorcycle<li> <b>cars:</b> Car<li> <b>buses:</b> Bus
|
[
[
{
"end": 443,
"label": "vehicleType",
"start": 436
},
{
"end": 538,
"label": "vehicleType",
"start": 526
},
{
"end": 548,
"label": "vehicleType",
"start": 544
},
{
"end": 583,
"label": "vehicleType",
"start": 578
},
{
"end": 570,
"label": "vehicleType",
"start": 556
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"three wheelers\" should be identified as vehicleType, exactly like \"two wheelers\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 443,
"label": "vehicleType",
"start": 436
},
{
"end": 538,
"label": "vehicleType",
"start": 526
},
{
"end": 548,
"label": "vehicleType",
"start": 544
},
{
"end": 583,
"label": "vehicleType",
"start": 578
}
] | null | null |
926357f3-e504-49d9-9263-55f3ef10bdd0
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-20T14:44:01.170000 |
9099a45b-5e7a-4cd2-a047-819543563d1b
|
Road traffi c noise in most of the urban areas is increasing at an alarming rate which is a cause of concern for the residents living along the highways.Noise levels must be controlled in order to reduce its societal impacts.Reducing noise levels may be produced following Harris (1979) who suggested some noise control measures like: • motor vehicle control; • land use control; • highway planning and design; • buff er zones; • noise barriers; • using dead end streets for residential complexes; • depressing freeways and arterial roads below the ground level; • creating more gap between road and buildings; • constructing high rise buildings along the roads providing barrier for low rise buildings; • making external and internal sound insulated walls; • making double glazed windows.
|
<li> <b>motor vehicle:</b> Other Vehicle
|
[
[
{
"end": 350,
"label": "vehicleType",
"start": 337
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 350,
"label": "vehicleType",
"start": 337
}
] | null | null |
bc6c7d0b-4cbd-493e-af9b-72fc45e2fffb
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T11:49:58.834000 |
9668b511-cce8-4ebf-9b83-5e13ca2b0e5b
|
The work was dedicated to the subject of innovative autonomous vehicles on the transport market. The paper presents basic information about autonomous cars: a nomenclature characteristic of autonomous vehicles, along with the terms “automatic”, “autonomous”, “self-drive” and “driverless”. The article also presents various types of autonomous cars based on the most popular classifications in the world. The purpose of the work is to present basic issues related to autonomous vehicles.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>cars:</b> Car
|
[
[
{
"end": 71,
"label": "vehicleType",
"start": 52
},
{
"end": 209,
"label": "vehicleType",
"start": 190
},
{
"end": 155,
"label": "vehicleType",
"start": 140
},
{
"end": 348,
"label": "vehicleType",
"start": 333
},
{
"end": 486,
"label": "vehicleType",
"start": 467
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"We added the combined terms \"autonomous vehicle\" & \"autonomous car\" in the vehicleType category"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 71,
"label": "vehicleType",
"start": 63
},
{
"end": 209,
"label": "vehicleType",
"start": 201
},
{
"end": 486,
"label": "vehicleType",
"start": 478
},
{
"end": 155,
"label": "vehicleType",
"start": 151
},
{
"end": 348,
"label": "vehicleType",
"start": 344
}
] | null | null |
0a1b98f5-1a75-4399-ae79-58055ccbfbf0
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:13:29.949000 |
6edeeeac-e005-418a-afa1-fdbdcc27a9b7
|
Autonomous driving levels according to the NHTSA classification Level Description 0 The driver's task is to support all systems in the vehicle. 1 The vehicle is equipped with automatic versions of some systems (e.g., ESP systems, ABS, automatic braking), which can be activated spontaneously or with the help of the driver.However, the driver's task is to oversee system functioning.
|
<li> <b>0:</b> Level 0<li> <b>vehicle:</b> Other Vehicle<li> <b>1:</b> Level 1
|
[
[
{
"end": 83,
"label": "levelOfAutomation",
"start": 82
},
{
"end": 142,
"label": "vehicleType",
"start": 135
},
{
"end": 157,
"label": "vehicleType",
"start": 150
},
{
"end": 145,
"label": "levelOfAutomation",
"start": 144
},
{
"end": 25,
"label": "levelOfAutomation",
"start": 0
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 83,
"label": "levelOfAutomation",
"start": 82
},
{
"end": 142,
"label": "vehicleType",
"start": 135
},
{
"end": 157,
"label": "vehicleType",
"start": 150
},
{
"end": 145,
"label": "levelOfAutomation",
"start": 144
}
] | null | null |
2245515a-b795-4d2f-92e1-859e046df4e4
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:38:31.190000 |
b886e51a-2d88-4dad-86bd-716325a1c7b5
|
This article explored the development of electric vehicle (EV) charging stations in Thailand between 2015 and 2020. This research aimed to study the main players and examine their goals, strategies, and operations in the EV charging business as well as the key issues that these charging operators have encountered in developing charging stations. The authors collected qualitative data (direct interviews with managers, video interviews, news, research articles, industry reports and press releases of EV charging operators) and used a constant comparison approach to analyze the data. The study found that after 2015, the Thai government created technology-push policies to kick-start the investment in the EV charging station business (such as subsidies for charging stations, setting a temporary selling price for electricity and building an EV charging consortium). The main players in the Thai charging business include: (1) oil and gas companies; (2) electricity state enterprises; (3) green energy companies; (4) start-ups; and (5) automotive companies. The goals of investing in the charging business for the oil and gas incumbents were to find a new growth engine and to prepare for the potential disruption in the energy sector whereas the green energy companies and start-ups wanted to capture customer bases in this promising industry. These players tended to use a partnership strategy to expand charging networks at key locations (malls, restaurants, offices). Regarding the key issues in expanding the EV charging network, the operators suggested that the high upfront investment costs, small number of EV users, and the high electricity prices (from the demand charge and usage guarantee fee) make them ‘wait-and-see’ and cautiously expand the charging network. Finally, we found that the government tried to address the constraints by setting up a national EV policy committee to accelerate EV adoption and EV charging stations in Thailand. The committee also set a fixed and reduced electricity price for charging operators.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>EV:</b> Car
|
[
[
{
"end": 57,
"label": "vehicleType",
"start": 50
},
{
"end": 61,
"label": "vehicleType",
"start": 59
},
{
"end": 223,
"label": "vehicleType",
"start": 221
},
{
"end": 505,
"label": "vehicleType",
"start": 503
},
{
"end": 711,
"label": "vehicleType",
"start": 709
},
{
"end": 848,
"label": "vehicleType",
"start": 846
},
{
"end": 1520,
"label": "vehicleType",
"start": 1518
},
{
"end": 1621,
"label": "vehicleType",
"start": 1619
},
{
"end": 1877,
"label": "vehicleType",
"start": 1875
},
{
"end": 1911,
"label": "vehicleType",
"start": 1909
},
{
"end": 1927,
"label": "vehicleType",
"start": 1925
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EV\" should be recognized as \"other vehicle\" and not as \"car\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 57,
"label": "vehicleType",
"start": 50
},
{
"end": 61,
"label": "vehicleType",
"start": 59
},
{
"end": 223,
"label": "vehicleType",
"start": 221
},
{
"end": 505,
"label": "vehicleType",
"start": 503
},
{
"end": 711,
"label": "vehicleType",
"start": 709
},
{
"end": 848,
"label": "vehicleType",
"start": 846
},
{
"end": 1520,
"label": "vehicleType",
"start": 1518
},
{
"end": 1621,
"label": "vehicleType",
"start": 1619
},
{
"end": 1877,
"label": "vehicleType",
"start": 1875
},
{
"end": 1911,
"label": "vehicleType",
"start": 1909
},
{
"end": 1927,
"label": "vehicleType",
"start": 1925
}
] | null | null |
e37e2604-a12a-4834-acfe-5351f9f6e86a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:13:09.674000 |
15c75685-f0c3-43a6-9c77-35cdd8e6f256
|
Along with the tax incentives (see Table 1) for the investment in the EV industry in Thailand, the Board of Investment of Thailand provides five-year corporate tax exemption for the EV charging station business.The imported machinery and related raw materials used in the charging stations will also receive import-tax exemptions.In 2018, Energy Mahanakhon Co., Ltd. was granted tax privileges by the Board of Investment to build 3000 EV charging stations all over the country [65].
|
<li> <b>EV:</b> Car
|
[
[
{
"end": 72,
"label": "vehicleType",
"start": 70
},
{
"end": 184,
"label": "vehicleType",
"start": 182
},
{
"end": 437,
"label": "vehicleType",
"start": 435
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" could be other vehicles than cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 72,
"label": "vehicleType",
"start": 70
},
{
"end": 184,
"label": "vehicleType",
"start": 182
},
{
"end": 437,
"label": "vehicleType",
"start": 435
}
] | null | null |
23512198-38a6-412a-800a-be469f84d622
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T06:53:51.729000 |
558228e0-4031-4473-be2b-cd3aeee6ba01
|
This paper explores the factors affecting the uptake of hybrid and electric vehicles in the European Union (EU) using data from two extensive cross-sectional surveys. Each survey consists of 26,500 responses to a questionnaire that combines socio-economic and behavioral aspects. The share of respondents across the EU stating that they would certainly or probably consider purchasing a hybrid or a battery-powered electric vehicle (H&EV) in the near future rose from 32% in 2014 to 37.4% in 2018. There is, however, a high variability among EU member states, as well as across different socio-economic groups. Propensity is highly correlated with income, educational attainment, and urbanization level. In order to address the high degree of collinearity, we applied a machine learning classification model to analyze and explain the interaction between the variables that affected the expressed propensity to purchase such a vehicle. The findings highlight something largely missing from the literature, namely that local conditions and regional variation are a major, if not decisive, factor regarding purchasing choices. Seen from a policy perspective, this conclusion may provide guidance regarding how to support the take up of H&EVs through measures that are tailored to the specific needs at the local level.
|
<li> <b>hybrid:</b> Car<li> <b>electric vehicles:</b> Car<li> <b>hybrid:</b> Car<li> <b>electric vehicle:</b> Car<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 62,
"label": "vehicleType",
"start": 56
},
{
"end": 393,
"label": "vehicleType",
"start": 387
},
{
"end": 84,
"label": "vehicleType",
"start": 67
},
{
"end": 431,
"label": "vehicleType",
"start": 415
},
{
"end": 934,
"label": "vehicleType",
"start": 927
},
{
"end": 437,
"label": "vehicleType",
"start": 433
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"electric vehicle could be a two- or three- wheeler"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 62,
"label": "vehicleType",
"start": 56
},
{
"end": 393,
"label": "vehicleType",
"start": 387
},
{
"end": 84,
"label": "vehicleType",
"start": 67
},
{
"end": 62,
"label": "vehicleType",
"start": 56
},
{
"end": 393,
"label": "vehicleType",
"start": 387
},
{
"end": 431,
"label": "vehicleType",
"start": 415
},
{
"end": 431,
"label": "vehicleType",
"start": 424
},
{
"end": 934,
"label": "vehicleType",
"start": 927
}
] | null | null |
99186771-ee47-450c-973d-20c9b7fa6a04
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:24:05.605000 |
b3e81fdb-a5fb-429d-b196-fb662631585c
|
Our approach was based on a classification model that uses the results of two cross-sectional surveys, carried out across all 28 EU member states in 2014 [35] and 2018 [36].The model predicts whether a respondent is likely to have stated a high propensity to purchase a hybrid or electric car using the respondent's answers to the other survey questions as independent variables.Since most of the survey questions correspond to socio-economic or behavioral variables, a robust predictive model allows for the identification of the variables that affect this choice the most and can help policymakers and other stakeholders in improving their strategies to increase the market share of new car technologies. We used the gradient boosting method, a machine learning technique with numerous applications in predictive modelling [37].The applications of machine learning classifiers in transport are rapidly increasing.For example, Choi and Ahn [38] conducted a multi-level analysis combining individual and urban characteristics regarding model trip preferences, Focas and Christidis [39] analyzed car use, while Cheng et al. [40] developed a mode choice model using a random forest method.Machine learning approaches allow for a higher model precision in modelling user choices than conventional stated preference methods [41]. The main advantages of using a data driven, tree-based approach, as opposed to other discrete choice methods frequently used in the literature (multinomial logit, hybrid choice, or latent class models), are the ability to account for the co-dependence between independent variables and the absence of strict assumptions concerning the model structure and parameters.Moreover, in the particular case of the surveys used here, gradient boosting classifiers allow for the use of categorical variables without assuming proportional odds, i.e., constant distances or monotonous relationships between their values.Machine learning approaches offer a wide range of metrics to estimate model precision, as well as tools for the interpretation of the results.A potential disadvantage of such approaches stems from the nature of the issue to be analyzed: the large number of non-linear tree-like decisions may obscure the physical interpretation of the resulting model [42,43].Additionally, unless a suitable validation strategy is in place, models that overfit with respect to their underlying data may not generalize well when applied to new data. The dependent variable of our model was a binary variable (0/1) indicating whether the user had a high propensity to purchase a hybrid or electric vehicle (value = 1) or not (value = 0).The binary variable was a direct transformation of the original multiclass response variable in order to simplify the model and concentrate on the two responses of main interest (high propensity corresponds to "certainly yes" or "probably yes").The modelling setup followed a 40-40-20% random split of the dataset into training, testing, and validation sets, respectively.This a standard practice in machine learning predictive modelling.The model was trained on the training set and its precision was evaluated using the testing set.The validation set was used for the final evaluation to ensure that the model did not overfit and that it could generalize sufficiently well to previously unseen data.As an evaluation metric, we used AUC (area under the curve), the most frequently used aggregate measure of performance for classification models.AUC is a function of the true positive and false positive rates of the model results and can range from 0 (when predictions are 100% wrong) to 1 (when all predictions are correct).Regarding feature engineering, we created additional variables based on the two-way interaction of categorical variables.The categorical variables, original and engineered, were subsequently transformed into numeric variables using the standard mean likelihood encoding technique (probability of the dependent variable, conditional on each category value). We constructed two different base models using the variables included in the year 2014 survey (model A) and the year 2018 survey (model B) respectively.The difference between the A and B models was the inclusion in the 2018 survey of four questions concerning Information and Communication Technologies (ICT) use and solutions, online-shopping, and teleworking, which were not present in the 2014 survey, plus the resulting two-way interaction variables. The train-test-validation procedure was performed in three combinations: All three models achieved a satisfactory level of precision, explaining a considerable part of the variance in user preferences.There were obviously additional factors that influenced the responses and were not covered by the surveys used, but the overall model performance was sufficient for conclusions on certain aspects to be drawn.The stability of the validation set AUC when using different years in model A (0.789 with 2014 data, 0.791 with 2018 data) suggests that the model could be generalized using different datasets and that the relative importance of its variables remained constant between the two points in time. Likelihood by age and income played an important role in all three models, while the importance of the region's level of education and urbanization declined between 2014 and 2018.The average likelihood at a regional level and at a center/suburb level remained equally important between time points.The importance of gender, already relatively low in 2014, decreased further.The change in the importance of the variables suggests that while the adopter profile remained centered on specific age and income groups and was still dependent on the local conditions of the respondents' region and urbanization level (Section 3 and Figures 567), the concept of H&EVs has become more familiar to respondents in other socio-economic groups.As a result, the variation in the propensity to purchase appeared to decrease between 2014 and 2018. The considerable improvement between model A and model B when using the year 2018 data (AUC from 0.791 to 0.821) could be attributed to the inclusion of the four new questions in the year 2018 survey, which allowed the model to capture additional information that could enrich the interpretation of certain behavioral aspects.The number of variables that were considered as important (i.e., contribute to variance) were 115 in model A. Adding the year 2018 new survey questions and their derived variables increased the number of important variables to 164 in model B. Figures 123show the twelve variables with the highest relative importance of the boosted decision trees within each model, ranked in terms of the contribution of each variable to the model.Variable names that include "likelihood" were derived from the mean likelihood encoding (MLE) of category combinations.
