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Development of Fuzzy Logic Forecast Models for Location-Based Parking Finding Services

机译:基于位置的停车查找服务的模糊逻辑预测模型的开发

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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.
机译:澳大利亚运输当局提供的停车即乘(PnR)设施一直是鼓励汽车驾驶员使用火车和公共汽车等公共交通工具的有效方法。但是,随着人口的增长和车辆行驶成本的增加,对更多停车位的需求也在增加。通常,PnR设施在清晨就已满员,通勤者则在车站周围的街道上非法停车。本文报告了针对PnR用户的基于位置的停车查找服务的开发。根据他们的当前位置,该系统可以通知用户哪个是高峰时段停车的最佳站点。泊车可用性和最短旅行时间这两个标准用于评估最佳车站。模糊逻辑预测模型用于估计高峰停车需求期间停车可用性的不确定性。基于对西澳大利亚州珀斯的Oats Street和Carlisle PnR设施的案例研究,开发了使用这些方法的原型。该系统已被证明是有效的,并且有可能应用于其他停车系统。

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