Investigating and modeling day-to-day route choices based on laboratory experiments. Part II: A route-dependent attraction-based stochastic process model

TL;DR

This paper introduces a novel stochastic process model for day-to-day route choices that accurately captures random oscillations observed in laboratory experiments, advancing understanding of traffic dynamics.

Contribution

It presents the first stochastic process model for DTD route choices, explaining random oscillations and providing a practical tool for transportation policy analysis.

Findings

The RDAB-SP model reproduces experimental flow oscillations accurately.

Random route switching is driven by simple, probability-based rules.

An efficient approximation model facilitates simulation and policy evaluation.

Abstract

To explain day-to-day (DTD) route-choice behaviors and traffic dynamics observed in a series of lab experiments, Part I of this research proposed a discrete choice-based analytical dynamic model (Qi et al., 2023). Although the deterministic model could well reproduce the experimental observations, it converges to a stable equilibrium of route flow while the observed DTD evolution is apparently with random oscillations. To overcome the limitation, the paper proposes a route-dependent attraction-based stochastic process (RDAB-SP) model based on the same behavioral assumptions in Part I of this research. Through careful comparison between the model-based estimation and experimental observations, it is demonstrated that the proposed RDAB-SP model can accurately reproduce the random oscillations both in terms of flow switching and route flow evolution. To the best of our knowledge, this is the first attempt to explain and model experimental observations by using stochastic process DTD models, and it is interesting to find that the seemingly unanticipated phenomena (i.e., random route switching behavior) is actually dominated by simple rules, i.e., independent and probability-based route-choice behavior. Finally, an approximated model is developed to help simulate the stochastic process and evaluate the equilibrium distribution in a simple and efficient manner, making the proposed model a useful and practical tool in transportation policy design.