Raw data can be shared among the partners of the project (GAMES) and must be placed in the following folders:
datasets/autoteldatasets/windkraft_simonsfelddatasets/mobility
Simulated mobility scenarios are available. For instance, the directory ./mobility/simulated contains the following files:
sim_reserv_df_in_zurich_1_day_from_userdemand.csv: Simulated reservations for one day in Zurich (generated using a pool of user demands for mobility).sim_presence_matrx_in_zurich_6_months_statistical_sampler.csv: Simulated presence matrix for six months in Zurich (generated with a statistical sampler).
- demo_traffic_model
# Defne macroscopic traffic model & street network for two geographical regions
TelAviv = Macroscopic_traffic_model(geographical_area = 'Tel Aviv, Israel')
Zurich = Macroscopic_traffic_model(geographical_area = 'Zurich, CH')
lon_min, lon_max, lat_min, lat_max = TelAviv.get_minmax_lon_lat() # get minimum and maximum latitudes, longitudes from the street graph
TelAviv.plot_graph_data(feature = 'speed_kph', data_threshold = 50) # in red are streets with speed >= 50 kph
#TelAviv.plot_graph_data(feature = 'travel_time', data_threshold = 60) # in red are streets with travel_time >= 60 seconds
#TelAviv.plot_graph_data(feature = 'length', data_threshold = 100) # in red are streets with length >= 20 meters
#TelAviv.plot_graph_data(feature = 'highway', data_threshold = 'residential') # in red are branches labelled as residential streets
df_edges = TelAviv.create_edge_dataframe() # get lat,lon start-end of the edges/streets
path = TelAviv.shortest_path_start_end(LO_LA_start= [34.79, 32.073], LO_LA_end = [34.791, 32.09] ) # get (if exist) the shortest path/route from start to end (latitude,longitude)
df_trips = pd.DataFrame([[1, 34.79, 32.073, 34.789, 32.093, 9],
[2, 34.789, 32.138, 34.7923, 32.1, 12]],
columns=['reservation_id', 'startLongitude', 'startLatitude',
'endLongitude', 'endLatitude', 'distance']) # example data set with two trips with only (ID,lat,lon,dis)
TelAviv.plot_trip_routes(df_trips, show=True, route_alpha=0.9)
routes, df_route_features = TelAviv.get_shortest_routes_and_features(df_trips) # routes= list of routes, df_route_features= data frame with features of the trip and shortest routes- demo_data_loader_pre_process
df_autotel = data_loader(data_dir='datasets/autotel', file_name='autotel_2021_2022.pkl')
df, df_sequence = preprocess_trip_data_frame(df,TelAviv.get_minmax_lon_lat())
results_daily = get_daily_profiles_data(df_sequence)
matrix_day = results_daily['matrix_daily_departures'] # a [n_days x 24] array contining the total number of departures for each day and hour in the data set 
- demo_zoning_analysis
# example of macroscopic zoning
n_disretized = [15, 15]
# grid partition of lat and lon.... n_disretized[0] x n_disretized[1] ....append zone lat,lon and indices to the dataframe
df_sequence_with_zones, linspace_lat, linspace_lon = define_zones(df_sequence, n_disretized_lat_lon=n_disretized)
# get statistics of idle times and trip durations on each zone
Idle_duration_zone_stats, Trips_duration_zone_stats = matrix_stats_idle_duration(df_sequence_with_zones)
# plot map (parking times)
plot_zone_duration_stats(Idle_duration_zone_stats['mean'], Idle_duration_zone_stats['std'], TelAviv, label1='Mean idle time', label2='STD idle time')
# plot inflows and outflows
plot_density_arrivals_departures_net_out_flows(Trips_duration_zone_stats['n_samples'],
Idle_duration_zone_stats['n_samples'], TelAviv)
- demo_train_total_mobility_demand_forecaster
Predict_net_number_of_departures.mp4
- demo_space_time_probabilistic_forecaster