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.

本文介绍了一种用于公路交通状况和行进时间的实时估计的新的，随时可用的方法。首先，在进行主成分分析之后，将历史数据集的观察天聚类。比较了两种不同的方法：高斯混合模型和k-means算法。聚类结果表明，同一组日子的拥堵图在交通状况和动态方面具有实质性相似性。这样的地图是高速公路拥堵传播的二进制可视化，对交通动态更加重视。其次，根据拥塞图，在每个群集中将共识日确定为社区中最具代表性的一天。第三，从历史数据中获得的信息可用于预测交通拥堵的传播和行驶时间。因此，使用新一天的首次测量来确定哪个共识日期最接近该新一天。然后，将在同意日期记录的过去观察结果用于预测未来的交通状况和旅行时间。使用在法国高速公路上收集的十个月数据对这种方法进行了测试，结果令人鼓舞。