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

TL;DR

This paper introduces a scalable, data-driven generative framework for household activity coordination in travel demand modeling, achieving high accuracy in replicating real-world mobility patterns with reduced costs.

Contribution

It presents a fully generative, transferable, and scalable travel demand modeling system integrating population synthesis, activity generation, and traffic simulation, validated in Los Angeles.

Findings

High similarity in origin-destination matrices (cosine similarity 0.97)

Accurate vehicle miles traveled estimation (9.8% MAPE)

Precise corridor-level traffic speed and volume predictions (6.11% MAPE)

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.