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add_households()
Add households to particular Census tracts in the region
calibrate_trip_distance_beta()
This calibrates the trip distance beta, using the method described in Merlin (2020) A new method using medians to calibrate single-parameter spatial interaction models, JTLU. The basic idea is that we adjust the beta for the decay function until half the distance-weighted accessibility occurs in the median travel distance. For NHB, productions and attractions will be the same
calibrate_trip_distance_betas()
Calibrate betas for all trip types median_distances should be a list with element HBW, HBO, and NHB with median crow-flies trip distances in each.
estimate()
Estimate a four step model for later use, based on 2017 NHTS data and PSRC household survey data (for distribution functions).
estimate_vmt()
Calculates VMT based on flows.
get_mode_shares()
This function uses the output of mode_choice()
load_landuse_scenario()
Load a land use scenario in Excel format
load_model()
Load a model
load_model_v0()
Load a model in model format 0 (used with pre-2026 releases)
load_nhts()
Load 2017 NHTS data, handling types appropriately
map_congestion()
Produce a congestion map
map_trip_distribution()
Map trip distribution from a single trip.
map_trip_generation()
Map trip generation.
mode_choice()
This runs the mode choice step of the model, and returns flows differentiated by mode for each trip type and time of day.
modify_ways()
Modify network attributes of existing OSM ways. Note that this currently only updates them for modeling; visualizations and GIS exports are not changed.
network_assignment()
This runs the network assignment step of the model.
network_to_gis()
Convenience function to export a network scenario to a GIS file
save_landuse_scenario()
Save a land use scenario in Excel format
save_model()
Save a model
trip_distribution()
This runs the trip distribution step of the model
trip_generation()
This runs the trip generation step of the model.
write_lm()
Write the minimal information to be able to reconstruct enough of an lm to be able to do prediction.
write_mnl()
Write just enough of a multinomial logit model that we can deserialize and apply it