Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination

• URL: http://arxiv.org/abs/2507.08871v1
• Date: Wed, 09 Jul 2025 18:06:36 GMT
• Title: Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination
• Authors: Xishun Liao, Haoxuan Ma, Yifan Liu, Yuxiang Wei, Brian Yueshuai He, Chris Stanford, Jiaqi Ma,
• Abstract summary: This paper presents a learning-based travel demand modeling framework.<n>It synthesizes household-coordinated daily activity patterns based on a household's socio-demographic profiles.<n>It is fully generative, data-driven, scalable, and transferable to other regions.
• Score: 12.533065786338863
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Travel demand models are critical tools for planning, policy, and mobility system design. Traditional activity-based models (ABMs), although grounded in behavioral theories, often rely on simplified rules and assumptions, and are costly to develop and difficult to adapt across different regions. This paper presents a learning-based travel demand modeling framework that synthesizes household-coordinated daily activity patterns based on a household's socio-demographic profiles. The whole framework integrates population synthesis, coordinated activity generation, location assignment, and large-scale microscopic traffic simulation into a unified system. It is fully generative, data-driven, scalable, and transferable to other regions. A full-pipeline implementation is conducted in Los Angeles with a 10 million population. Comprehensive validation shows that the model closely replicates real-world mobility patterns and matches the performance of legacy ABMs with significantly reduced modeling cost and greater scalability. With respect to the SCAG ABM benchmark, the origin-destination matrix achieves a cosine similarity of 0.97, and the daily vehicle miles traveled (VMT) in the network yields a 0.006 Jensen-Shannon Divergence (JSD) and a 9.8% mean absolute percentage error (MAPE). When compared to real-world observations from Caltrans PeMS, the evaluation on corridor-level traffic speed and volume reaches a 0.001 JSD and a 6.11% MAPE.

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