RT Journal Article
A1 Jishun Ou
A1 Shu Yang
A1 Yao-Jan Wu
A1 Chengchuan An
A1 Jingxin Xia

PB iet
T1 Systematic clustering method to identify and characterise spatiotemporal congestion on freeway corridors
JN IET Intelligent Transport Systems
VO 12
IS 8
SP 826
OP 837
AB 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.
K1 unsupervised clustering procedure
K1 traffic congestion management systems
K1 traffic authorities
K1 systematic clustering method
K1 day-to-day analysis
K1 Arizona
K1 graph-theory-based clustering algorithm
K1 freeway corridors
K1 noncongested areas
K1 decision-making process
K1 Phoenix
K1 traffic heatmaps
K1 minibatch k-means clustering algorithm
K1 spatiotemporal congested areas
K1 STCA
K1 traffic congestion manual inspection
K1 location-by-location analysis
DO https://doi.org/10.1049/iet-its.2017.0355
UL https://digital-library.theiet.org/;jsessionid=uz6gptgs40ov.x-iet-live-01content/journals/10.1049/iet-its.2017.0355
LA English
SN 1751-956X
YR 2018
OL EN