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"""
Some utility functions.
"""
import re
import argparse
import requests
import pandas as pd
import requests
from geopy.geocoders import Nominatim
from geopy.distance import distance
import heapq
import pickle
PR_STATIONS = "./data/pr_stations"
# Average speed in m/s
AVG_SPEED = {
"foot": 1.4,
"bike": 4.17,
"car": 13.89,
}
# Max travel time in seconds
MAX_TRAVEL_TIME = {
"foot": 60 * 60 // 4, # 15min
"bike": 60 * 60 // 2, # 30min
"car": 60 * 60, # 1h
}
PENALITIES = {
"foot": 1,
"bike": 1,
"car": 1,
"train": 1,
}
def get_penalties(sustainability: bool):
if sustainability:
PENALITIES["car"] *= 5
return PENALITIES
def get_args():
parser = argparse.ArgumentParser()
# Default date and time args
# current_date = date.today().strftime("%Y-%m-%d")
# current_time = datetime.now().strftime("%H:%M")
current_date = "2023-12-01"
current_time = "12:00"
# Help messages
loc_types = ["address", "station name", "station abbreviation", "coordinate"]
transportation_types = ["train", "tram", "ship", "bus", "cableway"]
start_help = f"Start location (Specify either {', '.join(loc_types)})"
# via_help = f"Locations to pass through (Specify either {', '.join(loc_types)})"
stop_help = f"Stop location (Specify either {', '.join(loc_types)})"
date_help = "Date of departure (Format: YYYY-MM-DD). Default: Today"
time_help = "Time of departure (Format: YYYY-MM-DD). Default: Now"
transportation_help = f"Modes of transportation (Specify from {', '.join(transportation_types)}). Default: All"
outage_simulation = f"Simulate outage of a station"
# Specify line arguments
parser.add_argument("--start", type=str, required=True, help=start_help)
# parser.add_argument("--via", type=list[str], help=via_help)
parser.add_argument("--end", type=str, required=True, help=stop_help)
parser.add_argument("--date", type=str, default=current_date, help=date_help)
parser.add_argument("--time", type=str, default=current_time, help=time_help)
parser.add_argument(
"--limit", type=int, default=3, help="Number of journeys to return"
)
parser.add_argument("--exact-travel-time", action="store_true", help=time_help)
parser.add_argument(
"--change-penalty", type=int, default=300, help="Change penalty"
)
parser.add_argument(
"--sustainability",
action="store_true",
help="Sustainability of journey",
)
parser.add_argument("--outage", action="store_true", help=outage_simulation)
return parser.parse_args()
def get_location(G, address: str) -> str:
"""
Converts an address to coordinates (latitude, longitude)
Address can be:
- Full name
- Coordinates (Format: "latitude, longitude")
Args:
address (str): Address to convert to coordinates.
Returns:
str: Coordinates of the address (Format: "latitude, longitude").
"""
pattern = re.compile(r"^\s*-?\d+\.\d+\s*,\s*-?\d+\.\d+\s*$")
# Check if it is already a coordinate
if bool(pattern.match(address)):
return address
# Check if already stationo
if address in G.nodes():
return G.nodes[address]["pos"]
# Use geopy to convert coordinates to address
geolocator = Nominatim(user_agent="sbb_project")
location = geolocator.geocode(
address, country_codes="ch", language="de", exactly_one=True
)
return "{}, {}".format(location.latitude, location.longitude)
def get_distance(start, end):
"""
Calculate distance between two coordinates in meters.
Coordinates must be in the format "latitude, longitude".
Args:
start (str): Start coordinate.
end (str): End coordinate.
Returns:
int: Distance between the two coordinates in meters.
"""
start_converted = tuple(map(float, start.split(", ")))
end_converted = tuple(map(float, end.split(", ")))
return distance(start_converted, end_converted).meters
def get_exact_travel_time(start, end, method="car"):
"""
Calculate travel time between two coordinates in seconds.
"""
endpoint = "http://www.mapquestapi.com/directions/v2/route"
if method not in ["foot", "bike", "car"]:
raise ValueError("Method must be either foot, bike or car")
names = {
"foot": "pedestrian",
"bike": "bicycle",
"car": "fastest",
}
# Search params
params = {
"key": "HnDX3JAuALRTge28jbZVWO1L538fJbZE",
"from": start,
"to": end,
"unit": "k", # Use km instead of miles
"narrativeType": "none", # Just some other parameters to omit information we don't care about
"sideOfStreetDisplay": False,
"routeType": names[method],
}
# Do GET request and read JSON
response = requests.get(endpoint, params=params)
data = response.json()
seconds = data["route"]["time"]
return seconds
def get_approx_travel_time(dist, method="car"):
"""
Calculate the approximate travel time between two coordinates in seconds.
Args:
dist (int): Distance between the two coordinates in meters.
method (str, optional): Method of transportation. Defaults to "car".
