GIS-based assessment of pedestrian-vehicle accidents in terms of safety with four different ML models

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Burak Yiğit Katanalp, Ezgi Eren
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引用次数: 9

Abstract

Abstract In this study, both micro and macro level evaluation of pedestrian-vehicle crashes were conducted. Macro-level findings were obtained with GIS-based density analyzes, and critical road segments were determined. The data on road characteristics, traffic characteristics, built environment and land use were collected in 70 critical urban road segments. While conducting micro-level research, commonly used multilayer perceptron and C4.5 decision tree, as well as innovative converted fuzzy-decision model and revised fuzzy-decision model, which significantly reduces the expert judgements on fuzzy models, were used. Significant rules were extracted, and were evaluated from safety perspective. Information gain ratio was used to deal with the black-box structure of machine learning models and to examine independent factors in-depth. The best performance was achieved in revised fuzzy decision model with 68.57% accuracy. The results revealed that land use, parking and peak hour volume have high effect, as well as public transport, speed and road type factors have the greatest effect on pedestrian safety. In the light of the results, various managerial implications such as controlling the density of public transport on main arterials, preventing stop-and-go effects, and monitoring vehicle speeds especially during peak hours were suggested to improve pedestrian safety.
基于gis的四种不同ML模型的行人-车辆事故安全评估
摘要本研究从微观和宏观两个层面对行人与车辆碰撞进行了评价。通过基于gis的密度分析获得宏观层面的结果,并确定关键路段。收集了70个城市关键路段的道路特征、交通特征、建筑环境和土地利用数据。在微观层面的研究中,使用了常用的多层感知器和C4.5决策树,以及创新的转换模糊决策模型和修正模糊决策模型,大大减少了对模糊模型的专家判断。提取有意义的规则,从安全性角度进行评价。利用信息增益比来处理机器学习模型的黑箱结构,并深入检查独立因素。修正模糊决策模型的准确率为68.57%。结果表明,对行人安全影响最大的因素是土地利用、停车和高峰时段量,而对行人安全影响最大的因素是公共交通、车速和道路类型。根据研究结果,提出了各种管理建议,如控制主干道上的公共交通密度,防止走走停停效应,以及监测车辆速度,特别是在高峰时段,以提高行人安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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