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

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).

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

“…Tsai et al [38] showed how clustering can be used to identify logical, but hard to model, groupings of the data. Applications of clustering include, but are not limited, to: (a) traffic categorization [38,94,95], (b) identifying accident clusters [96][97][98], and (c) grouping of weather conditions [99]. To demonstrate how an optimal number of clusters (k * ) can be obtained, we provide a detailed example in the Supplementary Materials where we use k−means clustering and the elbow method to determine the k * clusters for traffic data.…”

Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China

“…As for the quantitative risk assessment methods, they can reflect the risk levels by mathematical calculation, such as (a) fuzzy comprehensive evaluation method [13]; (b) factor analysis [22,23]; (c) analytic hierarchy process [24]; (d) cluster analysis [25,26]; (e) regression analysis [27][28][29]; (f) Bayesian analysis [30][31][32]; (g) logit model [33][34][35]; (h) time series analysis method [36,37]; and (i) Dempster-Shafer theory [38,39]. These methods have an objective and direct evaluation through the observed parameters, so as to find out the dangerous and harmful factors in the evaluation system, and then put forward the corresponding solutions in technology and management to realize safety management.…”

“…The visualization of keywords in risk analysis studies indicates that previous studies [8,13,16,19,20,25,26] that focused on risk assessment in ULPS were undertaken at an aggregate level. Systematization is the key requirement in safety risk assessment.…”

“…However, systematization firstly means the evaluation system is systematic, which reflects the performance of specific models; secondly, the evaluation system should comprehensively cover both qualitative and quantitative indicators. Most ULPS safety risk studies [8,13,16,19,20,25,26] rely on questionnaire survey data but do not provide much information about the index system of risk assessment. In addition, little is known about the underlying factors that affect the risk analysis of ULPS.…”

“…Furthermore, there is a paucity of research discerning the interrelationships between the multiple-index system and risk assessment, and the contributory indicators and multiple-index weight at a disaggregated level using Delphi method and entropy method. The visualization of keywords in risk analysis studies indicates that previous studies [8,13,16,19,20,25,26] that focused on risk assessment in ULPS were undertaken at an aggregate level. Systematization is the key requirement in safety risk assessment.…”