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import networkx as nx
import osmnx as ox
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import numpy as np
import warnings
from mpl_toolkits.axes_grid1 import make_axes_locatable
def map_highway_to_number_of_lanes(highway_type):
if highway_type == "motorway" or highway_type == "trunk":
return 4
elif highway_type == "primary":
return 3
elif (
highway_type == "secondary"
or highway_type == "motorway_link"
or highway_type == "trunk_link"
or highway_type == "primary_link"
):
return 2
else:
return 1
# def set_number_of_lanes(G):
# edges = ox.graph_to_gdfs(G, nodes=False)
# lanes = {e : map_highway_to_number_of_lanes(highway_type) for e, highway_type in edges['highway'].items()}
# nx.set_edge_attributes(G, lanes, 'lanes')
# return G
def set_number_of_lanes(G):
edges = ox.graph_to_gdfs(G, nodes=False)
edges["lanes"] = [
np.mean(list(map(float, v[0])))
if type(v[0]) == list
else float(v[0])
if type(v[0]) == str
else map_highway_to_number_of_lanes(v[1])
if np.isnan(v[0])
else v[0]
for k, v in edges[["lanes", "highway"]].iterrows()
]
nx.set_edge_attributes(G, edges["lanes"], "lanes")
return G
def set_actual_speed_diff(G, load_kw="load", alpha=50.0, kph_min=10):
loads = nx.get_edge_attributes(G, load_kw)
lengths = nx.get_edge_attributes(G, "length")
lanes = nx.get_edge_attributes(G, "lanes")
speed_lim = {e: v for e, v in nx.get_edge_attributes(G, "speed_kph").items()}
speeds = {
e: np.divide(alpha * lanes[e] * lengths[e], loads[e]) - 9
for e in G.edges(keys=True)
}
actual_speeds = {
e: kph_min if v < kph_min else speed_lim[e] if v > speed_lim[e] else v
for e, v in speeds.items()
}
speed_diff = {e: (speed_lim[e] - v) for e, v in actual_speeds.items()}
if load_kw == "load":
nx.set_edge_attributes(G, actual_speeds, "actual_speed")
nx.set_edge_attributes(G, speed_diff, "speed_diff")
elif load_kw == "load_flood":
nx.set_edge_attributes(G, actual_speeds, "actual_speed_flood")
nx.set_edge_attributes(G, speed_diff, "speed_diff_flood")
else:
print("wrong load_kw. Returning G")
return G
def set_daganzo_velocities(G, alpha, kph_min):
G = set_number_of_lanes(G)
loads = nx.get_edge_attributes(G, "load")
lengths = nx.get_edge_attributes(G, "length") # length in m
lanes = nx.get_edge_attributes(G, "lanes")
speed_lim = {e: v for e, v in nx.get_edge_attributes(G, "speed_kph").items()}
velocities = {
e: speed_lim[e]
if loads[e] == 0
else 60
* 60
/ 1000
* (alpha * np.divide(lanes[e] * lengths[e], loads[e] * 2) - 5 / 2)
for e in G.edges(keys=True)
}
daganzo_velo = {
e: kph_min
if v < kph_min
else speed_lim[e]
if v > speed_lim[e]
else np.round(v, 1)
for e, v in velocities.items()
}
nx.set_edge_attributes(G, daganzo_velo, "effective_velocity")
# def actual_speed(G, alpha = 50., kph_min = 10):
# loads = nx.get_edge_attributes(G, 'load')
# lengths = nx.get_edge_attributes(G, 'length')
# lanes = nx.get_edge_attributes(G, 'lanes')
# speed_lim = { e : v for e, v in nx.get_edge_attributes(G, 'speed_kph').items()}
# speeds = { e : alpha*lanes[e]*lengths[e]/loads[e] for e in G.edges(keys=True) }
#
# actual_speeds = { e : kph_min if v < kph_min else speed_lim[e] if v > speed_lim[e] else v for e, v in speeds.items() }
#
# nx.set_edge_attributes(G, actual_speeds, 'actual_speed')
# return G
def set_speed_diff(g):
speed = nx.get_edge_attributes(g, "actual_speed")
speed_kph = nx.get_edge_attributes(g, "speed_kph")
speed_diff = {k: speed_kph[k] - v for k, v in speed.items()}
nx.set_edge_attributes(g, speed_diff, "speed_diff")
return g
def split_graph_attributes(
G, attr="speed_diff", vmin=0, vmax=0, cmapsteps=8, cmap="viridis"
):
edges = ox.graph_to_gdfs(G, nodes=False)
vals = pd.Series(nx.get_edge_attributes(G, attr))
cmap = plt.cm.get_cmap(cmap).copy()
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap_discrete = mpl.colors.LinearSegmentedColormap.from_list(
"viridis_discrete", cmaplist, cmap.N
)
cmap_discrete.set_under("dimgrey")
cmap_discrete.set_over("lightgrey")
# define the bins and normalize
if vmin == 0 and vmax == 0:
bounds = np.ceil(np.linspace(min(vals), max(vals), cmapsteps))
else:
bounds = np.ceil(np.linspace(vmin, vmax, cmapsteps))
norm = mpl.colors.BoundaryNorm(bounds, cmap_discrete.N)
scalar_mapper = plt.cm.ScalarMappable(cmap=cmap_discrete, norm=norm)
ec = dict(vals.map(scalar_mapper.to_rgba))
nx.set_edge_attributes(G, ec, "ec")
# split graph according to attributes
# create generators
edg_2 = (
edge
for edge, hw in edges["highway"].items()
if hw == "motorway"
or hw == "trunk"
or hw == "primary"
or hw == "motorway_link"
or hw == "trunk_link"
or hw == "primary_link"
)
edg_1 = (
edge
for edge, hw in edges["highway"].items()
if hw == "secondary"
or hw == "secondary_link"
or hw == "tertiary"
or hw == "tertiary_link"
)
G2 = G.edge_subgraph(edg_2)
G1 = G.edge_subgraph(edg_1)
warnings.filterwarnings("ignore", category=DeprecationWarning)
fig, ax = ox.plot_graph(
G,
figsize=(20, 12),
edge_color=pd.Series(nx.get_edge_attributes(G, "ec")),
edge_linewidth=0.2,
node_size=0,
show=False,
)
ox.plot_graph(
G2,
ax=ax,
figsize=(20, 12),
edge_color=pd.Series(nx.get_edge_attributes(G2, "ec")),
edge_linewidth=3,
node_size=0,
show=False,
)
ox.plot_graph(
G1,
ax=ax,
figsize=(20, 12),
edge_color=pd.Series(nx.get_edge_attributes(G1, "ec")),
edge_linewidth=1,
node_size=0,
show=False,
)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="2%", pad=-3)
cb = fig.colorbar(scalar_mapper, cax=cax)
cb.set_label(r"$\Delta V_{i,j}$ [km/h]", color="w")
cb.ax.yaxis.set_tick_params(color="w")
plt.setp(plt.getp(cb.ax.axes, "yticklabels"), color="w")
cb.ax.tick_params(width=1.0)