Participant Recruitment of Vehicular Crowdsensing Along Freeways for Traffic Accident Detection

Abstract

Vehicular crowdsensing provides a new approach for freeway traffic accident detection. However, the uncertainty on traffic accidents and Mobile Users (MUs) brings great challenges for participant recruitment in constructing the deterministic representation of sensing tasks and estimating the participants. To address the challenges, a participant recruitment method for freeway traffic accident detection is proposed. In the method, to deal with the non-deterministic sensing tasks and MUs, the temporal-spatial distribution of accident risk is estimated by optimal transport theory to represent sensing tasks, and the probability distributions of MUs’ trip distance and requested rewards are used to estimate MUs. Then the participant recruitment problem is converted into an optimal coverage problem for accident risk under the macro statistical characteristics of MUs. The participant recruitment model is established to determine the participants by maximizing the coverage rate of accident risk with the budget constraint. And a greedy heuristic strategy is used to solve the model. Simulation experiments are carried out to validate the proposed method. The results show the proposed method is effective and reliable in freeway traffic accident detection.