|Table of Contents|

Overview of identification methods of highway accident-prone sections(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年5期
Page:
72-87
Research Field:
交通工程
Publishing date:

Info

Title:
Overview of identification methods of highway accident-prone sections
Author(s):
ZHANG Chi1;  ZHOU Yu-ming1;  ZHAI Yi-yang1;  ZHANG Min2;  WANG Bo1
(1. School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China; 2. School of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
traffic engineering;  identification method;  review;  accident-prone section;  neural network algorithm;  Bayesian method;  GIS
PACS:
U491.1
DOI:
10.19721/j.cnki.1671-8879.2023.05.008
Abstract:
To explore the research status and development trend of highway accident-prone section identification methods, the China knowledge network(CNKI)and Web of ScienceTM were used to examine the topic-related literature using bibliometric methodologies. From a macro viewpoint, a quick analysis of the number of publications, keywords, and research nations were given, along with an explanation of the strategy for identifying accident-prone highways basedon the mechanisms of accidents. The accident-prone road section identification method was divided into four sub-tasks based on this,road section unit division, data collection and processing, selection of identification indexes, and identification methods. The fourthsub-task was divided into three categories, statistically-driven methods, model-driven methods, and spatially-driven methods. Thecurrent status of research on the various types of methods was reviewed, as well as their advantages. The future path of theidentification methods for highway accident-prone sections was then suggested from the viewpoints of roadway unit division, trafficaccident data, and roadway identification methods. The results show that road units that satisfy the objective length ofaccident-prone road sections can be more accurately classified using the sliding window approach and clustering algorithm. Model-driven approaches can be seen as providing a solution to the issues with statistically driven methods, such as their lack of flexibilityin defining indicator levels that are more subjective and their failure to account for the randomness of accidents. The model-drivenmethod has the flaw of an unscientific selection of accident causative factors. A reasonable selection of accident causative factors,and rigorous relevance verification based on that selection, combined with grey theory can help to improve the accuracy of themodel. Spatially driven approaches are useful for visualizing data, and the spatiotemporal cube method based on geographicinformation systems(GIS)taking the time dimension into account has grown in popularity as a research area recently. The fusion ofmodel-driven and spatial-driven approaches helps to build models with strong visualization and accurate discrimination. Futureresearch may focus on expanding to two-dimensional geographical and temporal road segment units, multi-dimensional dynamic datafusion, quantitative modeling of accident causation, and accident indicators with a focus on human components.3 tabs, 14 figs, 62 refs.

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Last Update: 2023-10-10