Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering. (21st January 2019)

• Record Type: Journal Article
• Authors: Shen, Ling; Lu, Jian; Long, Man; Chen, Tingjun
• Other Names: Gasparetto Alessandro Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-01-21
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/2151284
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K-MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K-means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report to analyze recognition results. The proposed methodology will be illustrated by accident data in Chinese rural area in 2017. A grid-based partition was carried on by using intersection as a basic spatial unit. Appended hazard scores were then added to the segments and using K-means clustering, a result of similar hotspots was completed. The accuracy of the results is verified by the analysis of the cause extracted by Fuzzy C-means algorithm (FCM).