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

TL;DR

This paper introduces a scalable, data-driven method for estimating urban travel demand using aggregated Google Maps data, eliminating the need for traditional city-specific OD data, and demonstrates its effectiveness across multiple U.S. metropolitan highway networks.

Contribution

The paper presents a novel, efficient OD estimation approach that leverages publicly available traffic data and a lightweight macroscopic model, enabling broad applicability without extensive calibration.

Findings

Achieves 66-75% better fit to segment count data than baseline.

Demonstrates robust performance across diverse U.S. highway networks.

Improves travel time data fitting by 13% during peak hours.

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.