论文标题
基于历史拥塞地图和一致日期的确定，交通拥堵和旅行时间预测
Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days
论文作者
论文摘要
在本文中，引入了一种新的练习方法，用于实时估计高速公路上的交通状况和旅行时间。首先，经过主要组件分析，将历史数据集的观察日聚集。比较了两种不同的方法：高斯混合模型和K均值算法。聚类结果表明，同一组的日子的拥塞地图在其交通状况和动态上具有很大的相似性。这样的地图是高速公路上拥塞传播的二进制可视化，对交通动态的重视更为重要。其次，根据拥塞地图，在每个集群中确定了共识日为社区中最具代表性的日子。第三，从历史数据中获得的这些信息用于预测交通拥堵的传播和旅行时间。因此，新的一天的第一个测量方法用于确定最接近这一天的自愿日。然后，使用该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。使用法国高速公路上收集的十个月数据对此方法进行了测试，并显示出非常令人鼓舞的结果。
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.