|
<li> <b>hybrid or electric car:</b> Car<li> <b>car:</b> Car<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 292,
"label": "vehicleType",
"start": 270
},
{
"end": 292,
"label": "vehicleType",
"start": 289
},
{
"end": 692,
"label": "vehicleType",
"start": 689
},
{
"end": 1098,
"label": "vehicleType",
"start": 1095
},
{
"end": 2620,
"label": "vehicleType",
"start": 2613
},
{
"end": 2620,
"label": "vehicleType",
"start": 2604
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 292,
"label": "vehicleType",
"start": 270
},
{
"end": 292,
"label": "vehicleType",
"start": 289
},
{
"end": 692,
"label": "vehicleType",
"start": 689
},
{
"end": 1098,
"label": "vehicleType",
"start": 1095
},
{
"end": 2620,
"label": "vehicleType",
"start": 2613
}
] | null | null |
37ffef40-d0ee-4a9c-8dad-ea01e3764120
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T11:46:55.207000 |
8b936570-bf59-4384-8188-eba371003999
|
Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods.
|
<li> <b>Autonomous Underwater Vehicle (AUV):</b> Other Vehicle<li> <b>AUV:</b> Other Vehicle<li> <b>Inertial Navigation System (INS):</b> Other Sensor<li> <b>Doppler Velocity Log (DVL):</b> Other Sensor<li> <b>Sonar:</b> Other Sensor
|
[
[
{
"end": 1321,
"label": "sensorType",
"start": 1289
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"AUVs are not of interest in CCAM (we are mostly interested in ground Autonomous Vehicles)\nSonars and DVLs are not of interest in CCAM\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 159,
"label": "vehicleType",
"start": 124
},
{
"end": 158,
"label": "vehicleType",
"start": 155
},
{
"end": 575,
"label": "vehicleType",
"start": 572
},
{
"end": 831,
"label": "vehicleType",
"start": 828
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
},
{
"end": 1263,
"label": "vehicleType",
"start": 1260
},
{
"end": 1321,
"label": "sensorType",
"start": 1289
},
{
"end": 1349,
"label": "sensorType",
"start": 1323
},
{
"end": 1372,
"label": "sensorType",
"start": 1367
}
] | null | null |
39c826e4-4cd7-4dc4-973a-ce4e81cc31a3
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T07:09:24.247000 |
ecbd48c6-1247-4daa-8337-c0438fb46972
|
When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.
|
<li> <b>underwater vehicles:</b> Other Vehicle
|
[
[
{
"end": 24,
"label": "vehicleType",
"start": 16
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"underwater vehicles\" are not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 24,
"label": "vehicleType",
"start": 5
}
] | null | null |
03ebd117-f54a-4d09-92e0-7bd0bf013833
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:06:06.089000 |
e177b65c-3621-4dc6-bebb-0255356121bc
|
Underwater image enhancement algorithms have high requirements for engineering applications.FUnIE-GAN has a memory requirement of 17 MB and runs at 25.4 frames per second on an embedded platform (Nvidia Jetson TX2), and 148.5 frames per second on a graphics card (Nvidia GTX 1080).The running speed on the robot CPU (Intel Core-I3 6100U) is 7.9 frames.The algorithm FUnIE-GAN-NIQE proposed in this paper retains the advantage of a fast calculation speed of FUnIE-GAN, and can be fully applied to the real-time underwater image enhancement process of an underwater robot.
|
<li> <b>robot:</b> Other Vehicle<li> <b>underwater robot:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"robot\" should not be recognized as vehicleType\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 311,
"label": "vehicleType",
"start": 306
},
{
"end": 569,
"label": "vehicleType",
"start": 564
},
{
"end": 569,
"label": "vehicleType",
"start": 553
}
] | null | null |
d57fd9b1-adeb-4432-970d-9ec4882d6b12
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:49:25.025000 |
7eae8975-983e-4fcd-bf9d-1c14fad96a03
|
The energy transition, a process in which fossil fuels are being replaced by cleaner sources of energy, comes with many challenges. The intrinsic uncertainty associated with renewable energy sources has led to a search for complementary technologies to tackle those issues. In recent years, the use of electric vehicles (EVs) has been studied as an alternative for storage, leading to a much more complex market structure. Small participants are now willing to provide energy, helping to keep the desired balance of supply and demand. In this paper, we analyse the electricity spot market, providing a model where EVs decide to participate depending on the underlying conditions. We study pricing rules adapted from versions currently in use in electricity markets, and focus on two of them for our experimental settings: integer programming (IP) and extended locational marginal (ELM) pricing. We particularly pay attention to the properties those prices might satisfy, and numerically test them under some scenarios representing different levels of participation of EVs and an active demand side. Our results suggest that IP pricing generally derives larger individual uplift payments and further produces public prices that are not well aligned with the final payments of market participants, leading to distortions in the market.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car
|
[
[
{
"end": 325,
"label": "vehicleType",
"start": 302
},
{
"end": 617,
"label": "vehicleType",
"start": 614
},
{
"end": 1071,
"label": "vehicleType",
"start": 1068
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" are not cars exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 325,
"label": "vehicleType",
"start": 302
},
{
"end": 324,
"label": "vehicleType",
"start": 321
},
{
"end": 617,
"label": "vehicleType",
"start": 614
},
{
"end": 1071,
"label": "vehicleType",
"start": 1068
}
] | null | null |
42803fb7-09ae-41a7-80fe-97885d0f2519
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:00:04.540000 |
bf9c9a80-5b13-4843-9050-88a905d07ad3
|
The bidding language for EVs essentially takes into account technical restrictions for the state of charge and the (dis-)charging quantities.The state of charge for each EV v and period t is denoted by the decision variable soc v,t (soc v,t ≥ 0).Likewise, the charging quantity of EV v in period t is defined by q + v,t (q + v,t ≥ 0), and the discharging quantity by q - v,t (q - v,t ≥ 0).Given the capacity requirements SOC v,t (SOC v,t ≥ 0) and SOC v,t (SOC v,t ≥ 0), the electricity demand for driving ∆SOC v,t (∆SOC v,t ≥ 0), as well as the (dis-)charging efficiencies η + (0 ≤ η + ≤ 1) and η -(0 ≤ η -≤ 1), the state of charge is defined by the following equations: Constraints (2e) and (2f) formulate the state of charge as a function of scheduled (dis-)charge plus unscheduled power demand (i.e., driving).By Equation (2g), it is assumed that the vehicles are fully charged at the end of each day.(This condition can be easily changed.We designed in this way to fit in the experimental setting.An alternative is to consider them fully charged before departure.)Constraint (2h) establishes that the state of charge is limited by the minimum and maximum capacity requirements of the vehicle's battery. For the (dis-)charging processes, a limit on the maximum (dis-)charged power ≥ 0) must be maintained.Note that the parameter has a value of zero when EV v is unplugged in period t.Furthermore, it is only possible to either charge or discharge an EV v in period t.This binary decision is denoted by the variable m v,t (m v,t ∈ {0, 1}), which equals one if EV v is charging in period t and zero if it is discharging.M is a parameter described by the following equations:
|
<li> <b>EVs:</b> Car<li> <b>EV:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
"end": 28,
"label": "vehicleType",
"start": 25
},
{
"end": 172,
"label": "vehicleType",
"start": 170
},
{
"end": 283,
"label": "vehicleType",
"start": 281
},
{
"end": 1359,
"label": "vehicleType",
"start": 1357
},
{
"end": 1455,
"label": "vehicleType",
"start": 1453
},
{
"end": 1564,
"label": "vehicleType",
"start": 1562
},
{
"end": 862,
"label": "vehicleType",
"start": 854
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" could be other vehicles besides cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 28,
"label": "vehicleType",
"start": 25
},
{
"end": 172,
"label": "vehicleType",
"start": 170
},
{
"end": 283,
"label": "vehicleType",
"start": 281
},
{
"end": 1359,
"label": "vehicleType",
"start": 1357
},
{
"end": 1455,
"label": "vehicleType",
"start": 1453
},
{
"end": 1564,
"label": "vehicleType",
"start": 1562
},
{
"end": 862,
"label": "vehicleType",
"start": 854
}
] | null | null |
22f3f408-6fdf-4b12-801a-a1e2468f07d8
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T07:44:46.111000 |
83dbf05c-5cb4-409b-8ec2-7bf5e10983ce
|
Building a sustainable and eco-friendly transport system is crucial to tackling global challenges such as climate change, as transport can be seen as one of the main sources of air pollution and CO2 emissions in megacities, particularly in developing countries. By bringing together multiple modes of travel, and combining different transport provider options into a single service, Mobility-as-a-Service (MaaS) could offer an effective way to help build a sustainable city by improving public transport services. However, the strategies used to develop MaaS, vary in different cities based on their specific multimodal transport facilities and service. Many residents of Beijing have to contend with long-distance commuting, which may adversely affect individuals’ travel experience and satisfaction and is, therefore, a key issue for transport development in Beijing. Using Beijing as a case study, we carried out in-depth interviews and thereby captured long-distance commuters’ concerns and needs concerning their commuting experiences. Our findings show that long-distance commuters are primarily concerned about the following multimodal commuting scenarios: “Underground + bicycle”, “Underground + taxi”, “Underground + private car”, and “Underground + bus”. Therefore, we suggest that the priority should be to develop a MaaS model for Beijing that focuses on the integration of multimodal transport connected to the underground rail system.
|
<li> <b>bicycle:</b> Cyclist<li> <b>taxi:</b> Car<li> <b>private car:</b> Car<li> <b>bus:</b> Bus
|
[
[
{
"end": 1186,
"label": "VRUType",
"start": 1179
},
{
"end": 1208,
"label": "vehicleType",
"start": 1204
},
{
"end": 1237,
"label": "vehicleType",
"start": 1226
},
{
"end": 1262,
"label": "vehicleType",
"start": 1259
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 1186,
"label": "VRUType",
"start": 1179
},
{
"end": 1208,
"label": "vehicleType",
"start": 1204
},
{
"end": 1237,
"label": "vehicleType",
"start": 1226
},
{
"end": 1262,
"label": "vehicleType",
"start": 1259
}
] | null | null |
1888ed57-f7b2-4acf-83d7-40d4b43560b2
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T05:54:57.773000 |
dbc13623-f6a0-4c5f-9819-82633124663c
|
However, those commuters who live outside the fifth ring but without cars, always rely on car-hailing or taxis for work in Beijing.Yet, during peaking hours, it is very hard to get a taxi.Under such concerns, MaaS can dispatch the taxis in advance through the realization of integrated planning.For example, after being informed of the departure and arrival time of the subway as well as the flow of commuters, MaaS can instruct taxi drivers to prepare ahead of time at the station.It is a win-win strategy for both travellers and the environment.As for the commuters can receive better and smoother service in less time.As for the environment, by reducing the time for taxis to drive empty while waiting for the orders, the implementation of MaaS can thus decrease Carbon emissions, which is rather eco-friendly.
|
<li> <b>cars:</b> Car<li> <b>taxis:</b> Car<li> <b>taxi:</b> Car
|
[
[
{
"end": 73,
"label": "vehicleType",
"start": 69
},
{
"end": 110,
"label": "vehicleType",
"start": 105
},
{
"end": 236,
"label": "vehicleType",
"start": 231
},
{
"end": 675,
"label": "vehicleType",
"start": 670
},
{
"end": 187,
"label": "vehicleType",
"start": 183
},
{
"end": 433,
"label": "vehicleType",
"start": 429
},
{
"end": 93,
"label": "vehicleType",
"start": 90
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Car-hailing: \"car\" was not identified as entity\nTaxi: could be also a \"van\"/\"mini van\", but we agree that \"car\" is the prevalent equivalent\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 73,
"label": "vehicleType",
"start": 69
},
{
"end": 110,
"label": "vehicleType",
"start": 105
},
{
"end": 236,
"label": "vehicleType",
"start": 231
},
{
"end": 675,
"label": "vehicleType",
"start": 670
},
{
"end": 187,
"label": "vehicleType",
"start": 183
},
{
"end": 433,
"label": "vehicleType",
"start": 429
}
] | null | null |
e4039302-9b44-4a4d-8ce8-ebea2f1a5bf8
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-20T11:01:16.807000 |
883bf43c-4639-49f4-8132-6d970b1a46c7
|
Purpose. The purpose of work is the possibility estimation of аthermic technologies use of cold-deformed metal softening for elements of railway car body and wheel. Methodology. The material for research is the carbon steel of the wheel rim fragment containing 0.55%С, 0.74%Mn, 0.33%Si, and the steel 20. The wheel steel is studied after heat strengthening and cold work after operation. Steel 20 is studied after plastic cold work by rolling. Electric pulse treatment (ET) is carried out on the special equipment. As the property of metal strength the Vickers hardness number is used. The microstructure research is carried out using the light and electronic microscope. Findings. During operation of the rolling stock elements with different strength level origin of damages on metallic surfaces is caused by a simultaneous load action. Taking into account that forming of breakdown sites is largely determined by the state of metal volumes nearby the places of maximal active voltages, the technology development of defect accumulation slowdown or the level of active voltages development allow one to prolong the operating term of rolling stock elements. After electric pulse treatment of the wheel rim fragment the regular changes of metal internal structure corresponded to the hardness changes. The hardness of low carbon steel increases proportional to the increase of the level of cold work by rolling. Alternating bending of the cold-deformed flat is accompanied by strength decrease, which is caused by the metal substructure changes. Originality. The softening process of the cold-worked steel is accompanied by substructure changes, which to a greater extent correspond to the hardening development from the plastic cold-work: dispersion of the dislocation cellular structure, formation of the new sub boundaries and displacement of the formed sub boundaries. Practical value. Introduction of electric pulse treatment in the conditions of railway depots repair base allow one to attain the required level of softening of the cold-worked steel on the wheel thread of railway wheel without heating of metal. The given treatment reduces the metal hardness and prolongs the term of incisors use during the renovation of the rolling profile of the railway wheel
|
<li> <b>railway car:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"railway car\" is not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 148,
"label": "vehicleType",
"start": 137
}
] | null | null |
25fd8aae-0208-421e-ac23-f11d628e9a22
|
pending
| 2025-04-09T16:14:38.079000 | 2025-05-13T09:37:44.638000 |
ca63fd7b-d2a0-40dc-8f45-0e2a217e6bbf
|
В роботі представлено узагальнені результати, що стосуються розвитку процесів пом'якшення в холоднодеформованій вуглецевій сталі під час подальшого її знакозмінного навантаження. Процес пом'якшення наклепаної сталі супроводжується субструктурними змінами, які більшою мірою притаманні зміцненню від холодної пластичної деформації: диспергування дислокаційної чарункової структури, формування нових та переміщення сформованих субмеж. Впровадження в умовах ремонтної бази залізничних депо технології електричної імпульсної обробки дозволить без застосування нагріву металу досягти необхідного рівня пом'якшення сталі, наклепаної по поверхні кочення
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
"Do not acknowledge the language. --> DISCARDED"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[] | null | null |
693509bc-b375-4667-86c2-a60dc013326a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-04-11T13:44:44.291000 |
3d5df478-fc70-4135-941f-8d32bda1e990
|
Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.
