Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback

TL;DR

This paper presents an AI-driven pipeline that accurately models traffic demand from noisy, multimodal detector data and stakeholder feedback, improving traffic simulation fidelity and generalizability across different municipalities.

Contribution

It introduces a novel end-to-end AI pipeline combining computer vision, optimization, and language models for traffic demand modeling from complex data sources.

Findings

Accurately captures traffic patterns in a real-world testbed.

Generalizes well to municipalities with varying data quality.

Enhances traffic simulation realism and stakeholder engagement.

Abstract

How can a traffic simulation be designed to faithfully reflect real-world traffic conditions? One crucial step is modeling the volume of traffic demand. But past demand modeling approaches have relied on unrealistic or suboptimal heuristics, and they have failed to adequately account for the effects of noisy and multimodal data on simulation outcomes. In this work, we integrate advances in AI to construct a three-step, end-to-end pipeline for systematically modeling traffic demand from detector data: computer vision for vehicle counting from noisy camera footage, combinatorial optimization for vehicle route generation from multimodal data, and large language models for iterative simulation refinement from natural language feedback. Using a road network from Strongsville, Ohio as a testbed, we show that our pipeline accurately captures the city's traffic patterns in a granular simulation. Beyond Strongsville, incorporating noise and multimodality makes our framework generalizable to municipalities with different levels of data and infrastructure availability.