ANALYSIS AND FORECAST OF ROAD TRAFFIC ACCIDENTS IN VIETNAM BASED ON GREY BP NEURAL NETWORK

Vuong Xuan Can, Mou Rui Fang, Vu Trong Thuat
2018 KỶ YẾU HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XI NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN   unpublished
With development of economy and society, developing countries like Vietnam have to deal with transportation issues, such as traffic accidents, traffic congestion, environmental pollution and so on. Especially road traffic accidents (RTAs) have a great impact on sustainable development. Forecast of RTAs is an important step in the traffic safety management, it not only helps us to know the rules of RTAs, but also plays an important role to reduce the likelihood of RTAs and to improve the
more » ... improve the management levels of road traffic safety. The Grey BP Neural Network forecasting model (GM-BP model) of RTAs in short-term was proposed. This model combines the GM(1,1) model and BP Neural Network model with the aim of identifying its suitability for forecast of RTAs under Vietnam condition. An example is given with the number of fatalities by RTAs in Vietnam from 2002 to 2013. The results showed that, the proposed model is better than single GM(1,1) model and BP Neural Network model. This proves the applicability of GM-BP model to the short-term forecast of RTAs in Vietnam.
doi:10.15625/vap.2018.0005 fatcat:g3v5degrxzemtklmfppqmbfbge