|
<li> <b>Autonomous vehicles (AVs):</b> Other Vehicle<li> <b>AVs:</b> Other Vehicle<li> <b>bus driver:</b> Bus<li> <b>camera sensors:</b> Camera<li> <b>bus:</b> Bus
|
[
[
{
"end": 25,
"label": "vehicleType",
"start": 0
},
{
"end": 130,
"label": "vehicleType",
"start": 127
},
{
"end": 966,
"label": "sensorType",
"start": 952
},
{
"end": 658,
"label": "vehicleType",
"start": 655
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
},
{
"end": 595,
"label": "vehicleType",
"start": 576
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"shuttle\" is another common wording for the autonomous buses/minibuses used in CCAM. So \"shuttle\" should be added in the vehicle type\n- Contextually \"use cases\" in the last sentence could be regarded as a Scenario, so the scenarioType in this case could be \"autonomous shuttle\" and could be added to the \"F31\" cell of tab \"Definition v1.1\" of our Data Model\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 25,
"label": "vehicleType",
"start": 0
},
{
"end": 24,
"label": "vehicleType",
"start": 21
},
{
"end": 130,
"label": "vehicleType",
"start": 127
},
{
"end": 665,
"label": "vehicleType",
"start": 655
},
{
"end": 966,
"label": "sensorType",
"start": 952
},
{
"end": 658,
"label": "vehicleType",
"start": 655
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
}
] | null | null |
1f249f9c-1431-44e2-bca7-adae83a08109
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:44:05.838000 |
1c2a8ce2-e9f8-4b93-b6e0-a78b36cc423e
|
Using a threshold on the reconstruction error, we are able to determine whether a video frame is normal or abnormal (Figure 12).A predefined threshold is not a robust method since our solution should operate in real-time and support multiple camera sensors as we mentioned earlier in the design principles.A fixed threshold value can alter the sensitivity of the event detection, rendering it inappropriate in some scenarios.In addition, a wrong threshold can prevent the detection of certain abnormal events or produce false positives.In order to solve this issue, we introduce a variable thresholding technique in order to find the optimal value in real-time.The initialization procedure now includes a "warm-up" session, in which we aggregate the individual regularity score of each frame.During that session, no detections are performed, as we consider the events as regular.As the buffer continues to fill, we are able to calculate the average reconstruction error and provide a threshold value tailored to the specific conditions.Figure 13 indicates an abnormal scenario with the aforementioned metrics.
|
<li> <b>camera sensors:</b> Camera
|
[
[
{
"end": 256,
"label": "sensorType",
"start": 242
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 256,
"label": "sensorType",
"start": 242
}
] | null | null |
4270bbfc-5463-4f08-ad69-fd34106e763a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:11:44.140000 |
2744013e-a0bb-4be4-b7ac-99d726011b35
|
This paper presents a comparative analysis of the effects of short-range and long-range electric vehicles charging on transformer life. Long-range vehicles are expected to become more common in the future. They have higher battery capacity and charge at higher power levels, modifying demand profile. A probabilistic analysis is performed using the Monte Carlo Simulation, evaluating the transformer hottest-spot temperature and the aging acceleration factor. Residential demand is modeled based on real electricity measurements, and EVs’ demand is modeled based on real data collected from a trial project developed in the United Kingdom. Simulations are conducted considering the influence of ambient temperature analyzing summer and winter seasons and several EV penetration levels. Results show the impacts caused by long-range vehicles are more severe because they charge at higher power levels, especially during winter, when residential demand is higher. For penetration level of 50% during summer, the use of long-range EVs brings a minimum equivalent aging factor of 5.2, which means the transformer aged 124.8 h in a cycle of only 24 h, decreasing its lifetime.
|
<li> <b>electric vehicles:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>EVs:</b> Car
|
[
[
{
"end": 105,
"label": "vehicleType",
"start": 88
},
{
"end": 105,
"label": "vehicleType",
"start": 97
},
{
"end": 155,
"label": "vehicleType",
"start": 147
},
{
"end": 840,
"label": "vehicleType",
"start": 832
},
{
"end": 537,
"label": "vehicleType",
"start": 534
},
{
"end": 1031,
"label": "vehicleType",
"start": 1028
},
{
"end": 765,
"label": "vehicleType",
"start": 763
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"EVs\" could be other vehicles than cars\n- \"EV\" is not recognized as \"vehicleType\" in its singular format\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 105,
"label": "vehicleType",
"start": 88
},
{
"end": 105,
"label": "vehicleType",
"start": 97
},
{
"end": 155,
"label": "vehicleType",
"start": 147
},
{
"end": 840,
"label": "vehicleType",
"start": 832
},
{
"end": 537,
"label": "vehicleType",
"start": 534
},
{
"end": 1031,
"label": "vehicleType",
"start": 1028
}
] | null | null |
b67eec04-66d6-4a47-9d53-9760970f5c5d
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:42:45.905000 |
83145c55-6a3a-492b-8ba7-cbf6a7b40f5d
|
This paper analyzed the impacts of long-range EVs on transformer life and compared results with the effects caused by short-range EVs.A probabilistic analysis is employed using the Monte Carlo Simulation.Residential demand is modeled based on real household curves from the UK Data Service, and EVs' pattern is modeled based on data collected from the Electric Nation Project developed United Kingdom.Several EVs' penetration levels are analyzed from 0% to 60% in steps of 10% for winter and summer seasons.From the results, important conclusions can be addressed:
|
<li> <b>EVs:</b> Car
|
[
[
{
"end": 49,
"label": "vehicleType",
"start": 46
},
{
"end": 133,
"label": "vehicleType",
"start": 130
},
{
"end": 298,
"label": "vehicleType",
"start": 295
},
{
"end": 412,
"label": "vehicleType",
"start": 409
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EV\" is not a car exclusively (could be 2-wheeler, 3-wheeler, minivan, etc.)"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 49,
"label": "vehicleType",
"start": 46
},
{
"end": 133,
"label": "vehicleType",
"start": 130
},
{
"end": 298,
"label": "vehicleType",
"start": 295
},
{
"end": 412,
"label": "vehicleType",
"start": 409
}
] | null | null |
850c1eea-542f-4f13-b39e-b927ccd1657a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T09:43:45.144000 |
8b309dad-7739-42be-83f8-c5bfbe23ab28
|
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed.
|
<li> <b>Intersection scenarios:</b> Other Scenario<li> <b>vehicle:</b> Other Vehicle<li> <b>intersection scenarios:</b> Other Scenario<li> <b>intelligent vehicles:</b> Other Vehicle<li> <b>intersection scenario:</b> Other Scenario
|
[
[
{
"end": 22,
"label": "scenarioType",
"start": 0
},
{
"end": 131,
"label": "vehicleType",
"start": 124
},
{
"end": 488,
"label": "vehicleType",
"start": 481
},
{
"end": 185,
"label": "scenarioType",
"start": 163
},
{
"end": 558,
"label": "scenarioType",
"start": 536
},
{
"end": 1149,
"label": "vehicleType",
"start": 1129
},
{
"end": 1255,
"label": "vehicleType",
"start": 1235
},
{
"end": 764,
"label": "scenarioType",
"start": 743
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 22,
"label": "scenarioType",
"start": 0
},
{
"end": 131,
"label": "vehicleType",
"start": 124
},
{
"end": 488,
"label": "vehicleType",
"start": 481
},
{
"end": 185,
"label": "scenarioType",
"start": 163
},
{
"end": 558,
"label": "scenarioType",
"start": 536
},
{
"end": 1149,
"label": "vehicleType",
"start": 1129
},
{
"end": 1255,
"label": "vehicleType",
"start": 1235
},
{
"end": 764,
"label": "scenarioType",
"start": 743
}
] | null | null |
92ab985f-29aa-489a-8679-a19612a1b5c5
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T07:05:54.724000 |
66a77e99-7edf-4dda-aae2-e3f924d3e8bc
|
For the intersection scenario, this study uses time to enter (TTE) to evaluate the driving condition of intelligent vehicles through the intersection scenario.The TTE indicates the time that the vehicle continues to travel in its current state of motion from its current position until it enters the collision zone.The calculation process is as follows. In the above equation, t TTE j denotes the TTE value of the intelligent vehicle relative to the potential collision region c j ; a AV denotes the acceleration of the intelligent vehicle; d j denotes the distance of the intelligent vehicle from its current position along the road curvature to the safety line corresponding to the potential collision region c; and v AV denotes the travel speed of the intelligent vehicle.However, the existence of an intersection region between the future path of the vehicle in question and the future path of the intelligent driving vehicle does not necessarily mean that a collision will occur between them.Therefore, assessing the driving risk in the current traffic environment requires using the kinematic information of the intelligent vehicle and the related vehicle to determine the possibility of collision in the corresponding potential collision region.In this study, we use the temporal overlap of intelligent and related vehicles through the potential collision region to determine whether they will collide in the corresponding potential collision region.The mathematical expression of this temporal overlap degree is as follows. In the above formula, t s j indicates the time when the vehicle drives into the safety line corresponding to the potential collision area c j ; t e j indicates the time when the vehicle drives out of the end line corresponding to the potential collision area c j ; d s j is the distance when the vehicle drives from the current position along the road curvature to the safety line corresponding to the potential collision area c j ; d e j is the distance when the vehicle drives from the current position along the road curvature to the end line corresponding to the potential collision area c j ; v is the current driving speed of the vehicle; a is the acceleration of the vehicle; and l is the body length of the vehicle. By substituting the information of the map, the intelligent vehicle, and the related vehicles into Equation ( 6), we can obtain the time intervals [t s AV j , t e AV j ] and [t s SV j , t e SV j ] that the intelligent vehicle and the related vehicles pass in the potential collision region c j .By analyzing the overlap of these two-time intervals, we can determine whether a collision occurs between the intelligent vehicle and the vehicle of interest in the potential collision region c j .According to the different temporal arrangements of t s AV j , t e AV j , t s SV j and t e SV j , we can classify them into six cases, as shown in Figure 5. Except for the cases of 5 and 6 , there is an overlap of time intervals between the intelligent vehicle and the related vehicle in passing the potential collision area c j , and the intelligent vehicle needs to make reasonable driving decisions to avoid the collision.In the cases of 5 and 6 , there is no time interval overlap between the intelligent vehicle and the related vehicle in passing the potential collision area c j , and the intelligent vehicle can pass safely. World Electr.Veh.J. 2023, 14, x FOR PEER REVIEW 10 of 28
|
<li> <b>intersection scenario:</b> Other Scenario<li> <b>intelligent vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>intelligent driving vehicle:</b> Other Vehicle
|
[
[
{
"end": 29,
"label": "scenarioType",
"start": 8
},
{
"end": 158,
"label": "scenarioType",
"start": 137
},
{
"end": 124,
"label": "vehicleType",
"start": 104
},
{
"end": 202,
"label": "vehicleType",
"start": 195
},
{
"end": 433,
"label": "vehicleType",
"start": 426
},
{
"end": 539,
"label": "vehicleType",
"start": 532
},
{
"end": 592,
"label": "vehicleType",
"start": 585
},
{
"end": 774,
"label": "vehicleType",
"start": 767
},
{
"end": 862,
"label": "vehicleType",
"start": 855
},
{
"end": 1137,
"label": "vehicleType",
"start": 1130
},
{
"end": 1161,
"label": "vehicleType",
"start": 1154
},
{
"end": 1595,
"label": "vehicleType",
"start": 1588
},
{
"end": 1717,
"label": "vehicleType",
"start": 1710
},
{
"end": 1835,
"label": "vehicleType",
"start": 1828
},
{
"end": 2003,
"label": "vehicleType",
"start": 1996
},
{
"end": 2175,
"label": "vehicleType",
"start": 2168
},
{
"end": 2213,
"label": "vehicleType",
"start": 2206
},
{
"end": 2254,
"label": "vehicleType",
"start": 2247
},
{
"end": 2323,
"label": "vehicleType",
"start": 2316
},
{
"end": 2481,
"label": "vehicleType",
"start": 2474
},
{
"end": 2680,
"label": "vehicleType",
"start": 2673
},
{
"end": 2696,
"label": "vehicleType",
"start": 2689
},
{
"end": 3008,
"label": "vehicleType",
"start": 3001
},
{
"end": 3032,
"label": "vehicleType",
"start": 3025
},
{
"end": 3106,
"label": "vehicleType",
"start": 3099
},
{
"end": 3264,
"label": "vehicleType",
"start": 3257
},
{
"end": 3288,
"label": "vehicleType",
"start": 3281
},
{
"end": 3362,
"label": "vehicleType",
"start": 3355
},
{
"end": 929,
"label": "vehicleType",
"start": 902
},
{
"end": 1330,
"label": "vehicleType",
"start": 1322
},
{
"end": 2349,
"label": "vehicleType",
"start": 2341
},
{
"end": 2506,
"label": "vehicleType",
"start": 2498
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"vehicles\" was not identified as \"other vehicle\" --> included as vehicleType"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 29,
"label": "scenarioType",
"start": 8
},
{
"end": 158,
"label": "scenarioType",
"start": 137
},
{
"end": 124,
"label": "vehicleType",
"start": 104
},
{
"end": 202,
"label": "vehicleType",
"start": 195
},
{
"end": 433,
"label": "vehicleType",
"start": 426
},
{
"end": 539,
"label": "vehicleType",
"start": 532
},
{
"end": 592,
"label": "vehicleType",
"start": 585
},
{
"end": 774,
"label": "vehicleType",
"start": 767
},
{
"end": 862,
"label": "vehicleType",
"start": 855
},
{
"end": 929,
"label": "vehicleType",
"start": 922
},
{
"end": 1137,
"label": "vehicleType",
"start": 1130
},
{
"end": 1161,
"label": "vehicleType",
"start": 1154
},
{
"end": 1595,
"label": "vehicleType",
"start": 1588
},
{
"end": 1717,
"label": "vehicleType",
"start": 1710
},
{
"end": 1835,
"label": "vehicleType",
"start": 1828
},
{
"end": 2003,
"label": "vehicleType",
"start": 1996
},
{
"end": 2175,
"label": "vehicleType",
"start": 2168
},
{
"end": 2213,
"label": "vehicleType",
"start": 2206
},
{
"end": 2254,
"label": "vehicleType",
"start": 2247
},
{
"end": 2323,
"label": "vehicleType",
"start": 2316
},
{
"end": 2481,
"label": "vehicleType",
"start": 2474
},
{
"end": 2680,
"label": "vehicleType",
"start": 2673
},
{
"end": 2696,
"label": "vehicleType",
"start": 2689
},
{
"end": 3008,
"label": "vehicleType",
"start": 3001
},
{
"end": 3032,
"label": "vehicleType",
"start": 3025
},
{
"end": 3106,
"label": "vehicleType",
"start": 3099
},
{
"end": 3264,
"label": "vehicleType",
"start": 3257
},
{
"end": 3288,
"label": "vehicleType",
"start": 3281
},
{
"end": 3362,
"label": "vehicleType",
"start": 3355
},
{
"end": 929,
"label": "vehicleType",
"start": 902
}
] | null | null |
93d34645-3b78-4833-80f9-7d58008ff05c
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T14:27:41.188000 |
272715f9-b2fe-44d0-801d-0c62eb82367d
|
AbstractThe rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, early-stage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.
