Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days

TL;DR

This paper introduces a practical method for real-time traffic and travel time prediction using historical congestion maps, clustering similar days, and identifying representative 'consensual days' to improve forecast accuracy.

Contribution

The study presents a novel approach combining clustering of congestion maps and consensus day identification for enhanced traffic prediction accuracy.

Findings

Clustering reveals similar traffic dynamics across days.

Consensus days effectively represent typical traffic conditions.

Method shows promising results on real French freeway data.

Abstract

In this paper, a new practice-ready method for the real-time estimation of traffic conditions and travel times on highways is introduced. First, after a principal component analysis, observation days of a historical dataset are clustered. Two different methods are compared: a Gaussian Mixture Model and a k-means algorithm. The clustering results reveal that congestion maps of days of the same group have substantial similarity in their traffic conditions and dynamic. Such a map is a binary visualization of the congestion propagation on the freeway, giving more importance to the traffic dynamics. Second, a consensus day is identified in each cluster as the most representative day of the community according to the congestion maps. Third, this information obtained from the historical data is used to predict traffic congestion propagation and travel times. Thus, the first measurements of a new day are used to determine which consensual day is the closest to this new day. The past observations recorded for that consensual day are then used to predict future traffic conditions and travel times. This method is tested using ten months of data collected on a French freeway and shows very encouraging results.