Trustworthiness Assessment for Crowdsourcing-Based Citywide Parking Availability Sensing via Connected and Automated Vehicles

Abstract

Real-time status acquisition of parking spaces is highly valuable for an intelligent urban parking system. Crowdsourcing-based parking availability sensing via connected and automated vehicles (CAVs) provides a feasible method with the advantages of high coverage and low cost. However, data trust issues arise from incorrect detection and incomplete information. This paper proposes a trustworthiness assessment method for crowdsourced CAV data considering different impact factors, such as the distance between the CAV and the target parking space, line abrasion, scene complexity, and image sharpness. The crowdsourced CAV data are collected through extensive field experiments and PreScan simulations. The classical line detection algorithm of VPS-Net and the target detection algorithm of YOLO-v3 are applied to detect on-street parking availability. A failure probability model based on the XGBoost algorithm is then developed to establish the relationship between data trustworthiness and different impact factors. The results show that the proposed model has an average accuracy of 78.29% and can effectively assess the degrees of external influences on the trustworthiness of the crowdsourced data. This paper provides a new tool to identify the data quality and improve the sensing accuracy for a crowdsourcing-based parking availability information system.