|
<li> <b>rail vehicle:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 82,
"label": "vehicleType",
"start": 74
},
{
"end": 24,
"label": "vehicleType",
"start": 17
},
{
"end": 138,
"label": "vehicleType",
"start": 131
},
{
"end": 461,
"label": "vehicleType",
"start": 454
},
{
"end": 1033,
"label": "vehicleType",
"start": 1026
},
{
"end": 1381,
"label": "vehicleType",
"start": 1374
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"rail vehicle\" is not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 24,
"label": "vehicleType",
"start": 12
},
{
"end": 461,
"label": "vehicleType",
"start": 449
},
{
"end": 1033,
"label": "vehicleType",
"start": 1021
},
{
"end": 1381,
"label": "vehicleType",
"start": 1369
},
{
"end": 82,
"label": "vehicleType",
"start": 74
},
{
"end": 24,
"label": "vehicleType",
"start": 17
},
{
"end": 138,
"label": "vehicleType",
"start": 131
},
{
"end": 461,
"label": "vehicleType",
"start": 454
},
{
"end": 1033,
"label": "vehicleType",
"start": 1026
},
{
"end": 1381,
"label": "vehicleType",
"start": 1374
}
] | null | null |
fbbc2ec3-59d5-496b-a877-3ca63ac80f37
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:47:14.624000 |
24746c9f-e4f3-44c8-97e7-16fd149201e9
|
Real running data from a door system on a certain line in Nanjing (a city of China) were analyzed and used to verify the typical subhealth rules proposed above.Firstly, some off-peak normal data were selected and trained to construct the statistical envelope feature thresholds.Then, 3000 sets of real data were selected to verify the rules.The real-time M 11 and M 21 represent the number of points over the 3r upper limit and below the 3r lower limit, respectively, in the rising speed section.Similarly, N 11 and N 21 represent the number of points over the 6r upper limit and below the 6r lower limit, respectively, in the rising speed section.M 14 represents the number of points over the 3r upper limit in the whole process.For the meanings of the other parameters, see the body text above. (a) Opening door process (b) Closing door process The diagnostic results contain typical subhealth conditions, such as a local resistance increase in the rising speed section of the opening door, a serious resistance increase in the whole process of the opening door, a local resistance increase in the slow speed section of the closing door, and a local resistance increase in the uniform section of the closing door. According to the maintenance history of door systems, the problem of local resistance increasing basically occurs during the peak commuting hours.At that time, passengers squeeze between the doors, but the of the door system is not affected for a short period of time.Meanwhile, a serious increase in the resistance of the whole door is caused by poor lubrication of the long guide column, which returns to normal after relubrication following internal inspection. Part of the typical subhealth data diagnosed is compared with the normal data according to the established rules in Fig. 12.In Fig. 12a, compared with normal data, the opening door current signals diagnosed with a resistance anomaly in the whole process and in the uniform speed section of the opening door increase at corresponding positions.Also, in Fig. 12b, for the resistance anomaly for slow speed and uniform speed sections of the closing door, the current signals are higher than the normal data signals at the corresponding points.Therefore, the results are consistent with actual working conditions.The statistics of the diagnosis results using the subhealth rules are presented in Table 12.Note that there are 2995 door openings and closings in total, for which the subhealth state is completely diagnosed with a misdiagnosis rate of 0%. From the data judged to be normal, 200 randomly sampled sets are used to test the subhealth diagnostic rules.Table 13 presents the missed diagnosis statistics, showing that four groups of subhealth data are not diagnosed, corresponding to a missed diagnosis rate of 2%. Overall, the diagnosis results based on the typical subhealth rules indicate that they are successful.In consideration of the low rate of missed diagnoses, the rules can not only filter out typical faults and abnormal data but also cover most of the whole and local resistance subhealth states.The average diagnostic time for a single datum was only 0.1945 s, thus fully meeting the needs for online realtime diagnosis by the server. In addition, the signals with missed diagnoses mainly correspond to data for local resistance increases in the slow section of the closing door.Due to the locked current, the peak distribution in the termination section of the closed door is unstable, which may result in a small probability of missed diagnosis or misdiagnosis.Further work will continue to optimize the rules and reduce the missed diagnosis rate.For the diagnosed subhealth data, the real-time subhealth levels could be divided according to their severity, In the future, the rules will be continuously improved, and new subhealth types will be added to improve the diagnosis efficiency and accuracy of the subhealth rules and achieve early warning of failures.The reliability and safety of urban rail vehicle door systems will thereby be effectively improved. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.
|
<li> <b>rail vehicle:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"... urban rail vehicle door systems ...\" does not seem to refer to vehicle"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 4012,
"label": "vehicleType",
"start": 4000
}
] | null | null |
39e2a35d-dbeb-447a-af2e-a1c8464baee6
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T07:55:21.248000 |
b74c7a90-4e66-4a33-9cce-f07703df4a55
|
In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>commercial vehicle:</b> Truck<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
"end": 344,
"label": "vehicleType",
"start": 326
},
{
"end": 1365,
"label": "vehicleType",
"start": 1357
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 344,
"label": "vehicleType",
"start": 337
},
{
"end": 344,
"label": "vehicleType",
"start": 326
},
{
"end": 1365,
"label": "vehicleType",
"start": 1357
}
] | null | null |
f67a5e6a-7728-4f30-8d9f-e44c746b833a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T11:39:58.352000 |
ea26d155-dcf3-465b-a7ba-a5ee9452f766
|
Like the existing references [23][24][25][26], the single-pedal regenerative braking strategy proposed in this paper is verified in the simulation, and the braking energy recovery rate and effective braking energy recovery rate are increased by 20.3% and 3.5%, respectively.However, the simulation cannot fully simulate the actual road environment factors.The real-vehicle verification can take into account the system integration, provide more accurate and real data, and evaluate the performance, safety and reliability of the strategy.Therefore, this paper carried out a constant-speed car-following experiment.The results show that the maximum optimization rate of energy consumption of the single-pedal regenerative braking control strategy proposed in this paper reaches 5.81%, and the average optimization rate reaches 4.33%, which can effectively reduce energy consumption and improve the economy of single-pedal pure electric commercial vehicles. The fuzzy control method used in this paper is based on experience and has high requirements for researchers.Therefore, future research can explore the combination of neural networks and swarm intelligence algorithms to further improve the performance of the single-pedal regenerative braking control strategy.By combining the learning ability of neural networks with the optimization ability of swarm intelligence algorithms, a more intelligent and adaptive regenerative braking control strategy can be realized.This will bring greater improvements to the energy recovery and driving experience of the vehicle's braking system.
|
<li> <b>commercial vehicles:</b> Truck<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 954,
"label": "vehicleType",
"start": 935
},
{
"end": 372,
"label": "vehicleType",
"start": 365
},
{
"end": 1566,
"label": "vehicleType",
"start": 1559
},
{
"end": 602,
"label": "scenarioType",
"start": 589
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"In \"...car-following...\" the term \"car\" was not captured"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 954,
"label": "vehicleType",
"start": 935
},
{
"end": 372,
"label": "vehicleType",
"start": 365
},
{
"end": 1566,
"label": "vehicleType",
"start": 1559
}
] | null | null |
f20cef8c-c04c-4a7e-8cb9-100192bc1066
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T16:03:04.154000 |
f0e128dc-b57d-46be-9543-1d8c5893fe50
|
This study analyses the technical and economic aspects of a novel subsea freight glider (SFG). The SFG is an excellent replacement for tanker ships and submarine pipelines transporting liquefied CO2. The main aim of the SFG is to ship CO2 from an offshore facility to an underwater well where the gas can be injected; as an advantage, the SFG vehicle may be used to transport all kinds of cargo. The SFG travels below the sea surface, making the vessel weather-independent. The research is divided into two steps. Firstly, the technical feasibility analysis is performed by designing a baseline design with a length of 56.5 m, a beam of 5.5 m, and a cargo volume of 1194 m3. The SFG is developed using DNVGL-RU-NAVAL-Pt4Ch1, which was initially created for military submarine designs. Two additional half-scaled 469 m3 and double-scaled 2430 m3 models are created when the baseline design fulfils the technical requirements. Secondly, the economic analysis is carried out using the freely accessible MUNIN D9.3 and ZEP reports. The economic feasibility analysis is illustrated through a case study with a CO2 transport capacity range of 0.5 to 2.5 mtpa (million tons per annum) and a transport length range of 180 km to 1500 km. The prices of CO2 per ton for the SFG, crew and autonomous tankers, and offshore pipelines are comprehensively compared. According to the results, SFGs with capacities of 469 m3, 1194 m3, and 2430 m3 are technically possible to manufacture. Moreover, the SFGs are competitive with a smaller CO2 capacity of 0.5 mtpa at distances of 180 and 500 km and a capacity of 1 mtpa at a distance of 180 km.
|
<li> <b>subsea freight glider (SFG):</b> Other Vehicle<li> <b>SFG:</b> Other Vehicle<li> <b>tanker ships:</b> Other Vehicle<li> <b>submarine:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>vessel:</b> Other Vehicle<li> <b>tankers:</b> Other Vehicle
|
[
[
{
"end": 350,
"label": "vehicleType",
"start": 343
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Marine-related vehicles are not of interest to CCAM pilot, thus classification and annotation is conbducted accordingly"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 93,
"label": "vehicleType",
"start": 66
},
{
"end": 92,
"label": "vehicleType",
"start": 89
},
{
"end": 102,
"label": "vehicleType",
"start": 99
},
{
"end": 223,
"label": "vehicleType",
"start": 220
},
{
"end": 342,
"label": "vehicleType",
"start": 339
},
{
"end": 403,
"label": "vehicleType",
"start": 400
},
{
"end": 682,
"label": "vehicleType",
"start": 679
},
{
"end": 1266,
"label": "vehicleType",
"start": 1263
},
{
"end": 147,
"label": "vehicleType",
"start": 135
},
{
"end": 161,
"label": "vehicleType",
"start": 152
},
{
"end": 775,
"label": "vehicleType",
"start": 766
},
{
"end": 350,
"label": "vehicleType",
"start": 343
},
{
"end": 452,
"label": "vehicleType",
"start": 446
},
{
"end": 1295,
"label": "vehicleType",
"start": 1288
}
] | null | null |
bfb74706-02c2-42a1-b176-54626f324668
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T06:54:09.904000 |
86ca55a3-cd1c-40f3-b02a-845d1403939c
|
The minimum number of vessels required to perform the mission is illustrated in Figure 7.
|
<li> <b>vessels:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"A vessel is not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 29,
"label": "vehicleType",
"start": 22
}
] | null | null |
09c13aa1-b0cd-445a-a66e-c4815f1cd3fd
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-20T14:42:25.618000 |
8a019c2e-cb41-4c43-987f-5150703d6468
|
Existing research concerning the extention of the service life of various types of railway vehicles and assessing its remaining lifetime has been reviewed and analyzed. It has been established that the vast majority of studies relate to the assessment of the residual lifetime of various types of railway rolling stock based on the results of technical diagnostics and routine tests, as well as the assessment of corrosion wear of supporting elements and vehicles bodies. At the same time, little attention has been paid to the issue of improving existing programs and procedures of complex technical diagnostics. It was determined that the set of diagnostics operations for the extension of the service life includes routine tests of a test sample and examination of the technical condition of the metal structure of each railway vehicle for mechanical and corrosive damage.It is proposed to make changes to the existing current programs and procedures in such key sections as: terminology, objects of technical diagnostics and tests, selection of a test sample for routine tests, execution order and methods of technical diagnostics and routine tests, data processing and evaluation of results. A comprehensive approach for assessing the residual lifetime of railway vehicles is also proposed.