"""
return dist / AVG_SPEED[method]
def dijkstra(G, start, end, start_time, change_penalty=300, mode_penalties=PENALITIES):
# Initialize distances and time dictionary with all distances set to infinity
assert start in G.nodes(), f"Start node {start} not in graph"
assert end in G.nodes(), f"End node {end} not in graph"
distances = {
node: {
"distance": float("infinity"),
"time": start_time,
"visited": False,
}
for node in list(G.nodes())
}
# Initialise dictionary to keep track of the edges that lead to the node with the smallest distance
edges_to = {node: None for node in list(G.nodes())}
# Set the distance from the start node to itself to 0
distances[start] = {"distance": 0, "time": start_time, "visited": True}
edges_to[start] = ("", "Start", {"type": "foot", "journey_id": None, "duration": 0})
# Priority queue to keep track of nodes with their current distances
priority_queue = [(0, start)]
while priority_queue:
# Get the node with the smallest distance from the priority queue
current_distance, current_node = heapq.heappop(priority_queue)
if current_node == end:
return distances, edges_to
# Check if the current distance is smaller than the stored distance
if current_distance > distances[current_node]["distance"]:
continue
for _, neighbor, attributes in G.out_edges(current_node, data=True):
no_duration_avail = not (type(attributes["duration"]) in [int, float])
if no_duration_avail:
continue
# Initialise distance to neighbor
distance = attributes["duration"]
# Add penalty
distance *= mode_penalties[attributes["type"]]
# Compute wait time for train (if next edge is a train)
wait = 0 # number of seconds you have to wait for the train
next_is_train = attributes["type"] == "train"
prev_is_train = edges_to[current_node][-1]["type"] == "train"
changed_trip = False
if next_is_train:
wait = (
attributes["departure"] - distances[current_node]["time"]
).total_seconds()
train_departed = (
distances[current_node]["time"] > attributes["departure"]
)
train_too_far_away = (
distances[current_node]["time"] - attributes["departure"]
).total_seconds() > 60 * 60 * 1
if train_departed or train_too_far_away:
continue
if prev_is_train:
changed_trip = (
edges_to[current_node][-1]["journey_id"]
!= attributes["journey_id"]
)
# Penalise changing and waiting times
changed_mode = edges_to[current_node][-1]["type"] != attributes["type"]
# Add max of waiting time or changing penalty to current dist
if changed_mode or changed_trip:
distance += max(wait, change_penalty)
elif next_is_train:
distance += wait
# Overall dist (prev dist + dist to neighbor)
total_distance = current_distance + distance
# If the new distance is smaller, update the distance and add to the priority queue
if total_distance < distances[neighbor]["distance"]:
# Update distance and time of arrival for neighbor
time_of_arrival = distances[current_node]["time"] + pd.Timedelta(
seconds=distance
)
# print(f"Arrived at {neighbor} at {time_of_arrival}")
distances[neighbor]["distance"] = total_distance
distances[neighbor]["time"] = time_of_arrival
distances[neighbor]["visited"] = True
# for _, nneighbor, attributes in G.out_edges(neighbor, data=True):
# if nneighbor != current_node:
# distances[nneighbor]["visited"] = False
# Add edge that leads to neighbor
edges_to[neighbor] = (
current_node,
neighbor,
attributes,
)
# Update priority queue
heapq.heappush(priority_queue, (total_distance, neighbor))
print(f"Probably there is no path between {start} and {end}")
return distances, edges_to
def reconstruct_edges(edges_to, start: str, end: str):
"""
Given shortest path from start to end, reconstruct edges.
"""
path = [end]
edges = [edges_to[end]]
while path[-1] != start:
path.append(edges_to[path[-1]][0])
edges.append(edges_to[path[-1]])
return edges
def postprocess_path(edges):
"""Merge two edges if they have same transport type.
Edge Structure: (
start,
end,
{
type,
duration,
departure (only for train),
arrival (only for train),
journey_id (only for train),
trip_name (only for train),
)
Args:
edges (list[tuple]): list of travel edges.
Returns:
list[tuple]: post-processed list of travel edges.
"""
traversed = []
prev = None
for edge in edges[::-1]:
if prev is None:
prev = edge
traversed.append(edge)
continue
# Need to check the transport type and, if it is train,
# the journey id
if edge[2]["type"] == prev[2]["type"] and (
edge[2]["type"] != "train" or edge[2]["journey_id"] == prev[2]["journey_id"]
):
prev = traversed.pop()
# Merge the two edges
new_edge = (
prev[0],
edge[1],
{
"type": prev[2]["type"],
"duration": prev[2]["duration"] + edge[2]["duration"],
},
)
if edge[2]["type"] == "train":
new_edge[2]["departure"] = prev[2]["departure"]
new_edge[2]["arrival"] = edge[2]["arrival"]
new_edge[2]["journey_id"] = edge[2]["journey_id"]
new_edge[2]["trip_name"] = edge[2]["trip_name"]
else:
new_edge = edge
prev = new_edge
traversed.append(new_edge)
return traversed
def pretty_time_delta(seconds):
seconds = int(seconds)
days, seconds = divmod(seconds, 86400)
hours, seconds = divmod(seconds, 3600)
minutes, seconds = divmod(seconds, 60)
if days > 0:
return "%dd%dh%dm%ds" % (days, hours, minutes, seconds)
elif hours > 0:
return "%dh%dm%ds" % (hours, minutes, seconds)
elif minutes > 0:
return "%dm%ds" % (minutes, seconds)
else:
return "%ds" % (seconds,)
def pretty_print(edges, args):
"""Prints the edges in a nice format.
