Performance of Traffic Accidents’ Prediction Models

IF 1 Q4 ENGINEERING, CIVIL

Jordan Journal of Civil Engineering Pub Date : 2023-01-01 DOI:10.14525/jjce.v17i1.04

H. Al-Masaeid, Farah J. Khaled

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引用次数: 0

Abstract

Modeling traffic-accident frequency is a critical issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objectives of this study are to model traffic road accidents, fatalities and injuries in Jordan, using different modeling techniques, including regression, artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models and to evaluate the safety impact of travel-restriction strategies during Covid-19 pandemic on trafficaccident statistics for the year 2020. To accomplish these objectives, data of traffic accidents, registered vehicles (REGV), population (POP) and economic gross domestic product (GDP) from 1995 through 2020 were obtained from related sources in Jordan. The analysis revealed that accidents, fatalities and injuries have an increasing trend in Jordan. Root mean of square error (RMSE), mean absolute error (MAE) and coefficient of multiple determination (R2) were sued to evaluate the performance of the developed prediction models. Based on model performance, the ANN models are the best, followed by the ARIMA models and then the regression models. Finally, it was concluded that the strategies undertaken by the government of Jordan to combat Covid-19, including complete and partial banning of travel, resulted in a considerable reduction of accidents, injuries and fatalities by about 35%, 37% and 50%, respectively. KEYWORDS: Traffic accidents, Artificial neural network, Covid-19 pandemic, Regression, Timeseries analysis, Prediction model

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交通事故预测模型的性能

建立交通事故频率模型是一个关键问题，可以更好地了解不同国家的交通事故趋势和当前交通政策和实践的有效性。本研究的主要目标是利用不同的建模技术，包括回归、人工神经网络(ANN)和自回归综合移动平均(ARIMA)模型，对约旦的交通事故、死亡和伤害进行建模，并评估2019冠状病毒病大流行期间出行限制策略对2020年交通事故统计数据的安全影响。为了实现这些目标，从约旦的有关来源获得了1995年至2020年的交通事故、登记车辆、人口和经济国内生产总值的数据。分析显示，约旦的事故、死亡和伤害呈上升趋势。采用均方根误差(RMSE)、平均绝对误差(MAE)和多重决定系数(R2)来评价所建立的预测模型的性能。从模型性能来看，人工神经网络模型的性能最好，其次是ARIMA模型，最后是回归模型。最后，得出的结论是，约旦政府为抗击Covid-19而采取的战略，包括全面和部分禁止旅行，使事故、伤害和死亡人数分别大幅减少了约35%、37%和50%。关键词:交通事故，人工神经网络，Covid-19大流行，回归，时间序列分析，预测模型

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来源期刊

Jordan Journal of Civil Engineering ENGINEERING, CIVIL-

CiteScore

2.10

自引率

27.30%

发文量

期刊介绍： I am very pleased and honored to be appointed as an Editor-in-Chief of the Jordan Journal of Civil Engineering which enjoys an excellent reputation, both locally and internationally. Since development is the essence of life, I hope to continue developing this distinguished Journal, building on the effort of all the Editors-in-Chief and Editorial Board Members as well as Advisory Boards of the Journal since its establishment about a decade ago. I will do my best to focus on publishing high quality diverse articles and move forward in the indexing issue of the Journal.