|
<li> <b>railway vehicles:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 463,
"label": "vehicleType",
"start": 455
},
{
"end": 1277,
"label": "vehicleType",
"start": 1269
},
{
"end": 838,
"label": "vehicleType",
"start": 831
},
{
"end": 99,
"label": "vehicleType",
"start": 91
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"railway vehicle\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 99,
"label": "vehicleType",
"start": 83
},
{
"end": 1277,
"label": "vehicleType",
"start": 1261
},
{
"end": 99,
"label": "vehicleType",
"start": 91
},
{
"end": 463,
"label": "vehicleType",
"start": 455
},
{
"end": 1277,
"label": "vehicleType",
"start": 1269
},
{
"end": 838,
"label": "vehicleType",
"start": 831
}
] | null | null |
a5884aad-fe75-4c54-82f4-18c52547ca08
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-20T14:36:36.559000 |
f1c12cdd-071d-4ce9-8b85-fdb73b1e0e72
|
The purpose of the impact endurance tests is to experimentally check the durability of the metal structure of a sample of a specific type vehicle body with actual (after the intended period of operation) thicknesses of elements under specified shock load modes, which are equivalent in terms of destructive effect to the loading of the car by operational longitudinal dynamic forces.When carrying out impact endurance tests the following indicators are determined: impact force on the coupling; number of impacts; the presence of permanent deformations, damage from impact loads in the inspected elements of the body.At the same time, it is allowed to assess the durability of the load-bearing elements of the body structure of the passenger car and the locomotive by the fatigue resistance factor, which is calculated based on the results of dynamic and strength tests in accordance with the requirements of the "Standard for the calculation and design of new and modernized railway cars of MPS gauge 1520 mm (non-self-propelled) » [30] and RD 24.050.37 [31].During dynamic strength tests, dynamic stresses in the main load-bearing elements of the body structure which occur during the movement of wagons at different speeds, up to the design one, on specific sections of the railway track of the corresponding design and current condition are determined and evaluated.During dynamic strength tests, in accordance with the requirements [31], such indicators as the natural frequency of the main vertical bending oscillations of the body and the fatigue resistance factor of the main bearing elements of the body are determined. Impact endurance tests are carried out in the following sequence: -a vehicle is equipped with a special auto-coupling dynamometer, static load pre-graded up to 400 tons; -loading to the nominal carrying capacity (for freight cars); loading to the maximum passenger capacity and provision with water and coal (for passenger cars); complete servicing (for locomotives); -weighing of loaded/equipped vehicles; -location of railway vehicles on the test site with a shunting hump test stand with a buffer stop, equipped with a self-coupling device, and preparation of a striker, the weight of which must be no less than the weight of the test car.At the same time, it is allowed to use a railway locomotive instead of a shunting hump test stand, and instead of a railway stop -a formation of several wagons, the total weight of which is at least 300 tons; -direct impact test; -measurement of stresses, rolling speed of the tank car with the aid of measuring equipment; -inspection of the metal structure of the rolling stock is performed after every 3-5 impacts. The process of carrying out shock endurance tests using a locomotive and an experimental coupling as a railway stop is shown in Fig. 3.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>passenger car:</b> Car<li> <b>locomotive:</b> Other Vehicle<li> <b>wagons:</b> Other Vehicle<li> <b>railway cars:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>tank car:</b> Other Vehicle<li> <b>rolling stock:</b> Other Vehicle
|
[
[
{
"end": 145,
"label": "vehicleType",
"start": 138
},
{
"end": 1705,
"label": "vehicleType",
"start": 1698
},
{
"end": 339,
"label": "vehicleType",
"start": 336
},
{
"end": 2270,
"label": "vehicleType",
"start": 2267
},
{
"end": 2556,
"label": "vehicleType",
"start": 2553
},
{
"end": 745,
"label": "vehicleType",
"start": 732
},
{
"end": 2034,
"label": "vehicleType",
"start": 2026
},
{
"end": 2065,
"label": "vehicleType",
"start": 2057
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"locomotive\", \"railway car\", \"wagon\", \"tank car\", \"rolling stock\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 145,
"label": "vehicleType",
"start": 138
},
{
"end": 1705,
"label": "vehicleType",
"start": 1698
},
{
"end": 339,
"label": "vehicleType",
"start": 336
},
{
"end": 745,
"label": "vehicleType",
"start": 742
},
{
"end": 2270,
"label": "vehicleType",
"start": 2267
},
{
"end": 2556,
"label": "vehicleType",
"start": 2553
},
{
"end": 745,
"label": "vehicleType",
"start": 732
},
{
"end": 764,
"label": "vehicleType",
"start": 754
},
{
"end": 2330,
"label": "vehicleType",
"start": 2320
},
{
"end": 2756,
"label": "vehicleType",
"start": 2746
},
{
"end": 1205,
"label": "vehicleType",
"start": 1199
},
{
"end": 2430,
"label": "vehicleType",
"start": 2424
},
{
"end": 988,
"label": "vehicleType",
"start": 976
},
{
"end": 2034,
"label": "vehicleType",
"start": 2026
},
{
"end": 2065,
"label": "vehicleType",
"start": 2057
},
{
"end": 2556,
"label": "vehicleType",
"start": 2548
},
{
"end": 2649,
"label": "vehicleType",
"start": 2636
}
] | null | null |
2cdb795e-6a4e-4acc-8eda-c68800914313
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:37:33.729000 |
37c0fa96-4638-48b6-b259-079d1219ed3d
|
Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases.
|
<li> <b>Vehicle-to-Everything (V2X):</b> V2X<li> <b>connected and automated driving:</b> Other Level of Automation<li> <b>automated driving:</b> Other Level of Automation<li> <b>V2X:</b> V2X
|
[
[
{
"end": 564,
"label": "levelOfAutomation",
"start": 547
},
{
"end": 88,
"label": "entityConnectionType",
"start": 85
},
{
"end": 304,
"label": "entityConnectionType",
"start": 301
},
{
"end": 419,
"label": "entityConnectionType",
"start": 416
},
{
"end": 780,
"label": "entityConnectionType",
"start": 777
},
{
"end": 9,
"label": "communicationType",
"start": 7
},
{
"end": 239,
"label": "communicationType",
"start": 213
},
{
"end": 83,
"label": "entityConnectionType",
"start": 62
},
{
"end": 185,
"label": "levelOfAutomation",
"start": 154
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Included a few more communicationType instances:\n-5G\n-V2X communication\n-communication technologies\n-V2X application\n-V2X use cases\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 89,
"label": "entityConnectionType",
"start": 62
},
{
"end": 185,
"label": "levelOfAutomation",
"start": 154
},
{
"end": 185,
"label": "levelOfAutomation",
"start": 168
},
{
"end": 564,
"label": "levelOfAutomation",
"start": 547
},
{
"end": 88,
"label": "entityConnectionType",
"start": 85
},
{
"end": 304,
"label": "entityConnectionType",
"start": 301
},
{
"end": 419,
"label": "entityConnectionType",
"start": 416
},
{
"end": 780,
"label": "entityConnectionType",
"start": 777
}
] | null | null |
f53553f8-73df-4d42-a753-d9e6f5865299
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T09:50:29.166000 |
c5591c3f-7895-497f-8225-8475cf2deba9
|
Improved orchestration capabilities able to cope with the unique requirements of vehicular use cases and with improved network management and re-configurability capabilities to cope with the dynamicity in terms of traffic demand in vehicular scenarios. Finally, it is worth mentioning that 3GPP is working on enhancing the C-V2X technology within the further 3GPP New Radio (NR) framework [35].IEEE is, at the same time, working on enhancing 802.11p in the new project P802.11bd, with the aim to increase throughput, range, and improve procedures for positioning [36].The main challenges are related to the design of the new V2V broadcast, groupcast, and unicast SL communication interfaces to support the new demanding KPIs, e.g., the high data rate for cooperative perception, high reliability, and latency for remote driving.
|
<li> <b>vehicular use cases:</b> Other Scenario<li> <b>vehicular scenarios:</b> Other Scenario<li> <b>C-V2X:</b> Cellular (4G/5G)<li> <b>802.11p:</b> ITS-G5<li> <b>V2V:</b> V2V<li> <b>remote driving:</b> Other Scenario
|
[
[
{
"end": 100,
"label": "scenarioType",
"start": 81
},
{
"end": 251,
"label": "scenarioType",
"start": 232
},
{
"end": 328,
"label": "communicationType",
"start": 323
},
{
"end": 449,
"label": "communicationType",
"start": 442
},
{
"end": 628,
"label": "entityConnectionType",
"start": 625
},
{
"end": 827,
"label": "scenarioType",
"start": 813
},
{
"end": 478,
"label": "communicationType",
"start": 469
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 100,
"label": "scenarioType",
"start": 81
},
{
"end": 251,
"label": "scenarioType",
"start": 232
},
{
"end": 328,
"label": "communicationType",
"start": 323
},
{
"end": 449,
"label": "communicationType",
"start": 442
},
{
"end": 628,
"label": "entityConnectionType",
"start": 625
},
{
"end": 827,
"label": "scenarioType",
"start": 813
}
] | null | null |
29988c68-8141-41ee-9cec-fdf09eec826b
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:27:28.299000 |
15bac94d-705e-4849-8d91-5bb1d43f729b
|
Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.
|
<li> <b>hybrid electric vehicles:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
"end": 97,
"label": "vehicleType",
"start": 73
},
{
"end": 185,
"label": "vehicleType",
"start": 161
},
{
"end": 1010,
"label": "vehicleType",
"start": 986
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 97,
"label": "vehicleType",
"start": 73
},
{
"end": 185,
"label": "vehicleType",
"start": 161
},
{
"end": 1010,
"label": "vehicleType",
"start": 986
},
{
"end": 97,
"label": "vehicleType",
"start": 89
},
{
"end": 185,
"label": "vehicleType",
"start": 177
},
{
"end": 1010,
"label": "vehicleType",
"start": 1002
}
] | null | null |
a91a3417-d30f-4a37-8339-16ffe6a6b94a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T09:41:32.338000 |
2edfa996-a045-4952-8311-a885b47d3824
|
Comprehensive Coverage of Latest Advancements: The paper offers a comprehensive coverage of the latest advancements in HESS technology.By synthesizing information scattered across the literature, it presents an up-to-date account of the most recent developments, ensuring a thorough understanding of the current state-of-the-art in this field.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
2e4f3a25-5d5d-443f-b4ec-a4db03dd6b97
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T09:41:17.870000 |
6d851a60-22ec-44a8-a9c7-7a79379c7b3f
|
Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the polynomial kernel (PK) function and radial basis function (RBF). A mathematical model of the maneuvering of the USV was established and subjected to a zig-zag test on the DW-uBoat USV platform to obtain the test data. Cross-validation was used to optimize the parameters of SVR and determine suitable weight coefficients in the MK function to ensure the adaptive adjustment of the proposed method. The PK-SVR, RBF-SVR, and MK-SVR methods were used to identify the dynamics of the USV and build the corresponding predictive models. A comparison of the results of the predictions with experimental data confirmed the limitations of the SVR with a single kernel function in terms of forecasting different parameters of motion of the USV while verifying the validity of the MK-SVR based on data collected from a full-scale test. The results show that the MK-SVR method combines the advantages of the local and global kernel functions to offer a better predictive performance and generalization ability than SVR based on the nuclear kernel function. The purpose of this manuscript is to propose a novel method of dynamics identification for USV, which can help us establish a more precise USV dynamic model to design and verify an excellent motion controller.
|
<li> <b>unmanned surface vehicle (USV):</b> Other Vehicle<li> <b>USV:</b> Other Vehicle
|
[
[
{
"end": 70,
"label": "vehicleType",
"start": 40
},
{
"end": 69,
"label": "vehicleType",
"start": 66
},
{
"end": 407,
"label": "vehicleType",
"start": 404
},
{
"end": 475,
"label": "vehicleType",
"start": 472
},
{
"end": 775,
"label": "vehicleType",
"start": 772
},
{
"end": 1025,
"label": "vehicleType",
"start": 1022
},
{
"end": 1431,
"label": "vehicleType",
"start": 1428
},
{
"end": 1479,
"label": "vehicleType",
"start": 1476
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"USV is not of interest for CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 70,
"label": "vehicleType",
"start": 40
},
{
"end": 69,
"label": "vehicleType",
"start": 66
},
{
"end": 407,
"label": "vehicleType",
"start": 404
},
{
"end": 475,
"label": "vehicleType",
"start": 472
},
{
"end": 775,
"label": "vehicleType",
"start": 772
},
{
"end": 1025,
"label": "vehicleType",
"start": 1022
},
{
"end": 1431,
"label": "vehicleType",
"start": 1428
},
{
"end": 1479,
"label": "vehicleType",
"start": 1476
}
] | null | null |
64b63366-528b-42d3-b898-7ff9cae6e5e1
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T11:50:27.227000 |
e916bdab-02d8-4272-b63d-cff349d72708
|
The SVM is a statistical method based on supervised learning that was proposed in the 1990s [9].The objective of the SVM is to obtain the equation of the hyperplane of structural risk minimization.In the context of regression, the SVM is called the SVR.For the training data {(x i ,y i ), i = 1, 2, . . .N}, where x i ∈ R n is the input data, y i ∈ R is the target value for x i and N is the number of training samples.The SVR equation of the hyperplane is presented as: where ω denotes the matrix of weights and b denotes the bias constant. In contrast to traditional regression algorithms, SVR enables the maximum deviation ε between f(x) and y i .The value of the loss function l ε is chosen according to the following formula: Therefore, the equation of the hyperplane can be obtained by minimizing the optimization problem as follows: where C is the regularization parameter.Slack variables ξ i and ξi were selected to make the model more robust so that it can handle unfeasible constraints of the optimization problem as follows: The optimal solution can be obtained by solving the dual form of Equation (7).The Lagrange multipliers were imposed to solve this problem: where µ i ≥ 0, μi ≥ 0, α ≥ 0, α ≥ 0. We then computed the derivatives of ω, b, e i , and α i , and set them to zero to obtain the following: By placing Equation ( 9) into the Lagrangian function, the dual problem can be expressed as follows: Equation ( 10) meets the Karush-Kuhn-Tucker conditions as follows: The following solution can be acquired by calculating the dual problem:
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
abcc5e71-0c9a-4e85-af38-99118d519cb6
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:02:19.809000 |
0b78a601-c183-4653-a354-d00225c72ca1
|
As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.
