osmnx网络中最短路径的优化计算

Question

我的问题很简单。我必须计算osmnx网络中所有节点之间的最短路径。然而，这需要大量的时间。我想知道是否有什么可以加快/优化流程。先谢谢你

代码如下：

import osmnx as ox
import igraph as ig
import matplotlib.pyplot as plt
import pandas as pd
import networkx as nx
import numpy as np
import matplotlib as mpl
import random as rd
from IPython.display import clear_output
ox.config(log_console=True, use_cache=True)


%%time
city = 'Portugal, Lisbon'
G = ox.graph_from_place(city, network_type='drive')

G_nx = nx.relabel.convert_node_labels_to_integers(G)

weight = 'length'

G_ig = ig.Graph(directed=True)
G_ig.add_vertices(list(G_nx.nodes()))
G_ig.add_edges(list(G_nx.edges()))
G_ig.vs['osmid'] = list(nx.get_node_attributes(G_nx, 'osmid').values())
G_ig.es[weight] = list(nx.get_edge_attributes(G_nx, weight).values())

assert len(G_nx.nodes()) == G_ig.vcount()
assert len(G_nx.edges()) == G_ig.ecount()
nodes, edges = ox.graph_to_gdfs(G, nodes=True, edges=True) 

%%time
L_back_total = []
L_going_total =[]
i=1

for element in G_nx.nodes:

    clear_output(wait=True)
    L_going=[]
    L_back=[]
    
    for node in G_nx.nodes:
        
        length_going = G_ig.shortest_paths(source=element, target=node, weights=weight)[0][0]
        length_back = G_ig.shortest_paths(source=node, target=element, weights=weight)[0][0]
        
        L_going.append(length_going)
        L_back.append(length_back)

    L_back_total.append(length_back)
    L_going_total.append(length_going)
    print('Progress:', np.round(i/len(G_nx.nodes)*100, 5))
    i+=1

Answer 1

考虑使用弗洛依德WARWEL算法。

文件： https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.shortest_paths.dense.floyd_warshall.html

用法：

nx.floyd_warshall(G_ig)

Answer 2

在osmnx提供的示例中，有一个关于路由速度时间的笔记本，显示了如何并行计算