基于历史拥堵图和合意日识别的交通拥堵和行程时间预测
Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days
摘要：
本文介绍了一种新的实用方法，用于实时估计高速公路上的交通状况和行程时间。首先，在主成分分析之后，对历史数据集的观测日进行聚类。两种不同的方法进行了比较：高斯混合模型和k-means算法。聚类结果表明，同一组的拥堵图在交通状况和动态上具有很大的相似性。这种地图是高速公路上拥堵传播的二进制可视化，更加重视交通动态。其次，根据拥堵地图，在每个聚类中确定一个共识日作为社区最具代表性的一天。第三，从历史数据中获得的这些信息用于预测交通拥堵传播和旅行时间。因此，新的一天的第一次测量被用来确定哪个合意的一天最接近这个新的一天。然后，使用在该合意日记录的过去观测来预测未来的交通状况和旅行时间。这种方法是测试使用10个月的数据收集在法国的高速公路上，并显示出非常令人鼓舞的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
