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

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.

Automated summary

This paper introduces a new method for real-time estimation of traffic conditions and travel times on highways by utilizing principal component analysis and clustering of historical dataset. The clustering results show similarity in traffic conditions and dynamics of days in the same group, and a consensual day is identified as the most representative day of each cluster. This method uses past observations to predict future traffic conditions and travel times based on the closest consensual day to a new day, showing promising results on a French freeway dataset.