Detecting On-Street Parking Spaces in Smart Cities: Performance Evaluation of Fixed and Mobile Sensing Systems

作者：Roman, Cristian^*; Liao, Ruizhi; Ball, Peter; Ou, Shumao; de Heaver, Martin

来源：IEEE Transactions on Intelligent Transportation Systems, 2018, 19(7): 2234-2245.

DOI：10.1109/TITS.2018.2804169

• On-street/roadside parking
• sonar/ultrasonic
• supervised learning
• map matching
• parking sensors
• crowdsourcing

As the number of vehicles continues to grow, parking spaces are at a premium in city streets. In addition, due to the lack of knowledge about street parking spaces, heuristic circling in the streets not only costs drivers' time and fuel, but also increases city congestion. In the wake of the recent trend to build convenient, green, and energy -efficient smart cities, common techniques adopted by high-profile smart parking systems are reviewed, and the performance of the various approaches are compared. A mobile sensing unit has been developed as an alternative to the fixed sensing approach. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. By extracting parked vehicles' features from the collected trace, a supervised learning algorithm has been developed to estimate roadside parking occupancy. Multiple road tests were conducted around Wheatley (Oxfordshire) and Guildford (Surrey) in the U.K. In the case of accurate GPS readings, enhanced by a map matching technique, the accuracy of the system is above 90%. A quantity estimation model is derived to calculate the density of sensing units required to cover urban streets. The estimation is quantitatively compared with a fixed sensing solution. The results show that the mobile sensing approach can perform at the same level as fixed sensing solutions when accurate location information is available but substantially fewer sensors are needed compared with the fixed sensing system.

• 出版日期2018-7
• 单位香港中文大学（深圳）