Identifying Patterns of Pedestrian Crashes in Urban Metropolitan Roads in India using Association Rule Mining

Abstract

Pedestrian safety is an important component of efforts to prevent road traffic injuries. Pedestrians constitute to around 22% of the total deaths occurring on the world roads. According to the recent report by the Ministry of Road Transport and Highways (MoRTH), Government of India, the number of pedestrian-related deaths was 15,746 (10.5%) of total persons killed in the country during the year 2016. This high proportion of mortality and severity injury among pedestrians necessitates more investigation to identify determinants to reduce crashes in the future. The present research used the Apriori algorithm of supervised association rule mining to identify the patterns of pedestrian severity injury in urban Indian metropolitan city, Chennai. Using the RADMS database of the government of Tamilnadu, vehicle-pedestrian crashes were analyzed for recent two years between 2015 and 2016. The results highlight the fact that middle-aged pedestrians are more vulnerable to road traffic crashes. Exceeding speed limits than the posted speed, especially in the highways, results in fatal crashes among pedestrians. Vehicle-pedestrian crashes are frequent at sites where there are no median separators when drivers do not respect the right of way rules. The findings of the present study will help the traffic safety professionals to understand patterns of crashes and take necessary countermeasures to decrease pedestrian injury-related crashes potentially.