quetzal.model.transportmodel module

class quetzal.model.transportmodel.TransportModel(*args, **kwargs)

Bases: quetzal.model.optimalmodel.OptimalModel, quetzal.model.parkridemodel.ParkRideModel

analysis_mode_utility(how='min', segment=None, segments=None, time_expanded=False)

• requires: mode_utility, los, utility_values
• builds: los

analysis_utility(segment='root', time_expanded=False)

• requires: mode_utility, los, utility_values
• builds: los

compute_los_volume(time_expanded=False, keep_segments=True)

initialize_logit()

segmented_assigment(road=False, boardings=False, alightings=False, transfers=False, aggregated_los=None)

segmented_car_assignment()

segmented_pt_assignment(split_by=None, on_road_links=False, *args, **kwargs)

Performs pt assignment for all demand segments. Requires computed path probabilities in pt_los for each segment.

step_assignment(road=False, boardings=False, boarding_links=False, alightings=False, alighting_links=False, transfers=False, segmented=False, time_expanded=False, compute_los_volume=True)

step_car_assignment(volume_column=None)

Assignment step

• requires: road_links, car_los, road_links, volumes, path_probabilities
• builds: loaded_road_links

step_concatenate_los()

• requires: pt_los, car_los
• builds: los

step_distribution(deterrence_matrix=None, **od_volume_from_zones_kwargs)

• requires: zones
• builds: volumes

Parameters:

• deterrence_matrix – an OD unstaked dataframe representing the disincentive to travel as distance/time/cost increases.
• od_volume_from_zones_kwargs –

  if the friction matrix is not provided, it will be automatically computed using a gravity distribution which uses the following parameters: * param power: (int) the gravity exponent * param intrazonal: (bool) set the intrazonal distance to 0 if False,

  compute a characteristic distance otherwise.

step_logit(time_expanded=False, decimals=None, n_paths_max=None, nchunks=10, workers=1, keep_od_tables=True)

• requires: mode_nests, logit_scales, los
• builds: los, od_utilities, od_probabilities, path_utilities, path_probabilities

step_modal_split(build_od_stack=True, **modal_split_kwargs)

• requires: volumes, los
• builds: od_stack, shared

Parameters:modal_split_kwargs – kwargs of engine.modal_split

example:

sm.step_modal_split(
    time_scale=1/1800,
    alpha_car=2,
    beta_car=600
)

step_pathfinder(walk_on_road=False, complete=True, **kwargs)

• requires: links, footpaths, zone_to_transit, zone_to_road
• builds: pt_los

step_pr_pathfinder(force=False, path_analysis=True, **kwargs)

step_pt_assignment(volume_column=None, on_road_links=False, split_by=None, **kwargs)

Assignment step

• requires: links, nodes, pt_los, road_links, volumes, path_probabilities
• builds: loaded_links, loaded_nodes, add load to road_links

Parameters:

• volume_column – volume column of self.volumes to assign. If none, all columns will be assigned
• on_road_links – if True, performs pt assignment on road_links as well
• split_by – path categories to be tracked in the assignment. Must be a column of self.pt_los

example:

sm.step_assignment(
        volume_column=None,
        on_road_links=False,
        split_by='route_type',
        boardings=True,
        alightings=True,
        transfers=True
    }
)

step_pt_pathfinder(broken_routes=True, broken_modes=True, route_column='route_id', mode_column='route_type', boarding_time=None, speedup=False, walk_on_road=False, keep_pathfinder=False, force=False, path_analysis=True, **kwargs)

• requires: zones, links, footpaths, zone_to_road, zone_to_transit
• builds: pt_los

step_road_pathfinder(maxiters=1, *args, **kwargs)

• requires: zones, road_links, zone_to_road
• builds: car_los, road_links

quetzal.model.transportmodel.get_combined_mode_utility(route_types, mode_utility, how='min')

quetzal.model.transportmodel.read_hdf(filepath)

quetzal.model.transportmodel.read_json(folder)