|
<li> <b>electric vehicle:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
"end": 19,
"label": "vehicleType",
"start": 3
},
{
"end": 209,
"label": "vehicleType",
"start": 193
},
{
"end": 570,
"label": "vehicleType",
"start": 554
},
{
"end": 923,
"label": "vehicleType",
"start": 907
},
{
"end": 1352,
"label": "vehicleType",
"start": 1336
},
{
"end": 1506,
"label": "vehicleType",
"start": 1490
},
{
"end": 876,
"label": "vehicleType",
"start": 868
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicle\" can be 2-wheeler, 3-wheeler or minivan and not a car exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 19,
"label": "vehicleType",
"start": 3
},
{
"end": 209,
"label": "vehicleType",
"start": 193
},
{
"end": 570,
"label": "vehicleType",
"start": 554
},
{
"end": 923,
"label": "vehicleType",
"start": 907
},
{
"end": 1352,
"label": "vehicleType",
"start": 1336
},
{
"end": 1506,
"label": "vehicleType",
"start": 1490
},
{
"end": 876,
"label": "vehicleType",
"start": 868
}
] | null | null |
8e3411ab-af30-4ec0-8f75-71b7dd1236ea
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T09:02:01.476000 |
0ef17725-5e23-43bb-b58a-d2c35c55405c
|
In the following, results of the flexibility estimation are shown which consider weights in the aggregation process from individual profiles, e.g., for uncontrolled charging, to the estimated average EV profiles.As shown in Table 3, this flexibility is described by the four profiles' battery drain drain elec (t), maximum charging capacity p charge, max (t) as well as minimum (SoC min (t)) and maximum SoC (SoC max (t)).A fifth profile, uncontrolled charging charge unc (t), is calculated as well, assuming vehicles are charged as soon as a grid connection is available. Figure 3 shows the counteracting effects of increased travel distances in a lower number of trips [18].Charging is available when vehicle owners are at home which is mainly during the morning, evening and night.While daytime grid connection shares are higher based on the MiD17 data set during workdays, the opposite is the case in a milder intensity for the weekend.This can be explained by almost doubling average trip distance travelled on Sunday, compensating for less trips being carried out.However, as described above, this effect is small compared to the overall change in fleet grid connection share.Compared to the differences in fleet grid connection shares, changes in mobility patterns imply stronger changes in electricity outflow from the battery for driving as depicted in Figure 4 since mobility changes directly translate into drain changes.Increased average trip distance travelled between 9:00 and 17:00 leads to increased electricity consumption for driving mainly from 8:00 to 17:00.In its peak at 16:00, the average electricity consumption is 0.3 kWh/h based on MiD08, whereas it is 0.55 kWh/h based on MiD17.The average electricity consumption for driving shows characteristic morning and evening peaks throughout the week.The maximum in fleet consumption for driving drain elec (t) can be observed for evenings with approximately 0.6 kWh/h per vehicle on average.Increased average trip distance in MiD17 compared to MiD08 leads to higher electricity consumption at almost every hour.Sunday differences in consumption are higher by almost a factor of 2, from 0.35 kWh/h to 0.7 kWh/h on average.Saturdays show a broader travel characteristic during daytime the but peaking below Tuesday evening travels. Uncontrolled charging shown in Figure 5 differs between the two samples in the time interval between 16:00 and 21:00 with its peak at 18:00.In this peak, charge unc (t) results in 0.5 and 0.8 kW for MiD08 and MiD17, respectively.The patterns in the uncontrolled charging scenario are thus quite similar. The trends described for an average day are mirrored by weekly EV fleet uncontrolled charging.The peak broadened a bit and increased in size from under 0.6 to 0.8 kW.On Fridays and Saturdays uncontrolled charging is more distributed throughout the day with increasing peaks in the evenings.Sundays show the all-week high slightly below 1 kW on average, this having increased more than two-fold showing the stark travel increase on Sundays between the MiD08 and MiD17. SoC profiles shown in Figure 6 depict a temporally dependent average battery range for load shifting.This battery range is reduced by a reduced maximum SoC and an increased minimum SoC at daytimes when vehicles on the road cannot charge but need a minimum of SoC for the planned trip on that respective day. The average vehicle SoC is smaller for the estimation based on the MiD17 dataset, which is due to increased travel distances in the sample.This reduction is most pronounced on Sundays when the two profiles almost touch, implying no charging flexibility for the average vehicle at all.Still, during nights, an average vehicle provides a large source of flexibility with the full accumulator capacity of 50 kWh available for flexible charging. Simple methods for aggregating individual SoC profiles to fleet level as used here have been shown to overestimate the fleet load shifting potential.Mathematically correct methods exist but have been shown to currently be impractical on time horizons employed in the context of energy systems analysis (T >> 10 time steps) [2].The given profiles can be a first indication for a vehicle fleet flexibility, however greater care in applying them in the context of evidence-based decision making should be taken.
|
<li> <b>EV:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 202,
"label": "vehicleType",
"start": 200
},
{
"end": 2669,
"label": "vehicleType",
"start": 2667
},
{
"end": 517,
"label": "vehicleType",
"start": 509
},
{
"end": 3282,
"label": "vehicleType",
"start": 3274
},
{
"end": 710,
"label": "vehicleType",
"start": 703
},
{
"end": 1949,
"label": "vehicleType",
"start": 1942
},
{
"end": 3399,
"label": "vehicleType",
"start": 3392
},
{
"end": 3656,
"label": "vehicleType",
"start": 3649
},
{
"end": 3704,
"label": "vehicleType",
"start": 3697
},
{
"end": 4207,
"label": "vehicleType",
"start": 4200
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 202,
"label": "vehicleType",
"start": 200
},
{
"end": 2669,
"label": "vehicleType",
"start": 2667
},
{
"end": 517,
"label": "vehicleType",
"start": 509
},
{
"end": 3282,
"label": "vehicleType",
"start": 3274
},
{
"end": 710,
"label": "vehicleType",
"start": 703
},
{
"end": 1949,
"label": "vehicleType",
"start": 1942
},
{
"end": 3399,
"label": "vehicleType",
"start": 3392
},
{
"end": 3656,
"label": "vehicleType",
"start": 3649
},
{
"end": 3704,
"label": "vehicleType",
"start": 3697
},
{
"end": 4207,
"label": "vehicleType",
"start": 4200
}
] | null | null |
2cc8cd2d-36f9-4f78-b5fb-84bc03844e61
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T05:42:33.485000 |
e696ffae-d305-4d3a-8bde-5d762826f21e
|
With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car
|
[
[
{
"end": 60,
"label": "vehicleType",
"start": 37
},
{
"end": 166,
"label": "vehicleType",
"start": 163
},
{
"end": 446,
"label": "vehicleType",
"start": 443
},
{
"end": 1550,
"label": "vehicleType",
"start": 1547
},
{
"end": 1397,
"label": "vehicleType",
"start": 1395
},
{
"end": 506,
"label": "vehicleType",
"start": 504
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"EV in singular is not assigned. There are 2 instances of \"EV users\" where \"EV\" is not linked to \"Car\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 60,
"label": "vehicleType",
"start": 37
},
{
"end": 59,
"label": "vehicleType",
"start": 56
},
{
"end": 166,
"label": "vehicleType",
"start": 163
},
{
"end": 446,
"label": "vehicleType",
"start": 443
},
{
"end": 1550,
"label": "vehicleType",
"start": 1547
}
] | null | null |
2d82c421-c409-467b-a473-f4ce00f3758f
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-23T21:16:57.773000 |
d43cd14c-9f8d-4ee2-aa2f-5c10e6631a55
|
In order to compare the effects of different robust adjustment factors (β) on the VPP's operating revenue, Table 6 below shows the variation in the VPP's operating revenue with different robust adjustment coefficients.In Table 6, as β increases, the operator's use of wind power output becomes gradually more conservative, which then leads to a gradual decrease in the VPP's operating revenue.When β changes from 0 to 1, it indicates that the VPP's attitude toward wind power usage gradually changes from proactive to pessimistic and conservative.However, the charging cost of EV users does not change during this process.This indicates that when the VPP's use of wind turbine output changes, the operator will always prioritize the interests of EV users and will not transfer the risk cost to the charging cost of EV users, but will rely on the energy storage within the VPP, the regulated capacity of the demand response load, or the purchase and sale of electricity in the market to maintain the stable operation of the system.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
ed769c2f-d8b1-4732-b63e-b18e1dca8c46
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T07:56:22.780000 |
bcdc0d6a-6c83-4d8f-b92b-4c2b4e61b4b4
|
In this paper, it is proposed to carry out the assessment of road transport hazards using the minimization of the negative impact by three main components: impact of changes in emissions of toxic elements of pollutants from the exhaust gases of car engines, change of noise pollution of the environment and change of the number of accidents. The method of determination of equivalent losses which will be as a result of the release of a conventional ton of toxic components of pollutants from the exhaust gases of car engines and the impact of noise pollution of the environment on three groups of components – drivers and passengers, pedestrians and residents of adjacent territories is justified. Special attention is paid to determining equivalent losses due to traffic accidents with injured or dead. For example, a study to determine the change in environmental hazards of road transport for the section of Lubinska Str. in Lviv, depending on the main indicator of traffic flow - the speed of traffic was carried out. It is established that for the speed of 25 km/h the minimum environmental damage will be 1093 thousand UAH per year, and the minimum total hazards of road transport, taking into account the possibility of an accident at 12 km/h will be 1239 thousand UAH per year. With the help of the obtained model, it is possible to determine the amount of hazards from road transport, which allows taking into account environmental, social and economic components when studying the levels of the negative impact of transport on the environment and ensuring minimum accident rates on the studied section of the road network. Conducting preliminary theoretical research to find rational solutions when applying schemes to improve traffic organization will be especially useful.
|
<li> <b>car engines:</b> Car<li> <b>pedestrians:</b> Pedestrian
|
[
[
{
"end": 256,
"label": "vehicleType",
"start": 245
},
{
"end": 525,
"label": "vehicleType",
"start": 514
},
{
"end": 646,
"label": "VRUType",
"start": 635
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 256,
"label": "vehicleType",
"start": 245
},
{
"end": 525,
"label": "vehicleType",
"start": 514
},
{
"end": 646,
"label": "VRUType",
"start": 635
}
] | null | null |
9567b626-6c41-47e7-a4f4-c90e78eb1562
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:34:37.668000 |
ab5da189-2ad6-48ea-acbf-4a617da378ac
|
The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.
|
<li> <b>pedestrian:</b> Pedestrian<li> <b>vehicle:</b> Other Vehicle<li> <b>autonomous vehicles:</b> Other Vehicle<li> <b>LiDAR sensor:</b> LiDAR<li> <b>pedestrian detection:</b> Other Scenario
|
[
[
{
"end": 41,
"label": "VRUType",
"start": 31
},
{
"end": 441,
"label": "VRUType",
"start": 431
},
{
"end": 631,
"label": "VRUType",
"start": 621
},
{
"end": 130,
"label": "vehicleType",
"start": 123
},
{
"end": 530,
"label": "vehicleType",
"start": 511
},
{
"end": 1222,
"label": "vehicleType",
"start": 1203
},
{
"end": 687,
"label": "sensorType",
"start": 675
},
{
"end": 756,
"label": "scenarioType",
"start": 736
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 41,
"label": "VRUType",
"start": 31
},
{
"end": 441,
"label": "VRUType",
"start": 431
},
{
"end": 631,
"label": "VRUType",
"start": 621
},
{
"end": 746,
"label": "VRUType",
"start": 736
},
{
"end": 130,
"label": "vehicleType",
"start": 123
},
{
"end": 530,
"label": "vehicleType",
"start": 511
},
{
"end": 1222,
"label": "vehicleType",
"start": 1203
},
{
"end": 687,
"label": "sensorType",
"start": 675
},
{
"end": 756,
"label": "scenarioType",
"start": 736
}
] | null | null |
31df81f7-ee64-4901-996d-215328a774ef
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:55:25.520000 |
7a3aa19c-beaa-4321-ab09-51f133b7cd50
|
To compare the pedestrian detection performance between the two agents, we extracted LiDAR bin files for onboard and intersection data from the ROS bag files and label txt files containing pedestrian information.The ROS-ApproximateTimeSynchronizer package was utilized for extraction, and the label files stored label data in the order of x, y, z, dx, dy, dz, heading, and class type for 3D LiDAR detection training.As a result, the number of extracted data for each scenario is shown in Table 7 below.
|
<li> <b>pedestrian:</b> Pedestrian<li> <b>LiDAR:</b> LiDAR
|
[
[
{
"end": 25,
"label": "VRUType",
"start": 15
},
{
"end": 199,
"label": "VRUType",
"start": 189
},
{
"end": 90,
"label": "sensorType",
"start": 85
},
{
"end": 396,
"label": "sensorType",
"start": 391
},
{
"end": 475,
"label": "scenarioType",
"start": 467
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Added scenarioType instance for the term \"scenario\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 25,
"label": "VRUType",
"start": 15
},
{
"end": 199,
"label": "VRUType",
"start": 189
},
{
"end": 90,
"label": "sensorType",
"start": 85
},
{
"end": 396,
"label": "sensorType",
"start": 391
}
] | null | null |
0fab229b-0600-457e-9450-b828fff97d23
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T11:37:37.653000 |
956a3d70-2cdb-4524-b4f5-6cedb2908686
|
The analysis of single vehicle type dynamic marginal cost is extended to multiple vehicle type dynamic one based on time‐dependent multiple vehicle type queue analysis at a bottleneck. First, a dynamic link model to rep‐ resent the interactions between cars and trucks is provided. Then, the analytic expression of a multiple vehicle type dynamic marginal cost function considering departure time choices is deduced under congested and un‐congested conditions and consequently, a dynamic toll function is given. A heuristic algorithm is introduced to solve multiple vehicle type dynamic queues and toll under system optimum and user equilibrium conditions taking into account traveler's departure time. A numerical example shows that a dynamic congestion toll can diminish queues and improve system conditions when traffic demand is not changed.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>cars:</b> Car<li> <b>trucks:</b> Truck
|
[
[
{
"end": 30,
"label": "vehicleType",
"start": 23
},
{
"end": 89,
"label": "vehicleType",
"start": 82
},
{
"end": 147,
"label": "vehicleType",
"start": 140
},
{
"end": 333,
"label": "vehicleType",
"start": 326
},
{
"end": 573,
"label": "vehicleType",
"start": 566
},
{
"end": 257,
"label": "vehicleType",
"start": 253
},
{
"end": 268,
"label": "vehicleType",
"start": 262
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 30,
"label": "vehicleType",
"start": 23
},
{
"end": 89,
"label": "vehicleType",
"start": 82
},
{
"end": 147,
"label": "vehicleType",
"start": 140
},
{
"end": 333,
"label": "vehicleType",
"start": 326
},
{
"end": 573,
"label": "vehicleType",
"start": 566
},
{
"end": 257,
"label": "vehicleType",
"start": 253
},
{
"end": 268,
"label": "vehicleType",
"start": 262
}
] | null | null |
96b900a3-249e-4b56-b954-4a09a095a1ce
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:22:06.876000 |
66db00b5-39ae-452d-8f90-d076f40a4045
|
According to the above analysis, the toll of a car / truck can be written as: where: 1 i s and 2 i s are the times at which the first and last car and truck traveler respectively depart home.Furthermore, 0 a t and 1 a t are the times at which transition from Case 3 to 4 occurs at the early arrival stage and the time at which transition from Case 4 to 3 occurs at the late arrival stage respectively.0 t and 1 t describe respectively the starting and ending time of the total congested queue.The above toll function cannot guarantee a nonnegative toll value, and thus non-negative transformation is given as follows:
|
<li> <b>car:</b> Car<li> <b>truck:</b> Truck
|
[
[
{
"end": 50,
"label": "vehicleType",
"start": 47
},
{
"end": 146,
"label": "vehicleType",
"start": 143
},
{
"end": 58,
"label": "vehicleType",
"start": 53
},
{
"end": 156,
"label": "vehicleType",
"start": 151
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 50,
"label": "vehicleType",
"start": 47
},
{
"end": 146,
"label": "vehicleType",
"start": 143
},
{
"end": 58,
"label": "vehicleType",
"start": 53
},
{
"end": 156,
"label": "vehicleType",
"start": 151
}
] | null | null |
f806f29a-8ef8-4a2e-8336-df5edb45c89b
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:43:50.970000 |
b581f08c-7261-461c-acbe-ee36c3032efc
|
With the rapid development of electric vehicles (EVs), one of the urgent issues is how to deploy limited charging facilities to provide services for as many EVs as possible. This paper proposes a bilevel model to depict the interaction between traffic flow distribution and the location of charging stations (CSs) in the EVs and gasoline vehicles (GVs) hybrid network. The upper level model is a maximum flow-covering model where the CSs are deployed on links with higher demands. The lower level model is a stochastic user equilibrium model under elastic demands (SUE-ED) that considers both demands uncertainty and perceived path constraints, which have a significant influence on the distribution of link flow. Besides the path travel cost, the utility of charging facilities, charging speed, and waiting time at CSs due to space capacity restraint are also considered for the EVs when making a path assignment in the lower level model. A mixed-integer nonlinear program is constructed, and the equivalence of SUE-ED is proven, where a heuristic algorithm is used to solve the model. Finally, the network trial and sensitivity analysis are carried out to illustrate the feasibility and effectiveness of the proposed model.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car<li> <b>gasoline vehicles (GVs):</b> Car
|
[
[
{
"end": 53,
"label": "vehicleType",
"start": 30
},
{
"end": 160,
"label": "vehicleType",
"start": 157
},
{
"end": 324,
"label": "vehicleType",
"start": 321
},
{
"end": 883,
"label": "vehicleType",
"start": 880
},
{
"end": 352,
"label": "vehicleType",
"start": 329
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" and \"GVs\" may include other vehicles besides cars as well (2-wheelers, 3-wheelers, minivans, etc.)"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 53,
"label": "vehicleType",
"start": 30
},
{
"end": 52,
"label": "vehicleType",
"start": 49
},
{
"end": 160,
"label": "vehicleType",
"start": 157
},
{
"end": 324,
"label": "vehicleType",
"start": 321
},
{
"end": 883,
"label": "vehicleType",
"start": 880
},
{
"end": 352,
"label": "vehicleType",
"start": 329
}
] | null | null |
6d1395a3-2f09-4e0b-acce-92c971065518
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T08:48:08.149000 |
21a23752-e11e-42b9-bf74-50900c2d4a25
|
This paper studies the deployment of public charging stations in a hybrid network to maximize the service efficiency of the charging facilities.A bilevel model was proposed to depict the interaction between the mixed link flow and the location of the charging stations.The link flow obtained by the lower SUE-ED model is the key to determine the location of the CSs.In the upper level model, the CSs were arranged on the link flow ranking p to achieve the maximum coverage.Four important factors were taken into accounts in the lower level SUE model, including the range limit of EVs that affected the path choice, the elastic travel demand closely related to the distribution of link flow, the road congestion effect, and the capacity of charging facilities in travel costs.These four elements interact with each other and continuously iterate to reach equilibrium, which was more consistent with the actual travel situation.A hybrid integer nonlinear programming method based on the method of successive average (MSA) was constructed to prove the equivalence and the uniqueness of the SUE-ED model with range constraints.Finally, a network trial was conducted to examine the impact of elastic demand between OD pairs, the range limit, charging speed, the charging facilities' utility, and waiting cost on the location problem. It should be noted that the actual road network systems are very diverse, especially in urban areas, and significantly differ from the example of the Nguyen-Dupuis Network.More realistic factors are required to be considered in the future when designing the location of charging stations, not only by technical and operational factors but also by social factors.And assumptions can be relaxed appropriately in future work.The nonlinearity of the charging time, the uncertainties in EVs energy consumption, as well as the bounded rationality of EV travelers, should be considered.
