Spatiotemporal Clustering and Analysis of Road Accident Hotspots by Exploiting GIS Technology and Kernel Density Estimation. (5th May 2020)

• Record Type: Journal Article
• Authors: Kazmi, Syed Saqib Ali; Ahmed, Mehreen; Mumtaz, Rafia; Anwar, Zahid
• Editors: Murtagh, Fionn
• Is Part Of: Computer journal. Volume 65:Number 2(2022)
• Page Start: 155
• Page End: 176
• Publication Date: 2020-05-05
• Subjects: Data mining -- Spatiotemporal Analysis -- Accidents -- Kernel Density Estimation -- Geographical Information Systems -- Hotspots; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz158
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Traffic accidents are a common problem in any transportation network. Road traffic accidents are predicted to be the seventh leading cause of deaths by the year 2030. Recently research in the integration of geographical information systems (GIS) for analyzing accidents, road design and safety management has increased considerably. The perpetual use of GIS tools, lead this study to propose the identification of accident hotspots by exploiting GIS technology coupled with kernel density estimation (KDE). This paper proposes the use of KDE technique and GIS technology to automatically identify the accident hotspots using UK as the study area. Analysis shows that most of the accidents occur when there is a 30 mph speed limit, a weekend, in the evening time, during the months of October and November, on the single carriageway, where there is 'T' or staggered junction and on 'A' road class. Moreover, this study also proposed techniques to classify the accident severity that is classified as either fatal, serious or slight. The driver behavior and environmental features achieved an accuracy up to 85% on the severity classification with Bagging technique. Further, the shortcomings, limitations and recommendations for future work are also identified.