Analytical Optimized Traffic Flow Recovery for Large-scale Urban Transportation Network

TL;DR

This paper presents an analytical optimization method that combines GPS and sparse traffic data to accurately recover comprehensive traffic flow in large urban networks, reducing the need for extensive sensor deployment.

Contribution

It introduces a novel constrained optimization framework with regularization and relaxation techniques for effective traffic flow recovery using limited data sources.

Findings

Achieves low estimation errors in large-scale urban networks.

Validated using SUMO simulation in Shenzhen's Futian District.

Demonstrates effectiveness with sparse sensor data.

Abstract

The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high costs associated with installation and maintenance result in sparse traffic data coverage. To obtain complete, accurate, and high-resolution network-wide traffic flow data, this study introduces the Analytical Optimized Recovery (AOR) approach that leverages abundant GPS speed data alongside sparse flow data to estimate traffic flow in large-scale urban networks. The method formulates a constrained optimization framework that utilizes a quadratic objective function with l2 norm regularization terms to address the traffic flow recovery problem effectively and incorporates a Lagrangian relaxation technique to maintain non-negativity constraints. The effectiveness of this approach was validated in a large urban network in Shenzhen's Futian District using the Simulation of Urban MObility (SUMO) platform. Analytical results indicate that the method achieves low estimation errors, affirming its suitability for comprehensive traffic analysis in urban settings with limited sensor deployment.