Estimating Traffic Conditions At Metropolitan Scale Using Traffic Flow Theory

TL;DR

This paper introduces a novel framework that leverages traffic flow theory and GPS data to accurately estimate metropolitan traffic conditions, significantly outperforming existing methods through extensive real-world testing.

Contribution

The authors develop a new iterative and bilevel optimization framework that improves traffic condition estimation at large scales using GPS traces and traffic flow theory.

Findings

Achieved up to 96.57% relative improvement over state-of-the-art methods

Successfully applied to large-scale networks of San Francisco and Beijing

Demonstrated effectiveness with over 26 million GPS traces

Abstract

The rapid urbanization and increasing traffic have serious social, economic, and environmental impact on metropolitan areas worldwide. It is of a great importance to understand the complex interplay of road networks and traffic conditions. The authors propose a novel framework to estimate traffic conditions at the metropolitan scale using GPS traces. Their approach begins with an initial estimation of network travel times by solving a convex optimization program based on traffic flow theory. Then, they iteratively refine the estimated network travel times and vehicle traversed paths. Last, the authors perform a bilevel optimization process to estimate traffic conditions on road segments that are not covered by GPS data. The evaluation and comparison of the authors' approach over two state-of-the-art methods show up to 96.57% relative improvements. The authors have further conducted field tests by coupling road networks of San Francisco and Beijing with real-world GIS data, which involve 128,701 nodes, 148,899 road segments, and over 26 million GPS traces.