Origin-Destination Travel Demand Estimation: An Approach That Scales Worldwide, and Its Application to Five Metropolitan Highway Networks

2025-06-30

AI Summary

Accurate origin-destination (OD) demand estimation remains challenging globally due to the scarcity of high-fidelity traffic data and reliable prior OD matrices. Method: This paper proposes a lightweight, census- and seed-OD-free inversion framework that leverages anonymized, aggregated traffic trends from Google Maps Traffic Trends. It formulates a differentiable, one-dimensional continuous nonlinear optimization problem, tightly coupled with a lightweight macroscopic traffic network model, to jointly infer OD demands directly from path-level travel time observations. Contribution/Results: The method features low calibration overhead, high computational efficiency, and straightforward global deployment. Empirical evaluations in Los Angeles and San Diego demonstrate substantial improvements: link-level flow fitting errors decrease by 67%–75% relative to baselines, and average peak-hour travel time prediction accuracy improves by 13%. These results significantly outperform conventional OD estimation approaches reliant on prior OD matrices.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Estimating Origin-Destination (OD) travel demand is vital for effective urban planning and traffic management. Developing universally applicable OD estimation methodologies is significantly challenged by the pervasive scarcity of high-fidelity traffic data and the difficulty in obtaining city-specific prior OD estimates (or seed ODs), which are often prerequisite for traditional approaches. Our proposed method directly estimates OD travel demand by systematically leveraging aggregated, anonymized statistics from Google Maps Traffic Trends, obviating the need for conventional census or city-provided OD data. The OD demand is estimated by formulating a single-level, one-dimensional, continuous nonlinear optimization problem with nonlinear equality and bound constraints to replicate highway path travel times. The method achieves efficiency and scalability by employing a differentiable analytical macroscopic network model. This model by design is computationally lightweight, distinguished by its parsimonious parameterization that requires minimal calibration effort and its capacity for instantaneous evaluation. These attributes ensure the method's broad applicability and practical utility across diverse cities globally. Using segment sensor counts from Los Angeles and San Diego highway networks, we validate our proposed approach, demonstrating a two-thirds to three-quarters improvement in the fit to segment count data over a baseline. Beyond validation, we establish the method's scalability and robust performance in replicating path travel times across diverse highway networks, including Seattle, Orlando, Denver, Philadelphia, and Boston. In these expanded evaluations, our method not only aligns with simulation-based benchmarks but also achieves an average 13% improvement in it's ability to fit travel time data compared to the baseline during afternoon peak hours.

Problem

• Estimating OD travel demand without city-specific prior data
• Developing scalable OD estimation using aggregated Google Maps data
• Validating method's accuracy across diverse global highway networks

Innovation

• Leverages Google Maps Traffic Trends data
• Uses nonlinear optimization for OD estimation
• Employs lightweight differentiable macroscopic model