|
<li> <b>EVs:</b> Car
|
[
[
{
"end": 583,
"label": "vehicleType",
"start": 580
},
{
"end": 1814,
"label": "vehicleType",
"start": 1811
},
{
"end": 1875,
"label": "vehicleType",
"start": 1873
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"EV\" was not identified as vehicleType\n- \"EVs\" are not cars exclusively (2-wheelers, 3-wheelers, minivans, etc.)\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 583,
"label": "vehicleType",
"start": 580
},
{
"end": 1814,
"label": "vehicleType",
"start": 1811
}
] | null | null |
cdd6737e-76a6-42d6-bd8e-76754134f948
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:52:51.507000 |
ec77dbdf-f93b-4c1d-814c-c953cff3d797
|
The transportation sector has the highest energy demand worldwide and bears the primary responsibility for CO2 emissions. Electromobility has emerged as the most feasible way to alleviate this problem. However, its potential depends heavily on the development of renewable energies. Island regions raise additional barriers to electromobility due to their heavy dependence on fossil fuels. This article addresses this challenge by presenting a comprehensive well-to-wheel framework to assess the levels of efficiency and CO2 emissions of electromobility options such as battery and plug-in electric vehicles (BEVs and PHEVs). The results were compared with those for internal combustion engine vehicles (ICEVs). The framework proposed takes account of various factors including the extraction, refining, and transport of oil, different segments of land vehicles, and electricity system operations. The framework is demonstrated with a case study of the Spanish Canary archipelago. The results show that BEVs improve efficiency and CO2 emissions by around 30% compared to ICEVs on islands where the share of renewable energies is higher than 21%. In contrast, limited renewable generation may lead to BEVs polluting up to 15% more than ICEVs. PHEVs should be considered as a suitable alternative if the share of renewable generation is higher than 35%.
|
<li> <b>battery and plug-in electric vehicles (BEVs and PHEVs):</b> Car<li> <b>BEVs:</b> Car<li> <b>PHEVs:</b> Car<li> <b>internal combustion engine vehicles (ICEVs):</b> Car<li> <b>land vehicles:</b> Other Vehicle<li> <b>ICEVs:</b> Car
|
[
[
{
"end": 624,
"label": "vehicleType",
"start": 570
},
{
"end": 613,
"label": "vehicleType",
"start": 609
},
{
"end": 1007,
"label": "vehicleType",
"start": 1003
},
{
"end": 1204,
"label": "vehicleType",
"start": 1200
},
{
"end": 623,
"label": "vehicleType",
"start": 618
},
{
"end": 1247,
"label": "vehicleType",
"start": 1242
},
{
"end": 710,
"label": "vehicleType",
"start": 667
},
{
"end": 861,
"label": "vehicleType",
"start": 848
},
{
"end": 709,
"label": "vehicleType",
"start": 704
},
{
"end": 1076,
"label": "vehicleType",
"start": 1071
},
{
"end": 1240,
"label": "vehicleType",
"start": 1235
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"BEVs\", \"PHEVs\" and \"ICEVs\" might not be cars exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 624,
"label": "vehicleType",
"start": 570
},
{
"end": 613,
"label": "vehicleType",
"start": 609
},
{
"end": 1007,
"label": "vehicleType",
"start": 1003
},
{
"end": 1204,
"label": "vehicleType",
"start": 1200
},
{
"end": 623,
"label": "vehicleType",
"start": 618
},
{
"end": 1247,
"label": "vehicleType",
"start": 1242
},
{
"end": 710,
"label": "vehicleType",
"start": 667
},
{
"end": 861,
"label": "vehicleType",
"start": 848
},
{
"end": 709,
"label": "vehicleType",
"start": 704
},
{
"end": 1076,
"label": "vehicleType",
"start": 1071
},
{
"end": 1240,
"label": "vehicleType",
"start": 1235
}
] | null | null |
a7c77ee0-1b3a-4fe8-b8d7-a2cce05c652a
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:02:55.774000 |
b10bd68c-190f-468d-a30c-4cb235bbea5b
|
Figure 3 details the energy pathways for each vehicle and the processes covered from the well of the energy source to the vehicle's wheels (given that 96% of the primary energy sources in the Canaries are oil based).In Figure 3, each step (s) of the process and each emission focus (f) are shown along the pathways.The first step is the extraction of crude, which in the Canaries is a mix from more than 20 producing countries.The oil exporting nations most represented in Spain's oil imports are Nigeria (20%), Mexico (15%), Saudi Arabia (10%), Kazakhstan (8%), Iraq (6%), the United States (6%), and Brazil (5%) [28].The average energy consumption for the extraction, flaring, and venting associated with oil extraction (Figure 3, s = 1) for an average yield is between 92.7% and 94.3%.These processes are responsible for emissions allocated out of the Canaries of around 3.75-4.75g CO 2 /MJ (f = 1) [29]. Crude is transported by sea using oil tankers (Figure 3, s = 2), with load capacities from 60,000 to 160,000 tons per trip.The energy consumption of one of these tankers is nearly 49 to 51 tons of fuel per day [16].Table 1 details the distance of the routes to continental Spain and the energy efficiency of transport measured in relative energy losses related to the energy contained in the cargo.The oil refining efficiencies and CO 2 -eq emissions per fuel product from Spanish facilities are not easily accessible due to confidentiality concerns and a lack of industry transparency.The refineries' energy efficiency and emissions depend on the following: (i) the level of technological development; (ii) the type of refinery composition; (iii) the investment in energy efficiency; (iv) the type of crude; (v) the output products produced; (vi) the environmental requirements arising from national legislation; (vii) the location of the plant, which determines the climatic and seasonal factors and the planning and optimization of each process; and (viii) the implementation of the energy management strategies. The crude oil destined to fuel the Canary Island's energy consumption is sourced from various countries and is usually refined in one or more of the eight Spanish refineries.As per Han et al. [29], the efficiencies and emissions from refining processing in the European plants were included in the study (see Table 2).From EuroStat [30], the average consumption of oil derivatives for the last decade (2011-2020) was recorded with the following share range for each product: (i) 3.7-4.1% of LPG; (ii) 44.2-47.0%gasoline; (iii) 26.1-29.5% diesel; and (iv) 2.5-4.2%fuel oil.There was a variation in the energy efficiency of the process (s = 3.1 to 3.4) and the associated emissions for each petroleum product (f = 3.1 to 3.4).In a subsequent step, the final oil-derivate products are separated and transported to the Canary Islands in specific tankers.Therefore, to calculate the efficiency of the process, the calculation of the process's efficiency was simplified by using the diesel energy consumption data of a representative vessel [16].The efficiency of the tanker that transports the oil-derivates from Iberian Spain to the Canary Islands (1500 km) is around 99.5% (s = 4) and pollutes between 0.6 and 0.4 g CO 2 -eq /MJ of fuel (f = 4). Once the product arrives on the islands, the fuel is discharged and stored.Therefore, all emissions from this downstream process related to the movement and fuel processing are considered as indirect-local emissions (f = 5 to 5.3).The process efficiencies (s = 5.1 to 5.3) and the emissions of the diesel and fuel-oil storage were calculated.The process generally consumes steam to maintain adequate temperatures and conserve energy.The efficiencies in road transport are between 99.6% and 99.9% for diesel or gasoline and 99.5% and 99.7% for LPG [16].Finally, they arrive at the refueling stations to load the tanks of the ICEVs (completing the WtT path) or to the power plants to produce electricity (following in the next subsection).
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>oil tankers:</b> Other Vehicle<li> <b>tanker:</b> Other Vehicle<li> <b>tankers:</b> Other Vehicle<li> <b>vessel:</b> Other Vehicle<li> <b>ICEVs:</b> Car
|
[
[
{
"end": 53,
"label": "vehicleType",
"start": 46
},
{
"end": 129,
"label": "vehicleType",
"start": 122
},
{
"end": 3896,
"label": "vehicleType",
"start": 3891
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Oil tankers, tankers and vessels do not seem relevant for CCAM (at least we emphasize on ground automated mobility)\nConsequently, ICEV should be generally treated as a wheeled vehicle (\"Car\") in our case, but in this specific text it refers to vessels.\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 53,
"label": "vehicleType",
"start": 46
},
{
"end": 129,
"label": "vehicleType",
"start": 122
},
{
"end": 953,
"label": "vehicleType",
"start": 942
},
{
"end": 3092,
"label": "vehicleType",
"start": 3086
},
{
"end": 953,
"label": "vehicleType",
"start": 946
},
{
"end": 1077,
"label": "vehicleType",
"start": 1070
},
{
"end": 2873,
"label": "vehicleType",
"start": 2866
},
{
"end": 3058,
"label": "vehicleType",
"start": 3052
},
{
"end": 3896,
"label": "vehicleType",
"start": 3891
}
] | null | null |
00769242-d4f1-48b6-b312-d8c61284cc56
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-23T21:18:09.874000 |
edd902c0-b21e-430f-a7ce-1a59ab9c151c
|
The rapidly increasing global populations and socio-economic development in the Global South have resulted in rising demand for natural resources. There are many plans for harvesting natural resources from the ocean floor, especially rare metals and minerals. However, if proper care is not taken, there is substantial potential for long-lasting and even irreversible physical and environmental impacts on the deep-sea ecosystems, including on biodiversity and ecosystem functioning. This paper reviews the literature on some potentials and risks to deep seabed mining (DSM), outlining its legal aspects and environmental impacts. It presents two case studies that describe the environmental risks related to this exploitative process. They include significant disturbance of the seabed, light and noise pollution, the creation of plumes, and negative impacts on the surface, benthic, and meso- and bathypelagic zones. The study suggests some of the issues interested companies should consider in preventing the potential physical and environmental damages DSM may cause. Sustainable mining and the use of minerals are vital in meeting various industrial demands.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
9c3ddd1b-a1d7-4795-a6b2-cdb5a1043bdf
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-26T13:48:17.715000 |
579b2edd-e4a7-4d47-9b1c-74507944b7a5
|
As the global population is forecasted to rise to almost 10 billion people by 2030 and 11 billion by 2050, the demand for minerals will follow a similar trend [1].In addition, the changing consumption patterns in the developing world will further impact the global demand for all resources, including minerals.There is a growing demand for strategic metals such as cobalt, nickel, copper, and manganese because their terrestrial reserves are fast depleting [2,3].Although some lab-based alloys can substitute minerals used by industries, this has limitations regarding capacity and quality, so mining activities will inevitably continue.Land-based mineral ores have been used to satisfy this demand in the past. However, several countries have started restricting mining activities using cheap techniques that show no concern for environmental or human health.As these countries try to prevent or even stop permitting these ill mining practices, coupled with the increase in demand, the interest to use deep seabed minerals has become more attractive [4].The Sustainable Development Goal (SDG) 12 targets clean manufacturing and the sustainable use of minerals to meet various industrial demands [5].At present, almost all mineral resources are extracted from terrestrial ore deposits.However, high-capacity and highquality ore deposits are becoming arduous to unearth, so the search expands to the deep seabed as an alternative for low-grade mining.Island countries occupy the deep-sea area within their territorial waters and Exclusive Economic Zones (EEZ), which is an area that sovereign states have special rights to explore and use its marine resources [6]. The deep seabed is generally an area 200 m below sea level.It largely lies outside the limits of coastal countries' continental shelf, defined as a section of a continent submerged beneath a shallow water area called a shelf sea.However, depending on the national coastline, it can also be within a continental shelf, especially in countries that have extended their EEZ.It is regulated by the 1982 UN Convention on the Law of the Sea (UNCLOS), called the "Constitution for the oceans" [6].This Convention is the basis for different rights and obligations concerning the oceans' uses, such as navigation, construction of pipelines and submarine cables, and national jurisdiction over coastal areas.Based on the Convention, institutions facilitating its implementation (the Commission on the Limits of the Continental Shelf, located in New York) and enforcement (The International Tribunal for the Law of the Sea located in Hamburg, Germany) were established.This legal system facilitates the peaceful settling of disputes and the protection of the oceanic environment and ecosystems. The deep seabed, also called "the area", is described in the Convention as "the seabed and ocean floor and subsoil thereof, beyond the limits of national jurisdiction."The Convention was signed by almost 200 countries, of which 168 have already ratified it, meaning that the obligations and rights established there apply to them [7].For the regions classified as continental shelves, which correspond to approximately 56% of the oceans, coastal states can develop their own rules.It is worth noting that state regulations are expected to be at least as strict as international regulations.The international seabed corresponds to approximately 44% of the oceans. The Convention was the basis for establishing the International Seabed Authority (ISA) or "the Authority."The Authority became operational in 1994 when the Convention came into force (12 months after its 60th ratification, according to article 308) [8].This institution aims to regulate activities in the deep seabed to prevent damage to ecosystems and biodiversity and even the economic advantages of seabed exploitation.The Authority has been working on a draft mining code to cover environmental, administrative, and financial aspects with a targeted deadline of 2020 for it to come into effect [8]. Article 136 of the Convention indicates that the main objective of the deep seabed mining (DSM) code is regulating the exploitation and development of mineral resources, which are the "common heritage of mankind" [7].The code means "the whole of the comprehensive set of rules, regulations, and procedures issued by ISA to regulate prospecting, exploration, and exploitation of marine minerals in the international seabed Area" [8].The Authority has issued 30 multi-year exploration permits, covering 1.3 million km 2 , or 0.7% of "the area". Those permits take the form of contracts, which establish the specific rights and obligations to the companies undertaking those activities.For a company to sign such a contract, it has to be supported by the ISA member of which the company is a national (article 4, of Annex III to the Convention).The supporting country then acts in the role of "sponsoring state".The sponsoring state is responsible for taking all necessary and appropriate measures to ensure that the sponsored companies comply with their contractual duties, with ISA regulations and with obligations arising from the Convention, such as protecting the marine environment and human life.Failure to take those measures means that those sponsoring states may also be liable for any damages which may occur [8]. However, no commercial mining activities so far occur, although DSM has been envisioned since the 1960s when the potential of extracting minerals and other resources from the seabed started to be noticed.This emergent industry took many years to develop due to three main factors:
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
