The Prediction of Road-Accident Risk through Data Mining: A Case Study from Setubal, Portugal

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
David Dias, José Silvestre Silva, Alexandre Bernardino
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引用次数: 3

Abstract

This work proposes a tool to predict the risk of road accidents. The developed system consists of three steps: data selection and collection, preprocessing, and the use of mining algorithms. The data were imported from the Portuguese National Guard database, and they related to accidents that occurred from 2019 to 2021. The results allowed us to conclude that the highest concentration of accidents occurs during the time interval from 17:00 to 20:00, and that rain is the meteorological factor with the greatest effect on the probability of an accident occurring. Additionally, we concluded that Friday is the day of the week on which more accidents occur than on other days. These results are of importance to the decision makers responsible for planning the most effective allocation of resources for traffic surveillance.
基于数据挖掘的道路事故风险预测——以葡萄牙塞图巴尔为例
这项工作提出了一种预测道路事故风险的工具。开发的系统包括三个步骤:数据的选择和收集、预处理和挖掘算法的使用。这些数据是从葡萄牙国民警卫队的数据库中导入的,它们与2019年至2021年发生的事故有关。结果表明,17 ~ 20时是事故发生最集中的时段,降雨是对事故发生概率影响最大的气象因素。此外,我们得出结论,周五是一周中发生事故最多的一天。这些结果对负责规划最有效地分配交通监控资源的决策者具有重要意义。
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
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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