Online Bayesian Inference for a Dirichlet Process Mixture of Binary Logit Models

Abstract

Flexible travel behaviour models for preference heterogeneity have largely remained within the batch-estimation paradigm. The growing availability of continuously collected behavioural data, for example from smartphone applications, highlights the need for methods that update posterior inference on such heterogeneity as new observations arrive. This study proposes an online Bayesian inference method for a Dirichlet process mixture of binary logit models. The main challenge is to update the posterior as observations arrive while allowing the latent cluster structure to expand and historical cluster assignments to be revised. We develop a sequential Monte Carlo sampler using a sequence of Pólya--Gamma-augmented partial-data posteriors, local particle-weight corrections, and recursively maintained sufficient statistics. Each observation's contribution can be added, removed, or replaced in these summaries, supporting both sequential coefficient updating and rejuvenation that revisits historical cluster assignments. The simulation study shows that observation-by-observation sequential updating yields final posterior summaries close to those obtained by batch Markov chain Monte Carlo. Sensitivity analyses further demonstrate trade-offs between posterior approximation accuracy and computational cost as the number of particles and rejuvenation settings are varied. An illustrative empirical application shows how the method tracks posterior evolution and produces observation-specific provisional posterior summaries with low latency as real-world trip records are processed sequentially. The proposed approach provides a methodological foundation for online inference in travel behaviour models with parameter heterogeneity and for future extensions to richer choice structures, low-latency decision support, and online anomaly screening.