Liao, Xishun (University of California, Los Angeles), Ma, Haoxuan (University of California, Los Angeles), Liu, Yifan (University of California, Los Angeles), Wei, Yuxiang (UCLA), He, Brian Yueshuai (University of Louisville), Stanford, Chris (Novateur), Ma, Jiaqi (University of California, Los Angeles)

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

Scheduled for presentation during the Regular Session "S01c-Data-Driven Simulation and Modeling for Smart Mobility Systems" (WE-LA-T1), Wednesday, November 19, 2025, 16:20−16:40, Southport 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

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.