Development of Fuzzy Logic Forecast Models for Location-Based Parking Finding Services
Development of Fuzzy Logic Forecast Models for Location-Based Parking Finding Services
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DOI：
10.1155/2013/473471
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发表时间：
2013-03
影响因子：
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通讯作者：
Zhirong Chen;J. Xia;B. Irawan
中科院分区：
文献类型：
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作者：
Zhirong Chen;J. Xia;B. Irawan
澳大利亚交通当局提供的停车换乘设施一直是鼓励汽车司机使用火车和公共汽车等公共交通工具的有效方法。然而，随着人口的增长和车辆运行成本的增加，对更多停车位的需求已经升级。通常情况下，PnR设施在清晨就满负荷运转，通勤者在车站周围的街道上非法停车。本文报道了一个基于位置的停车位寻找服务的PnR用户的发展。根据用户当前的位置，系统可以通知用户在高峰期哪个车站是停放汽车的最佳地点。两个标准-停车可用性和最短的旅行时间-被用来评估最好的车站。采用模糊逻辑预测模型对停车需求高峰期的停车可用性进行了预测。使用这些方法的原型已经开发的基础上的燕麦街和卡莱尔PnR设施在珀斯，西澳大利亚州的案例研究。该系统已被证明是有效的，并有潜力被应用到其他停车系统。
Park-and-ride (PnR) facilities provided by Australian transport authorities have been an effective way to encourage car drivers to use public transport such as trains and buses. However, as populations grow and vehicle running costs increase, the demand for more parking spaces has escalated. Often, PnR facilities are filled to capacity by early morning and commuters resort to parking illegally in streets surrounding stations. This paper reports on the development of a location-based parking finding service for PnR users. Based on their current location, the system can inform users which is the best station to park their cars during peak period. Two criteria—parking availability and the shortest travel time—were used to evaluate the best station. Fuzzy logic forecast models were used to estimate the uncertainty of parking availability during the peak parking demand period. A prototype using these methods has been developed based on a case study of the Oats Street and Carlisle PnR facilities in Perth, Western Australia. The system has proved to be efficacious and has the potential to be applied to other parking systems.