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

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