Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

行程时间的可靠性量化了行程时间的可变性，并已成为评估运输网络性能的关键方面。经验旅行时间累积分布函数（CDF）已用作保存关于旅行时间的可变性和分布的固有信息的工具。随着数据收集技术的进步，探测车辆数据已经常用于测量高速公路系统的性能。在处理大量探测车辆数据时，使用CDF面临的一个挑战是确定要完全表征经历的行驶时间需要多少个不同的CDF。本文探索了将行进时间的CDF聚类到细分数量的均质聚类的最佳数量的统计方法，这些聚类保留了所有相关的分布信息。测试了两种聚类方法，一个基于经典层次聚类，另一个使用基于模型的功能数据聚类，使用来自弗吉尼亚州64号州际公路的旅行时间数据来找出它们在聚类分布上的性能。高速公路路段和交汇处内的路段分别分组。为了找到合适的数据格式作为聚类输入，同时考虑了缩放时间和原始旅行时间。此外，还包括了一种基于几何特征的非数据驱动方法进行比较。结果表明，对于高速公路路段，使用行驶时间和Anderson-Darling不相似矩阵和Ward的联系进行聚类具有最佳性能。对于互换路段，基于模型的聚类提供了最佳的聚类。通过将段聚类为同质的组，