Identification of Road Traffic Injury Risk Prone Area Using Environmental Factors by Machine Learning Classification in Nonthaburi, Thailand

Morakot Worachairungreung, Sarawut Ninsawat, Apichon Witayangkurn and Matthew N. Dailey
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Morakot Worachairungreung: Asian Institute of Technology, Pathum Thani, Klong Luang 12120, Thailand
Sarawut Ninsawat: Asian Institute of Technology, Pathum Thani, Klong Luang 12120, Thailand
Apichon Witayangkurn: Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan
Matthew N. Dailey: Asian Institute of Technology, Pathum Thani, Klong Luang 12120, Thailand

Sustainability, 2021, vol. 13, issue 7, 1-25

Abstract: Road traffic injuries are a major cause of morbidity and mortality worldwide and currently rank ninth globally among the leading causes of disease burden regarding disability-adjusted life years lost. Nonthaburi and Pathum Thani are parts of the greater Bangkok metropolitan area, and the road traffic injury rate is very high in these areas. This study aimed to identify the environmental factors affecting road traffic injury risk prone areas and classify road traffic injuries from an environmental factor dataset using machine learning algorithms. Road traffic injury risk prone areas were set as the dependent variables for the analysis, with other factors that influence road traffic injury risk prone areas being set as independent variables. A total of 20 environmental factors were selected from the spatial datasets. Then, machine learning algorithms were applied using a grid search. The first experiment from 2017 in Nonthaburi and Pathum Thani was used for training the model, and then, 2018 data from Nonthaburi and Pathum Thani were used for validation. The second experiment used 2018 Nonthaburi data for the training, and 2018 Pathum Thani data were used for the validation. The important factors were grocery stores, convenience stores, electronics stores, drugstores, schools, gas stations, restaurants, supermarkets, and road geometrics, with length being the most critical factor that influenced the road traffic injury risk prone model. The first and second experiments in a random forest model provided the best model environmental factors affecting road traffic injury risk prone areas, and machine learning can classify such road traffic injuries.

Keywords: road traffic injury; environmental factors; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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