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

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