MODELLING ROAD ACCIDENTS IN SELANGOR: A COMPARATIVE ANALYSIS BY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) AND ARTIFICIAL NEURAL NETWORK (ANN)

Q3 Social Sciences

JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT Pub Date : 2023-05-31 DOI:10.46754/jssm.2023.05.007

WAN NUR AFRINA WAN MUHAMMAD AZAN, NURBAIZURA BORHAN, SITI MERIAM ZAHARI

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引用次数: 0

Abstract

Road accidents have become one of the major problems globally. It was reported that the total number of road accidents in Malaysia has increased by 0.92% within the last ten years. Modelling road accident occurrences is critical for policymakers to understand the trend and pattern of road accidents to provide an appropriate countermeasure. This study attempts to forecast the road accident occurrences on federal and state roads in Selangor, Malaysia. This study utilised monthly road accident data from January 2011 to December 2021. The traditional univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) models were employed and the performance of each model was assessed. The findings found that the ANN model outperformed the SARIMA model in training and validation. Furthermore, the ANN model has the lowest RMSE, MAE, MAPE and MASE values for both sets. This study demonstrates the potential of machine learning in forecasting and predicting road accident occurrences, giving more flexibility and assumption-free methodology.

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雪兰莪州道路交通事故建模:季节性自回归综合移动平均(sarima)和人工神经网络(ann)的比较分析

道路交通事故已成为全球性的主要问题之一。据报道，马来西亚的道路交通事故总数在过去十年中增加了0.92%。模拟道路交通事故的发生对于决策者了解道路交通事故的趋势和模式，从而提供适当的对策至关重要。本研究试图预测道路事故发生在雪兰莪州的联邦和州道路，马来西亚。这项研究利用了2011年1月至2021年12月的月度道路交通事故数据。采用传统的单变量季节性自回归综合移动平均(SARIMA)和人工神经网络(ANN)模型，并对模型的性能进行了评估。结果发现，ANN模型在训练和验证方面优于SARIMA模型。此外，人工神经网络模型的RMSE、MAE、MAPE和MASE值在两组中均最低。这项研究展示了机器学习在预测和预测道路交通事故发生方面的潜力，提供了更大的灵活性和无假设的方法。

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来源期刊

JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT Social Sciences-Geography, Planning and Development

CiteScore

1.40

自引率

0.00%

发文量

163

期刊介绍： The Journal of Sustainability Science and Management is an Open-Access and peer-reviewed journal aims to publish scientific articles related to sustainable science; i.e. an interaction between natural sciences, social science, technologies and management for sustainable development and wise use of resources. We particularly encourage manuscripts that discuss contemporary research that can be used directly or indirectly in addressing critical issues and sharing of advanced knowledge and best practices in sustainable development.