Args:
edges (list[tuple]): list of travel edges.
"""
print(f"# Your Journey from {args.start} to {args.end}")
print(f"\nDate/ Time: {args.date} at {args.time}")
print(f"Sustainability: {args.sustainability}\n")
print("### Travel Information\n---\n")
for i, (src, dst, attr) in enumerate(edges):
if src == "Start":
src = args.start
if dst == "End":
dst = args.end
duration = pretty_time_delta(attr["duration"])
departure = attr.get("departure", None)
arrival = attr.get("arrival", None)
msg = f"{i+1}. Go by {attr['type']} from {src} to {dst} for {duration}."
if attr["type"] == "train":
msg += "\n -> Take {} ({} - {})".format(
attr["trip_name"], departure, arrival
)
print(msg)
"""
Remove all the trains from one station to another to simulate an outage.
"""
def remove_all_trains(G, from_station, to_station):
edges_to_remove = []
for edge in G.out_edges(from_station, data=True):
if edge[2]["type"] == "train" and edge[1] == to_station:
edges_to_remove.append(edge[:2])
print(
f"Removing all the edges from {from_station} to {to_station} : {len(edges_to_remove)}"
)
for edge in edges_to_remove:
G.remove_edge(*edge)
def get_final_path_md(edges, start, end, date, time, sustainability):
md = f"## Your Journey from {start} to {end}\n\n"
md += f"π
Date/ Time: {date} at {time}\n"
md += f"π Sustainable?: {sustainability}\n\n"
md += "\n### Travel Information\n"
for i, (src, dst, attr) in enumerate(edges):
if src == "Start":
src = start
if dst == "End":
dst = end
duration = pretty_time_delta(attr["duration"])
departure = attr.get("departure", None)
arrival = attr.get("arrival", None)
emoji = {
"foot": "πΆ",
"bike": "π΄",
"car": "π",
"train": "π",
}
msg = f"{i+1}. {emoji[attr['type']]} Go by {attr['type']} from {src} to {dst} for {duration}.\n"
if attr["type"] == "train":
msg += " -> Take {} ({} - {})\n".format(
attr["trip_name"], departure, arrival
)
md += msg
return md
def get_best_path(
start,
end,
date,
time,
limit,
outage=False,
sustainability=False,
change_penalty=300,
):
# Load graph
with open("graph.pkl", "rb") as f:
G = pickle.load(f)
if outage:
# Remove all the edges from 2 stations to simulate an outage
remove_all_trains(G, from_station="Renens VD", to_station="Lausanne")
# Convert start and destination to location (lon, lat)
start_loc = get_location(G, start)
end_loc = get_location(G, end)
# Add both locations to graph
G.add_node("Start", pos=start_loc)
G.add_node("End", pos=end_loc)
# Find k-closest stations from start and end
dists_from_start = []
dists_to_end = []
for station, attr in G.nodes(data=True):
try:
station_pos = attr["pos"]
dists_from_start.append((station, get_distance(start_loc, station_pos)))
dists_to_end.append((station, get_distance(end_loc, station_pos)))
except Exception as e:
continue
# Sort the distances in place
start_k_closest = sorted(dists_from_start, key=lambda x: x[1])[:limit]
end_k_closest = sorted(dists_to_end, key=lambda x: x[1])[:limit]
# Compute travel time from start to k closest stations
for mode in ["foot", "bike", "car"]:
for station, dist in start_k_closest:
travel_time = get_approx_travel_time(dist, method=mode)
G.add_edge("Start", station, duration=travel_time, type=mode)
for station, dist in end_k_closest:
travel_time = get_approx_travel_time(dist, method=mode)
G.add_edge(station, "End", duration=travel_time, type=mode)
# Run Dijkstra on graph
start_time = pd.to_datetime(f"{date} {time}")
mode_penalties = get_penalties(sustainability)
dists, edges_to = dijkstra(
G,
"Start",
"End",
start_time=start_time,
change_penalty=change_penalty,
mode_penalties=mode_penalties,
)
# Reconstruct path
edges = reconstruct_edges(edges_to, "Start", "End")
# Postprocess path
path = postprocess_path(edges[:-1])
md = get_final_path_md(path, start, end, date, time, sustainability)
return md
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