PW 0997 Investigation factors affecting motorized two-wheel vehicle accident severity on thailand national highway

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Abstract

Road accident is one of the biggest problems that has made a tremendous impact on economic and social in every country, especially the developing and low-income countries where many of it located on South East Asia region. Road accident has been one of the biggest problems causing economic and social losses in Thailand. Motorcycles were the highest vehicles that involved in the severe accidents.

Although, the Royal Thai Government has attempted to find many solutions to reduce the number of road accident victims. This research tries to investigate the factors that affect to the motorcyclist severity injuries. The binary logistic regression technique was implemented to generate the prediction model to predict a probability of the severe motorcycle accidents on Thailand national highway. The accident data that occurred on Thai national highway between January 2014 to December 2016 which extracted from the Highway Accident Information Management System (HAIMS) were used to generate the model. The result of the prediction model provides the variables that affect to the severity injuries of motorcycle riders. The variables consist of rider gender, the age of motorcyclists, vehicle collision partner, highway alignment, environmental condition, the cause of the accident, and crash characteristic. These variables bring about directly influence to the severe injury of the two-wheel vehicle rider. Most of the variable was coming from the rider factors such as gender, age, and drunk driving. Lacking education and ineffectiveness law enforcement can cause the bad behavior in the riders. Implementing the 3E safety principle will help to reduce the problems. Improving traffic safety education and strictly enforce the law would help to change the rider behavior. Furthermore, training the young generation to acknowledge and concern with the traffic safety problems. This solution will be the sustainable answer to reducing the overall traffic accident.

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