Systematic clustering method to identify and characterise spatiotemporal congestion on freeway corridors

Many analytical procedures, technical methods, and tools have been developed to facilitate manual inspection of traffic congestion and support the decision-making process for traffic authorities. However, lacking an automatic mechanism, it would be a time-consuming and labour-intensive process for day-to-day and location-by-location analyses. This study presents a method based on a three-stage framework that is capable of automatically identifying and characterising spatiotemporal congested areas (STCAs) by parsing, extracting, analysing and quantifying the knowledge contained in traffic heatmaps. The key components of the proposed method are two unsupervised clustering procedures: (i) a mini-batch k-means clustering algorithm to separate the congested and non-congested areas and (ii) a graph-theory-based clustering algorithm to distinguish between different STCAs. Twenty weekdays of dual loop detector data collected from a 26-mile stretch of Interstate 10 in Phoenix, Arizona was analysed for the case study. The new method identified and quantified 102 STCAs without the need for human intervention. Based on 14 traffic measures calculated for each STCA, 19 active bottlenecks along the study corridor were identified. Top-ranked bottlenecks identified in this study were consistent with those reported in previous studies but were produced with less effort, demonstrating the new method's potential utility for traffic congestion management systems.