Comparative Analysis of the Proportional Distribution Method and the Random Forest Algorithm for Predicting Pedestrian Traffic Accident Risk

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-07-22 DOI:10.1109/ACCESS.2025.3591297

Hristo V. Uzunov;Plamen G. Matzinski;Vasil H. Uzunov;Silvia V. Dechkova

{"title":"Comparative Analysis of the Proportional Distribution Method and the Random Forest Algorithm for Predicting Pedestrian Traffic Accident Risk","authors":"Hristo V. Uzunov;Plamen G. Matzinski;Vasil H. Uzunov;Silvia V. Dechkova","doi":"10.1109/ACCESS.2025.3591297","DOIUrl":null,"url":null,"abstract":"The risk of pedestrian-involved traffic accidents represents a significant challenge to road safety and necessitates objective methods for analyzing the contributing factors. This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian traffic accidents: a methodology based on proportional risk distribution and the Random Forest algorithm. The analysis utilizes data derived from real court cases, where linguistic variables defined as risk factors are categorized and quantified based on expert evaluations. The results demonstrate that both approaches are applicable for risk assessment, with Random Forest exhibiting higher accuracy and robustness in handling complex and heterogeneous data. Correlation analysis confirms a statistically significant linear relationship between the outputs of the two methods, supporting their validity. Graphical representations derived from the results offer a visual interpretation of risk severity and facilitate comparison between the two approaches. The proposed method is intended for road safety experts, engineers, analysts, and institutions in the field of transportation safety. Its primary aim is to provide an objective and quantitative tool for evaluating the risk factors contributing to pedestrian-related incidents. The method supports informed decision-making regarding preventive measures and awareness campaigns targeting both drivers and pedestrians.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"129828-129844"},"PeriodicalIF":3.6000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11088083","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11088083/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

The risk of pedestrian-involved traffic accidents represents a significant challenge to road safety and necessitates objective methods for analyzing the contributing factors. This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian traffic accidents: a methodology based on proportional risk distribution and the Random Forest algorithm. The analysis utilizes data derived from real court cases, where linguistic variables defined as risk factors are categorized and quantified based on expert evaluations. The results demonstrate that both approaches are applicable for risk assessment, with Random Forest exhibiting higher accuracy and robustness in handling complex and heterogeneous data. Correlation analysis confirms a statistically significant linear relationship between the outputs of the two methods, supporting their validity. Graphical representations derived from the results offer a visual interpretation of risk severity and facilitate comparison between the two approaches. The proposed method is intended for road safety experts, engineers, analysts, and institutions in the field of transportation safety. Its primary aim is to provide an objective and quantitative tool for evaluating the risk factors contributing to pedestrian-related incidents. The method supports informed decision-making regarding preventive measures and awareness campaigns targeting both drivers and pedestrians.

查看原文本刊更多论文

比例分布法与随机森林算法在行人交通事故风险预测中的比较分析

行人交通事故的风险是道路安全面临的重大挑战，需要客观的方法来分析其影响因素。本研究比较分析了两种预测行人交通事故风险的方法：基于比例风险分布的方法和随机森林算法。该分析利用了来自真实法庭案件的数据，其中定义为风险因素的语言变量根据专家评估进行分类和量化。结果表明，两种方法都适用于风险评估，随机森林在处理复杂和异构数据时表现出更高的准确性和鲁棒性。相关分析证实了两种方法的输出之间具有统计显著的线性关系，支持其有效性。从结果中得出的图形表示提供了对风险严重程度的可视化解释，并便于两种方法之间的比较。该方法适用于交通安全领域的道路安全专家、工程师、分析人员和机构。其主要目的是提供一个客观和定量的工具，以评估导致行人相关事故的风险因素。该方法支持针对司机和行人的预防措施和提高认识运动的知情决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.