Reconstructing the Traffic State by Fusion of Heterogeneous Data

TL;DR

This paper introduces an advanced interpolation method that fuses stationary detector data with floating car data to accurately reconstruct detailed traffic profiles, capturing transitions, waves, and addressing data gaps.

Contribution

The paper presents a novel interpolation technique that integrates heterogeneous traffic data sources for detailed and robust traffic state reconstruction.

Findings

Accurately captures free and congested traffic transitions.

Effectively fills data gaps caused by detector failures.

Successfully fuses floating car data with stationary detector data.

Abstract

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.