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Traffic radar-based speed meter (TRSM) is an important measuring instrument for measuring the speed of target vehicle based on the radar Doppler effect, and has been legally used in road vehicle speeding enforcement for more than 30 years in China. TRSM with target position determination (TRSM-TPD) is a new generation of TRSM with the advantages of speeding enforcement by lane, high accuracy of speed measurement and target position determination, high efficiency, low cost, etc. This paper proposes a new verification method for TRSM-TPD in road vehicle speeding enforcement. The proposed verification method includes the simulated speed calibration method for initial verification in the laboratory, and the online speed and position calibration method for subsequent verification in the field. The range of simulated radial speed is up to (1∼400) km/h and the maximum permissible error is within ±0.03 km/h, which can satisfy the full-range and high-accuracy speed calibration requirements for TRSM-TPD. The uncertainty of the reference facility in actual traffic is 0.12% within the speed range of (60∼250) km/h, where coverage factor k = 3. The simulated and online calibration results validate the effectiveness of the proposed verification method and the feasibility of the realized target speed simulator and the established reference facility. [ABSTRACT FROM AUTHOR]

Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users’ private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection. [ABSTRACT FROM AUTHOR]