Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City

Authors

Daniel F. Villarraga
Ricardo A. Daziano

Document Type

Journal Article

Publication Date

2025

Subject Area

place - north america, place - urban, ridership - commuting, ridership - mode choice, ridership - modelling, economics - value of time

Keywords

Hierarchical Gaussian process, Nearest Neighbor Gaussian Process (NNGP), Spatially autoregressive models, Interpretable machine learning, Discrete choice, Travel mode choice, Value of Travel Time (VOTT)

Abstract

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor Gaussian Process (HNNGP) model to account for spatially correlated unobservables in discrete choice analysis. Gaussian Processes (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of-sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part B Home Page:

http://www.sciencedirect.com/science/journal/01912615

Recommended Citation

Villarraga, D. F., & Daziano, R. A. (2025). Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City. Transportation Research Part B: Methodological, 191, 103132.