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Distraction is one of the most prominent driver characteristics which accounts for several road crashes and is a major cause of concern for almost all road safety institutions. The main objective of this study was to explore and identify various causes of a crash on Yamuna Expressway. Furthermore, another objective includes the detailed study of driver’s fault, including in-vehicular distraction and identifying the most and least distracting activity. Moreover, other driver characteristics like fatigue, the reaction of the driver when someone suddenly comes on the way, etc. were also analyzed for their contributions towards crash occurrence. In this study, supervised machine learning techniques viz. Classification and Regression Tree (CART) was used to predict the possibility of crash occurrence. The model results show that using a cell phone in case of adverse weather conditions has the highest likelihood of crash occurrence. Moreover, among other distraction tasks adjustment of radio/compact disc (CD), while driving has the least likelihood of a crash. Further, it was also found that the reaction of the driver when someone comes suddenly on the way (e.g. animal, another crossing vehicle/pedestrian) and long driving hours leading to headache/backache/fatigue also affect the possibility of crash occurrence. Model results were then validated using the testing dataset and it was found that the model is equally efficient in predicting crashes as it was with the training dataset. This study provides insight into several important driver characteristics and will be helpful in developing educational and enforcement strategies thereby reducing crash risk.

Investigating major cause of crashes on Indian expressways and developing strategies for traffic safety management