def get_multimodal(inData, params, request, plot=True):
o = request.origin
d = request.destination
params.GTFS.beta = 5
# A find transfer stop from ride to transit
# get two distances
leg1 = inData.skims.ride.loc[inData.transit_stops.node][
o] # from origin to all transit_stops via road network
leg2 = inData.skims.transit.loc[d][
inData.transit_stops.
node] / params.GTFS.beta # and from all transit_stop to destination with transit
multimodalRT = pd.concat(
[leg1, leg2],
axis=1) # merge them by the stop_point (two columns of travel times)
multimodalRT['time'] = multimodalRT.sum(axis=1) +\
params.GTFS.transfer_penalty # calculate sum of travel times plus transfer penalty
transfer_pointRT = multimodalRT.time.idxmin(
) # find stop of minimal total time - this is transfer point
# B find transfer stop from transit to ride
leg1 = inData.skims.transit.loc[inData.transit_stops.node][
o] / params.GTFS.beta # transit distances from origin to stop points
leg2 = inData.skims.ride.loc[d][
inData.transit_stops.
node] # ride distances from transit stops to destination
multimodalTR = pd.concat([leg1, leg2],
axis=1) # merge to two-clumn data frame
multimodalTR['time'] = multimodalTR.sum(
axis=1) + params.GTFS.transfer_penalty # calc total travel and penalty
transfer_pointTR = multimodalTR.time.idxmin(
) # find transfer point of minimal time
if plot:
import seaborn as sns
dists = DotMap()
G = inData.GTFS.G
palette = sns.color_palette("bright")
ev = [
0.001 if 'IVT' in edge[-1].keys() else 0.3
for edge in G.edges(data=True)
] # assign line weights
colors = [
'blue' if 'IVT' in edge[-1].keys() else 'grey'
for edge in G.edges(data=True)
] # assign color
#plot graph
fig, ax = ox.plot_graph(G,
fig_height=15,
fig_width=15,
node_size=0,
edge_linewidth=ev,
show=False,
close=False,
edge_color=colors)
# plot stops
ax.scatter(inData.transit_stops.x,
inData.transit_stops.y,
s=3,
c='black',
marker='x')
# plot O and D
ax.annotate(
'O',
(inData.nodes.loc[o].x * 1.0002, inData.nodes.loc[o].y * 1.00001))
ax.annotate(
'D',
(inData.nodes.loc[d].x * 1.0002, inData.nodes.loc[d].y * 1.00001))
# plot two transfer points
TRp = inData.transit_stops[inData.transit_stops.node ==
transfer_pointTR].squeeze()
ax.scatter(TRp.x, TRp.y, s=30, c='brown', marker='o')
ax.annotate('T->R', (TRp.x * 1.0002, TRp.y * 1.00001))
TRp = inData.transit_stops[inData.transit_stops.node ==
transfer_pointRT].squeeze()
ax.scatter(TRp.x, TRp.y, s=30, c='green', marker='o')
ax.annotate('R->T', (TRp.x * 1.0002, TRp.y * 1.00001))
route = nx.shortest_path(G, o, d, weight='TRANSIT_COST')
dists.transit = nx.shortest_path_length(
G, o, d, weight='TRANSIT_COST') / params.GTFS.beta
ax = add_route(G, ax, route, color='black', alpha=0.7, key='length')
route = nx.shortest_path(G, o, d, weight='length')
dists.ride = nx.shortest_path_length(
G, o, d, weight='length') / params.speeds.ride
ax = add_route(G, ax, route, color='blue', alpha=0.7, key='length')
route = nx.shortest_path(G, o, transfer_pointRT, weight='length')
ax = add_route(G, ax, route, color='green', alpha=0.7, key='length')
route = nx.shortest_path(G, transfer_pointRT, d, weight='TRANSIT_COST')
ax = add_route(G, ax, route, color='green', alpha=0.7, key='length')
dists.RT_R = nx.shortest_path_length(
G, o, transfer_pointRT, weight='length') / params.speeds.ride
dists.RT_T = nx.shortest_path_length(
G, transfer_pointRT, d, weight='TRANSIT_COST') / params.GTFS.beta
dists.RT = dists.RT_R + dists.RT_T
route = nx.shortest_path(G, o, transfer_pointTR, weight='TRANSIT_COST')
ax = add_route(G, ax, route, color='brown', alpha=0.7, key='length')
route = nx.shortest_path(G, transfer_pointTR, d, weight='length')
ax = add_route(G, ax, route, color='brown', alpha=0.7, key='length')
dists.TR_T = nx.shortest_path_length(
G, o, transfer_pointTR, weight='TRANSIT_COST') / params.GTFS.beta
dists.TR_R = nx.shortest_path_length(
G, transfer_pointTR, d, weight='length') / params.speeds.ride
dists.TR = dists.TR_R + dists.TR_T
return pd.Series(dists).to_frame().drop(['_typ', '_subtyp'])