  Estimating agent-specific taste heterogeneity with a large information and
communication technology (ICT) dataset requires both model flexibility and
computational efficiency. We propose a group-level agent-based mixed (GLAM)
logit approach that is estimated with inverse optimization (IO) and group-level
market share. The model is theoretically consistent with the RUM model
framework, while the estimation method is a nonparametric approach that fits to
market-level datasets, which overcomes the limitations of existing approaches.
A case study of New York statewide travel mode choice is conducted with a
synthetic population dataset provided by Replica Inc., which contains mode
choices of 19.53 million residents on two typical weekdays, one in Fall 2019
and another in Fall 2021. Individual mode choices are grouped into market-level
market shares per census block-group OD pair and four population segments,
resulting in 120,740 group-level agents. We calibrate the GLAM logit model with
the 2019 dataset and compare to several benchmark models: mixed logit (MXL),
conditional mixed logit (CMXL), and individual parameter logit (IPL). The
results show that empirical taste distribution estimated by GLAM logit can be
either unimodal or multimodal, which is infeasible for MXL/CMXL and hard to
fulfill in IPL. The GLAM logit model outperforms benchmark models on the 2021
dataset, improving the overall accuracy from 82.35% to 89.04% and improving the
pseudo R-square from 0.4165 to 0.5788. Moreover, the value-of-time (VOT) and
mode preferences retrieved from GLAM logit aligns with our empirical knowledge
(e.g., VOT of NotLowIncome population in NYC is $28.05/hour; public transit and
walking is preferred in NYC). The agent-specific taste parameters are essential
for the policymaking of statewide transportation projects.
