Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering

Ling Shen, Jian Lu, Man Long and Tingjun Chen

Mathematical Problems in Engineering, 2019, vol. 2019, 1-12

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).

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2019/2151284.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2019/2151284.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2151284

DOI: 10.1155/2019/2151284

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().