Identification of factors causing Risky Driving Behavior on high-speed multi-lane highways in India through Principal Component Analysis

In developing countries like India, Multilane high-speed National Highways (NHs) are victims of high accident rates. The Indian National Highway network comprises only ۲% of the Indian road network, but transports ۴۰% of traffic, resulting in traffic accidents on National Highways. As observed from past studies, drivers are the main responsible factors for accident causation due to their risky behavior. Hence, to determine significant factors causing the risky behavior of drivers on multi-lane high-speed highways, the personal interview survey through Questionnaire was conducted for the road users of NH-۴۷ comprising of the responses to the drivers' demographics, attitude towards vehicle condition and maintenance, traffic regulations/ enforcement following attitude characteristics, and roadway environment characteristics. Principal component analysis (PCA) was applied to the questionnaire variables, and significant category-wise variables for risky driving were identified. Fifteen important variables contributed to risky driving behavior from the questionnaire database by PCA. They are Roadway environment characteristics like improper signals, roadside accident prevention infrastructure, improper pavement, and no safe crossing points; Driver's age and experience; Using mirrors while overtaking, using lights and dipper during night-time, and using hand signals during daytime; Using helmets and seatbelts while driving and having a valid vehicle insurance policy; Age of the vehicles, vehicle service frequency, and lane preference in their decreasing significance based on the questionnaire database. The authorities can take suitable measures to control the significant variables causing risky driving behavior on high speed multi-lane highways and reduce the accidents scenarios on the multilane highways.

کلیدواژه ها:

Road Safety ، Risky driving behavior ، Multi-lane high speed highways ، Questionnaire ، Principal component analysis