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

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  • Ling Shen
  • Jian Lu
  • Man Long
  • Tingjun Chen

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).

Suggested Citation

  • Ling Shen & Jian Lu & Man Long & Tingjun Chen, 2019. "Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, January.
    • Handle: RePEc:hin:jnlmpe:2151284
    DOI: 10.1155/2019/2151284
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    Cited by:

    1. Rishuang Sun & Chi Zhang & Yujie Xiang & Lei Hou & Bo Wang, 2022. "Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    2. Wen Wen & Wenhui Zhang, 2022. "Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    3. Artur I. Petrov & Victor I. Kolesov & Daria A. Petrova, 2021. "Theory and Practice of Quantitative Assessment of System Harmonicity: Case of Road Safety in Russia before and during the COVID-19 Epidemic," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    4. Jibiao Zhou & Xinhua Mao & Yiting Wang & Minjie Zhang & Sheng Dong, 2019. "Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China," IJERPH, MDPI, vol. 16(16), pages 1-28, August.

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