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This paper introduces a fully generative, data-driven framework for travel demand modeling that simulates household-coordinated daily activity patterns based on socio-demographic profiles. The framework integrates population synthesis, activity generation, location assignment, and traffic simulation. Validated on a 10 million population in Los Angeles, the model achieves high fidelity in replicating real-world mobility patterns, matching the performance of legacy activity-based models (ABMs) while significantly reducing modeling costs and improving scalability, as evidenced by metrics like cosine similarity of 0.97 for origin-destination matrices and low Jensen-Shannon Divergence for VMT.
Forget costly, inflexible legacy travel demand models: a new generative framework matches their accuracy while slashing development time and boosting scalability.
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.
