ITAIS: A novel framework for enhancing traffic safety

Abstract

Traffic safety is considered as a key issue in public health. Traffic collisions bring pain and suffering, and cause large amount of losses around the world each year. In this paper, a novel framework, called iTAIS, is proposed to enhance traffic safety using data mining and mobile computing techniques. iTAIS can provide users with driving tips based on their locations, which help to reduce the occurrence of potential traffic collisions. iTAIS consists of two main components: key factors identification and smart client applications. iTAIS first uses clustering algorithm to analyze the traffic collision data, which was collected from 2006 to 2010 in the city of Regina, Canada. In this step, traffic collisions will be grouped based on the locations of occurrence, and the key factors that contribute to the occurrence of collisions in each group will be identified respectively. In the second step, two smart client applications are designed to provide users with driving tips based on their locations. Experimental results show that iTAIS can effectively identify the key factors that contribute to the occurrence of traffic collisions occurred on different roads under various circumstances. Also, the two smart client applications can efficiently help users gain easy access to obtaining the driving tips, and help to further enhance traffic safety in the city of Regina.