Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

Abstract

Task-related fatigue, caused by prolonged driving, is a major cause of vehicle crashes. Despite noticeable academic achievements, monitoring drivers’ fatigue on road sections is still an ongoing challenge which must be met to prevent and reduce traffic accidents. Fortunately, individual instances of vehicle trajectory big data collected through advanced vehicle-GPS systems offer a strong opportunity to trace driving durations. We propose a new approach by which to monitor task-related fatigued drivers by directly using the ratio of potentially fatigued drivers (RFD) to all drivers for each road section. The method used to compute the RFD index was developed based on two inputs: the distribution of the driving duration (extracted from vehicle trajectory data), and the boundary condition of the driving duration between fatigued and non-fatigued states. We demonstrate the potentialities of the method using vehicle trajectory big data and real-life traffic accident data. Results showed that the measured RFD has a strong explanatory power with regard to the traffic accident rate, with a statistical correlation of 0.86 at least, for regional motorway sections. Therefore, it is expected that the proposed approach is a feasible means of successfully monitoring fatigued drivers in the present and near future era of smart-mobility big data.