A Dynamic Choice Model with Heterogeneous Decision Rules: Application in Estimating the User Cost of Rail Crowding

TL;DR

This paper introduces a dynamic latent class model that captures how subway riders' decision rules evolve over time, accounting for inertia and learning, to better estimate the perceived cost of crowding.

Contribution

It develops a novel dynamic choice model with latent classes and learning behavior, improving crowding valuation accuracy in transit rider behavior analysis.

Findings

Riders follow compensatory decision rules only 25.5% of the time.

Estimated crowding valuation increases by 47% under crowded conditions.

Model demonstrates practical advantages in estimating user costs in transit systems.

Abstract

Crowding valuation of subway riders is an important input to various supply-side decisions of transit operators. The crowding cost perceived by a transit rider is generally estimated by capturing the trade-off that the rider makes between crowding and travel time while choosing a route. However, existing studies rely on static compensatory choice models and fail to account for inertia and the learning behaviour of riders. To address these challenges, we propose a new dynamic latent class model (DLCM) which (i) assigns riders to latent compensatory and inertia/habit classes based on different decision rules, (ii) enables transitions between these classes over time, and (iii) adopts instance-based learning theory to account for the learning behaviour of riders. We use the expectation-maximisation algorithm to estimate DLCM, and the most probable sequence of latent classes for each rider is retrieved using the Viterbi algorithm. The proposed DLCM can be applied in any choice context to capture the dynamics of decision rules used by a decision-maker. We demonstrate its practical advantages in estimating the crowding valuation of an Asian metro's riders. To calibrate the model, we recover the daily route preferences and in-vehicle crowding experiences of regular metro riders using a two-month-long smart card and vehicle location data. The results indicate that the average rider follows the compensatory rule on only 25.5% of route choice occasions. DLCM estimates also show an increase of 47% in metro riders' valuation of travel time under extremely crowded conditions relative to that under uncrowded conditions.