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

Nonparametric estimation of k-modal taste heterogeneity for group level agent-based mixed logit