k_shortest_paths¶

Solve k shortest paths from an origin node to a destination node.

See also shortest_path to get just the one shortest path.

In [ ]:
# OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
import osmnx as ox

ox.config(use_cache=True, log_console=False)
ox.__version__
Out[ ]:
'1.1.2'
In [ ]:
center_point = (37.5661, 126.9783) # (lat, lng) Seoul, South Korea
dist = 1000
dist_type = 'bbox' # "network", "bbox"
network_type = 'drive' # "all_private", "all", "bike", "drive", "drive_service", "walk"

# Create a graph from OSM within some distance of some (lat, lng) point.
G = ox.graph_from_point(
    center_point,
    dist=dist, 
    dist_type=dist_type,
    network_type=network_type)

# Plot a graph.
fig, ax = ox.plot_graph(G)
In [ ]:
# Set origin and destination node ID
orig = list(G)[0]
dest = list(G)[-1]

length¶

In [ ]:
# Solve k shortest paths from an origin node to a destination node.
# k (int) – number of shortest paths to solve
paths = ox.k_shortest_paths(G, orig, dest, k=2, weight='length')

# routes as a list of lists of node IDs
route = list(paths)

# Plot several routes along a graph.
fig, ax = ox.plot_graph_routes(G, route, route_colors=['r', 'y'])

travel_time¶

In [ ]:
import random

# Solve k shortest paths from an origin node to a destination node.
# k (int) – number of shortest paths to solve
paths = ox.k_shortest_paths(G, orig, dest, k=2, weight='travel_time')

# routes as a list of lists of node IDs
route = list(paths)

# get node colors by linearly mapping an attribute's values to a colormap
route_colors = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(route))]

# Plot several routes along a graph.
fig, ax = ox.plot_graph_routes(G, route, route_colors=route_colors)