c91a8cfd-95ef-46a3-80f3-4ac0bbde3809
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T06:16:55.800000 |
34ced56c-f2b6-4708-bb71-2bb08e69c5b5
|
More and more attention become to Transport Company’s functioning efficiency due to growing of goods’ nomenclature and specific requirements for their service. Existing scientific and practical approaches to managing of transportation process consider separate service of each contract individually. Up-today requirements for transportation services complicate such evaluation. These requirements primarily include transportation frequency and volumes variation in each logistics system due to seasonal consumption of material flows. Different seasonality leads to irrational use of vehicles and decrees of their efficiency. All this gives rise to the mechanism of compatible transportation service of numerous logistics systems and their material flows by any enterprise. The paper consist of next sections the analysis of scientific framework and methods on the transportation services, fleet estimation, efficiency evaluations, analysis of requirements of transportation services; Mechanism of joint transportation services; Modelling of transportation services in logistics systems, where joint efficiency estimation of transportation functioning and logistics system and conclusions. The proposed methods and tools in the complex allow to identify and evaluate the effectiveness of the joint motor transport service of logistic systems by own and hired vehicles compared to the separate on the basis of performance indicators, which vary depending on the technological parameters: transport distances, runway usage factor, cargo class, load capacity of motor vehicles . The offered approach will reveal: regularities of change of indicators of efficiency of variants of the joint motor transport service between the traditional approach (a separate calculation of efficiency for each logistics system) and the proposed (calculation of compatible services), which allows to determine the equivalent cost of transport services during motor transport maintenance of material flows. The calculations confirm the effect of use compared to the separate combined transportation of material flows, which will be shown in reducing the required amount of vehicles by 31,8% and increasing efficiency from 5% to 60%, depending on the initial values of the transportation services parameters. The results of the project can be used in the formation of a freight vehicle fleet of any enterprise that is faced with the issue of hiring transport or have its own, PL providers, transport companies, and others.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>motor transport:</b> Other Vehicle<li> <b>motor vehicles:</b> Other Vehicle<li> <b>freight vehicle:</b> Truck
|
[
[
{
"end": 591,
"label": "vehicleType",
"start": 583
},
{
"end": 1366,
"label": "vehicleType",
"start": 1358
},
{
"end": 2158,
"label": "vehicleType",
"start": 2150
},
{
"end": 1572,
"label": "vehicleType",
"start": 1558
},
{
"end": 2361,
"label": "vehicleType",
"start": 2346
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"Motor transport\" does not seem to be a vehicle type"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 591,
"label": "vehicleType",
"start": 583
},
{
"end": 1366,
"label": "vehicleType",
"start": 1358
},
{
"end": 1572,
"label": "vehicleType",
"start": 1564
},
{
"end": 2158,
"label": "vehicleType",
"start": 2150
},
{
"end": 1312,
"label": "vehicleType",
"start": 1297
},
{
"end": 1700,
"label": "vehicleType",
"start": 1685
},
{
"end": 1952,
"label": "vehicleType",
"start": 1937
},
{
"end": 1572,
"label": "vehicleType",
"start": 1558
},
{
"end": 2361,
"label": "vehicleType",
"start": 2346
}
] | null | null |
0530f3d2-326c-4fe7-9403-b57be74624dd
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T09:39:03.148000 |
19684e0a-e311-4f47-876a-6229fd5d3092
|
The capacity of an advection/diffusion model to predict sand transport under varying wave and current conditions is evaluated. The horizontal sand transport rate is computed by vertical integration of the suspended sediment flux. A correction procedure for the near-bed concentration is proposed so that model results are independent of the vertical resolution. The method can thus be implemented in regional models with operational applications. Simulating equilibrium sand transport rates, when erosion and deposition are balanced, requires a new empirical erosion law that involves the non-dimensional excess shear stress and a parameter that depends on the size of the sand grain. Comparison with several datasets and sediment transport formulae demonstrated the model’s capacity to simulate sand transport rates for a large range of current and wave conditions and sand diameters in the range 100–500 μm. Measured transport rates were predicted within a factor two in 67% of cases with current only and in 35% of cases with both waves and current. In comparison with the results obtained by Camenen and Larroudé (2003), who provided the same indicators for several practical transport rate formulations (whose means are respectively 72% and 37%), the proposed approach gives reasonable results. Before fitting a new erosion law to our model, classical erosion rate formulations were tested but led to poor comparisons with expected sediment transport rates. We suggest that classical erosion laws should be used with care in advection/diffusion models similar to ours, and that at least a full validation procedure for transport rates involving a range of sand diameters and hydrodynamic conditions should be carried out.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
2a8537fc-9051-4aa8-b049-833651619130
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-13T09:38:36.141000 |
7453eb3d-022b-4e3f-98bc-eb905286820c
|
Transporting sand in suspension is necessary in sediment transport models dealing with cohesive sediment, in order to allow the management of mixtures of sand and mud.The initial objective of this work was to evaluate the ability to predict fine to medium sand horizontal fluxes under varying wave and current conditions by using an advection-diffusion model.It has been shown that using classical erosion rate formulations from the literature in our model did not lead to consistent results.Those classical erosion laws should not be used in advection-diffusion models without a full validation procedure involving a range of sand diameters and hydrodynamic conditions.The ability to transport fine sands in our modelling framework was only made possible by introducing a new empirical erosion law.Further, the transport rate computed from the advection-diffusion model had to be independent of the vertical resolution, so that it can be implemented in regional models with operational applications.This was successfully achieved by using a correction procedure for extrapolating the near-bed concentration (in the deposition flux) from the concentration computed in the bottom layer.In addition, the actual horizontal flux in this bottom layer deserved a correction to account for very strong inverse gradients of velocity and concentration in the boundary layer.It is suggested that in a three-dimensional model, such a flux correction should be applied when solving the advection-diffusion equation for suspensions. Using the new empirical erosion flux formulation, the model demonstrated its capacity to simulate transport rates for sand diameter in the range 100-500 μm.The proposed erosion law was evaluated as best as possible for a wide range of different hydrodynamic conditions and grain sizes.This has been done despite the difficulty involved in qualifying sand transport models, illustrated by the very scattered results of classical sand transport formulations [19,41,51,60,62].This evaluation showed that our model compares well with various other sand transport models.While comparing the model with data, we showed that measured transport rates were predicted within a factor 2 in 67% of cases with current only and in 35% of cases with both waves and current.In comparison with the results obtained by Camenen and Larroudé [51], who provided the same indicators for several practical transport rate formulations (whose means are respectively 72% and 37%), our model gives reasonable results. Finally, the model presented in this study has been formulated for well-sorted, non-cohesive sediments.In the future, it would have to be adapted in the case of heterogeneous sediment composition to reach our longer term goal.where ρ is the water density, z is the elevation where the velocity u is computed in the first layer and κ is the Von Karman constant (0.4).Secondly, the wave induced shear stress is computed: where: -the wave friction factor fw is evaluated according to the Swart formulation [63]: and otherwise: , with T the wave period). The formulation of Soulsby [1] is used to account for non-linear wave-current interactions: and finally: where τm represents the mean (wave-averaged) shear stress in the direction of the current and φ is the angle between current and wave directions.τ is the maximum shear stress generated during a wave period and is either used for the expression of the skin sf or the hydraulic shear stress ff , depending on the roughness used (ks = kss or ksf).
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
b9b661bc-f32e-4e11-a1e7-fa9f298a8b07
|
completed
| 2025-04-09T16:14:38.079000 | 2025-05-19T09:40:08.627000 |
90923a38-eb3a-4b11-bfe0-96e43ca3512c
|
AbstractThe concentration of population in cities and processes of rural depopulation coupled with the generational shift to older societies represent new challenges in road safety. Here, we examine the severity of injuries suffered by the occupants of motor vehicles involved in a crash based on the population density of the area in which the crash occurs, the driver’s age and the density of their place of residence. We conduct the study in Spain, a country with one of the highest levels of elderly population concentrated in rural areas in Europe. Relational methods are used to match Eurostat’s urbanization classifications with the accident database of Spain’s Directorate General of Traffic so as to correlate each crash with the population density of the place where it occurred. A set of generalized linear models with random effects is fitted to analyze the relationship between population density and the bodily injury severity of the occupants of the vehicle(s) involved in a crash, measuring the effect of drivers’ relocation and aging by geographical area. Independence of injury severity and the degree of urbanization was rejected at the 5% significance level. While 53.8% of the Spanish population is living in densely populated areas and only 13.5% in rural areas, the latter concentrates most crashes with fatalities: 2.3 times more than in urban areas (43.5 and 18.6%, respectively). Drivers living in rural areas are more likely to be associated with serious or fatal injuries when involved in a crash in urban and intermediate areas. Moreover, drivers aged over 75 are significantly more likely to be associated with serious and fatal injuries, especially when the crash occurred in urban areas. Recent research alerts on the implications for rural (often elderly) residents of concentrating public services, particularly healthcare, in densely populated areas. Our study shows that motor crashes in more densely populated areas are also a rural health concern. Policy decision-makers need to address this issue to reduce the number of victims and their bodily injury severity.
|
<li> <b>motor vehicles:</b> Other Vehicle<li> <b>vehicle(s):</b> Other Vehicle
|
[
[
{
"end": 267,
"label": "vehicleType",
"start": 253
},
{
"end": 975,
"label": "vehicleType",
"start": 965
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 267,
"label": "vehicleType",
"start": 253
},
{
"end": 975,
"label": "vehicleType",
"start": 965
}
] | null | null |
Dataset Card for scilake-ccam
This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets
library in Load with datasets
.
Using this dataset with Argilla
To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade
and then use the following code:
import argilla as rg
ds = rg.Dataset.from_hub("SIRIS-Lab/scilake-ccam", settings="auto")
This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.
Using this dataset with datasets
To load the records of this dataset with datasets
, you'll just need to install datasets
as pip install datasets --upgrade
and then use the following code:
from datasets import load_dataset
ds = load_dataset("SIRIS-Lab/scilake-ccam")
This will only load the records of the dataset, but not the Argilla settings.
Dataset Structure
This dataset repo contains:
- Dataset records in a format compatible with HuggingFace
datasets
. These records will be loaded automatically when usingrg.Dataset.from_hub
and can be loaded independently using thedatasets
library viaload_dataset
. - The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
- A dataset configuration folder conforming to the Argilla dataset format in
.argilla
.
The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.
Fields
The fields are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.
Field Name | Title | Type | Required |
---|---|---|---|
text | Text | text | True |
links | Linked entities | text | True |
Questions
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
Question Name | Title | Type | Required | Description | Values/Labels |
---|---|---|---|---|---|
span_label | Select and classify the tokens according to the specified categories. | span | True | N/A | ['communicationType', 'sensorType', 'scenarioType', 'vehicleType', 'VRUType', 'entityConnectionType', 'levelOfAutomation'] |
assess_ner | Extracted entity validation | label_selection | True | Are the extracted entities correct? | ['Correct', 'Partially correct', 'Incorrect'] |
assess_nel | Linked vocabulary entity validation | label_selection | True | Are the linked entities in the vocabulary correct? | ['Correct', 'Partially correct', 'Incorrect'] |
comments | Comments | text | False | Additional comments | N/A |
Data Splits
The dataset contains a single split, which is train
.
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation guidelines
Validation guidelines for CCAM entities
Task Description
Your task is to validate the extraction of the different entities and their linking to their closest matching entries in the vocabulary created for SciLake.
What to Validate
For each record, please verify the following:
- Entity Spans: Are all text spans correctly identified? Are the span boundaries accurate?
- Entity Types: Are entity types correctly assigned?
- Entity Linking: Are the matching entities in the vocabulary correctly assigned?
Instructions
- Carefully read the texts.
- Review the NER spans and correct them if:
- The boundaries (start/end) are incorrect
- The entity label is wrong
- Verify that the extracted entities are correctly linked to their closest match in the vocabulary
- Add any comments or feedback you deem relevant
Validation Guidelines
- Entity Annotations: Mark spans as "Correct" only if boundaries and labels are accurate.
- Entity Extraction: Mark as "Correct" if all energy (storage) types mentioned are extracted; "Partially correct" if any are missing or incorrect.
- Vocabulary Linking: Mark as "Correct" if all links are to the appropriate entries. Use "Partially correct" if any are incorrect.
Entities
communicationType
: the technology used for communication (eg. 4G, 5G), NOT who is connecting with whomsensorType
: the type of sensor (eg. camera, LIDAR)scenarioType
: the driving scenario (eg. cut in, lane keeping)vehicleType
: the type of vehicle (eg. car, truck)VRUType
: vulnerable road users (eg. pedestrian, cyclist)entityConnectionType
: type of connection between entities (eg. V2V, V2I), NOT the technologylevelOfAutomation
: entities related to automation (eg. ALKS, driver assistance) and their relation to the FAME level of automation
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
[More Information Needed]
- Downloads last month
- 9