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

Content Provider	IET Digital Library
Author	Ou, Jishun Yang, Shu Wu, Yao Jan An, Chengchuan Xia, Jingxin
Abstract	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.
Starting Page	826
Ending Page	837
Page Count	12
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 8, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0355
Journal	IET Intelligent Transport Systems
Publisher Date	2018-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Arizona Combinatorial Mathematics Data Handling Technique Day-to-day Analysis Decision Making Decision-making Process Freeway Corridors Graph Theory Graph-theory-based Clustering Algorithm Inspection Location-by-location Analysis Minibatch K-means Clustering Algorithm Noncongested Areas Pattern Clustering Phoenix Spatiotemporal Congested Areas STCA Systematic Clustering Method Traffic Authorities Traffic Congestion Management System Traffic Congestion Manual Inspection Traffic Engineering Computing Traffic Heatmaps Unsupervised Clustering Procedure
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

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