Clustering of urban traffic patterns is an essential task in many different areas of traffic management and planning. In this paper, two significant applications in the clustering of urban traffic patterns are described. The first application estimates the missing speed values using the speed of road segments with similar traffic patterns to colorify map tiles. The second one is the estimation of essential road segments for generating addresses for a local point on the map, using the similarity patterns of different road segments. The speed time series extracts the traffic pattern in different road segments. In this paper, we proposed the time series clustering algorithm based on K-Means and Dynamic Time Warping. The case study of our proposed algorithm is based on the Snapp application's driver speed time series data. The results of the two applications illustrate that the proposed method can extract similar urban traffic patterns.

翻译：城市交通模式聚类是交通管理与规划众多领域中的一项基础任务。本文阐述了城市交通模式聚类中的两个重要应用：第一个应用利用具有相似交通模式的道路路段速度值来估算缺失速度，从而对地图瓦片进行着色；第二个应用则基于不同道路路段的相似模式，为地图上的局部点生成地址，并估算关键道路路段。速度时间序列提取了不同道路路段的交通模式。本文提出了一种基于K-Means和动态时间规整的时间序列聚类算法。案例研究基于Snapp应用程序的驾驶员速度时间序列数据。两个应用的结果表明，所提方法能够有效提取相似的城市交通模式。