Estimating a k-modal nonparametric mixed logit model with market-level data

We propose a group-level agent-based mixed (GLAM) logit model that is estimated using market-level choice share data. The model non-parametrically represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric taste heterogeneity. A case study of mode choice in New York State is conducted using synthetic population data of 53.55 million trips made by 19.53 million residents in 2019. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets/agents. We benchmark in-sample and out-of-sample predictive performance of the GLAM logit model against multinomial logit, nested logit, inverse product differentiation logit, and random coefficient logit (RCL) models. The results show that GLAM logit outperforms benchmark models, improving the overall in-sample predictive accuracy from 78.7% to 96.71% and out-of-sample accuracy from 65.30% to 81.78%. The price elasticities and diversion ratios retrieved from GLAM logit and benchmark models exhibit similar substitution patterns among the six travel modes. GLAM logit is scalable and computationally efficient, taking less than one-tenth of the time taken to estimate the RCL model. The agent-specific parameters in GLAM logit provide additional insights such as value-of-time (VOT) across segments and regions, which has been further utilized to demonstrate its application in analyzing NYS travelers' mode choice response to the congestion pricing. The agent-specific parameters in GLAM logit facilitate their seamless integration into supply-side optimization models for revenue management and system design.