Real-Time Driving State Identification and Collision Risk Detection in Dump Trucks: A GPS Streaming Data Approach

Abstract. Real-time analysis and mining of vehicle GPS data is essential for effective traffic regulation. Currently, most vehicle analysis algorithms based on GPS data are designed for static datasets, with fewer algorithms addressing dynamic streaming GPS data. Moreover, the limited number of real-time algorithms primarily focus on public transportation vehicles such as taxis and buses. There remains a significant gap in the analysis of specialized vehicles like dump trucks, which fails to meet the regulatory needs for monitoring these vehicles. To address this, this paper proposes a method for identifying driving states and detecting collision risks for dump trucks based on real-time GPS stream data. First, by partitioning the data, the method enables the separate calculation and identification of various operational states of different vehicles, such as their location, speed, and direction. Second, we partition the data based on vehicle positions to detect potential collision risks among vehicles in nearby areas. Experimental results show that the data throughput reaches 25,000 and 66,000 records per second for each method, with a data skew rate controlled below 0.1, demonstrating the method’s efficiency in real-time driving state recognition and collision risk detection for dump trucks.