Joint Estimation of Dynamic O-D Demand and Choice Models for Dynamic Multi-modal Networks: Computational Graph-Based Learning and Hypothesis Tests

TL;DR

This paper introduces a scalable, joint estimation framework for dynamic multi-modal travel demand and choice modeling using system-level data, integrating a computational graph approach and hypothesis testing for large-scale networks.

Contribution

It develops a novel joint estimation method for dynamic OD demand and disutility functions in multi-modal systems, incorporating multiple data sources and a hypothesis testing framework.

Findings

Effective inference of time-varying travel demand and choices.

Scalable computational graph-based solution for large networks.

Statistically rigorous assessment of behavioral parameters.

Abstract

Understanding travel demand and behavior, particularly route and mode choices, is critical for effective transportation planning and policy design in multi-modal systems with emerging mobility options. Multi-modal system-level data, such as traffic counts, probe speeds, and transit ridership, offer scalable, cost-effective, and privacy-preserving advantages for inferring and analyzing travel behavior. This research uses such system-level data to infer travel demand and choices that vary by time of day, origin/destination location, and mode. Existing studies focus on a single transportation mode, consider limited behavioral factors in disutility functions, rely on static travel time functions, and face computational challenges when applied to large-scale networks. This research addresses these gaps by proposing a joint estimation framework for dynamic origin-destination demand and disutility functions within a multi-modal transportation system that includes both private driving and public transit, using multi-source system-level data. A multi-modal dynamic traffic assignment model that accounts for both route and mode choices is integrated into the framework, with detailed travel time modeling for multiple modes. Alternative-specific and zone-specific factors are incorporated into generic disutility functions to capture heterogeneous traveler perceptions. The estimation problem is formulated and solved using a computational graph-based approach, enabling dynamic network modeling and scalable inference across large-scale networks and diverse data sets. Furthermore, we propose a hypothesis testing framework tailored to this complex estimation setting to assess the statistical significance of behavioral parameters, thereby enabling model selection and statistically rigorous insights for real-world applications.