DOI: 10.5281/zenodo.13379434
title: Parameters and predicted probabilities of mode choice and ride pass subscription for microtransit in Arlington, TX

description: We provide parameters and predicted probabilities of mode choice (at trip level) and ride pass subscription (at individual level) for microtransit in Arlington, TX. The parameters are estimated by an agent-based, nested behavioral model that is developed in the C2SMARTER project “Multi-modal Tripchain Planner for Disadvantaged Travelers to Incentivize Transit Usage” (Award #69A3551747124). We separate the choice related to microtransit into two parts: travel mode choice and ride pass subscription choice. Synthetic population data from Replica Inc. and microtransit service data from City of Arlington are used for estimation. 

In the lower-branch travel mode choice, individuals decide on mode to use by considering factors such as travel time, cost, trip purpose, tour type, and mode-specific preferences. The mode choice set includes driving, walking, biking, carpool, and microtransit. Different from traditional mode choice models, we allow time and cost parameters to vary across individuals, and we allow mode specific constants to vary across trip OD pairs. This makes sense when we do not have sufficient data for socioeconomic attributes and built environment variables. The assumption we made here is that the impacts of these unobserved variables are included in the nonparametric distribution of individual and OD pair-level parameters.

In the upper-branch ride pass subscription choice, individuals decide whether to purchase a weekly ride pass, a monthly ride pass, or no ride pass at all. By subscribing to a ride pass, travelers pay an amount of money in advance and enjoy free microtransit trips until the ride pass expires. The utility of purchasing a ride pass consists of four components: (1) the utility related to the prices of ride passes, (2) the utility related to the change in consumer surplus (or compensating variation) brought by free microtransit trips with a ride pass, (3) the utility specific to microtransit users, and (4) the alternative specific constant of the ride pass. Given the data availability, we consider the ride pass model as a simple MNL model with six parameters to calibrate. These ride pass parameters are calibrated using the Nelder-Mead Simplex Method. The cost function to minimize is the squared distance between the predicted ride pass market share and the observed one.

Accordingly, this dataset consists of six .csv files:



Mode_choice_parameters_weekday.csv

Mode_choice_parameters_weekend.csv

RidePass_subscription_parameters.csv

Mode_choice_probability_weekday.csv

Mode_choice_probability_weekend.csv

Ridepass_subscription_probability.csv


 

Field definition in "Mode_choice_parameters_weekday.csv" and "Mode_choice_parameters_weekend.csv"




Field Name
Description


iid
IDs of each synthetic individual


trip_id
IDs of each synthetic trip


origin_bgrp
FIPs code of the block group where the trip starts


destination_bgrp
FIPs code of the block group where the trip ends


B_AUTO_TT
The parameter of auto travel time (vary across individuals)


B_MICRO_TT
The parameter of microtransit waiting time (vary across individuals)


B_NON_AUTO_TT
The parameter of non-auto travel time (vary across individuals)


B_COST
The parameter of travel cost (vary across individuals)


ASC_MIRCO
The alternative specific constant of microtransit (vary across trip OD pairs)


ASC_DRIVING
The alternative specific constant of driving (vary across trip OD pairs)


ASC_BIKING
The alternative specific constant of biking (vary across trip OD pairs)


ASC_WALKING
The alternative specific constant of walking (vary across trip OD pairs)


MICRO_P_SHOPPING
The interaction effect between microtransit and shopping trip purpose (generic)


MICRO_P_SCHOOL
The interaction effect between microtransit and school trip purpose (generic)


MICRO_P_OTHER
The interaction effect between microtransit and other trip purpose (generic)


MICRO_T_COMMUTE
The interaction effect between microtransit and commute tour type (generic)


MICRO_T_HOME_BASED
The interaction effect between microtransit and home-based tour type (generic)




 

Field definition in "RidePass_subscription_parameters.csv"




Field Name
Description


B_COST_RP
A transfer factor from trip fare to ride pass price


B_CS_WEEKDAY
The parameter of increased consumer surplus (due to the ride pass) on weekday


B_CS_WEEKEND
The parameter of increased consumer surplus (due to the ride pass) on weekend


B_MICRO_USER
The parameter of a binary variable indicating former microtransit users


ASC_WRP
The alternative specific constant of subscribing weekly ride pass


ASC_MRP
The alternative specific constant of subscribing monthly ride pass




 

Field definition in "Mode_choice_probability_weekday.csv" and "Mode_choice_probability_weekend.csv"




Field Name
Description


iid
IDs of each synthetic individual


trip_id
IDs of each synthetic trip


origin_bgrp
FIPs code of the block group where the trip starts


destination_bgrp
FIPs code of the block group where the trip ends


P_biking
The predicted probability of choosing biking


P_carpool
The predicted probability of choosing carpool


P_microtransit
The predicted probability of choosing microtransit


P_driving
The predicted probability of choosing driving


P_walking
The predicted probability of choosing walking




 

Field definition in "Ridepass_subscription_probability.csv"




Field Name
Description


iid
IDs of each synthetic individual


Micro_user
A binary variable indicating whether the individual used microtransit before


Population_seg
Segment ID of the synthetic individual


BLOCKGROUP
FIPs code of the home block group


P_weekly_pass
The predicted probability of subscribing weekly ride pass


P_monthly_pass
The predicted probability of subscribing monthly ride pass


P_None
The predicted probability of subscribing no ride pass