Identifying and characterizing truck stops from GPS data

Abstract

Information about truck stops in highways is essential for trip planning, monitoring and other applications. GPS data of truck movement can be very useful to extract information that helps us understand our highway network better. In this paper, we present a method to identify truck stops on highways from GPS data, and subsequently characterize the truck stops into clusters that reflects their functionality. In the procedure, we extract the truck stoppage locations from the GPS data and cluster the stoppage points of multiple trips to obtain truck stops. We construct arrival time distribution and duration distribution to identify the functional nature of the stops. Subsequently, we cluster the truck stops using the above two distributions as attributes. The resultant clusters are found to be representative of different types of truck stops. The characterized truck stoppages can be useful for dynamic trip planning, behavior modeling of drivers and traffic incident detection.