Cluster analysis of day-to-day traffic data in networks
Cluster analysis of day-to-day traffic data in networks
复制标题
网络中日常流量数据的聚类分析
DOI：
10.1016/j.trc.2022.103882
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Qian, Sean
中科院分区：
文献类型：
--
作者：
Zhang, Pengji;Ma, Wei;Qian, Sean
日常交通数据已广泛应用于交通规划和管理。然而，随着新技术的出现，许多模型所依赖的一个传统假设，即网络上的所有日常观察都遵循一个单一的模式，似乎是值得怀疑的。为了更好地理解网络流模式及其各自的相似性，聚类分析将日常数据划分为组是一种有效的解决方案，但由于忽略了交通网络特性，直接应用通用聚类算法可能并不总是合适的识别和解释日常模式的变化。鉴于这一实际问题，我们提出了一种新的聚类方法，集成网络流模型，即统计交通分配模型和概率OD出行需求估计模型，到通用聚类算法。它本质上是通过将交通数据的概率特征投影到OD需求的维度上来考察交通数据的概率特征。因此，它可以处理某些日期和位置的观测可能丢失或观测位置可能每天变化的交通数据。该算法嵌入了交通网络的领域知识，并在两个玩具网络和一个真实世界的网络上进行了测试。数值实验表明，新的聚类算法可以有效地识别和解释模式，很难看到的通用聚类算法，否则，即使与缺失值或随时间变化的传感位置。
Day-to-day traffic data has been widely used in transportation planning and management. However, with the emerging of new technologies, one conventional assumption, on which many models rely, that all the day-to-day observations on the network follow a single pattern appears to be questionable. To better understand network flow patterns and their respective similarities, cluster analysis that partitions the day-to-day data into groups is an effective solution, but directly applying generic clustering algorithms may not always be appropriate identifying and interpreting day-to-day pattern changes due to the ignorance of the transportation network characteristics. In view of this practical issue, we propose a new clustering method that integrates network flow models, namely a statistical traffic assignment model and a probabilistic OD travel demand estimation model, into generic clustering algorithms. It essentially examines the probabilistic characteristics of traffic data by projecting those onto the dimensions of OD demands. For this reason, it can deal with traffic data where observations on some days and locations may be missing, or observing locations may change from day to day. The proposed algorithm embeds the domain knowledge of the transportation network, and is tested on two toy networks and one real-world network. Numerical experiments show the new clustering algorithm can effectively identify and interpret patterns that are hard to see by generic clustering algorithms otherwise, even with missing values or day-varying sensing locations.