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Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

机译:大城市地区具有实时传感数据的出租车调度:后视控制方法

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Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.
机译:由于系统容量和客户需求发生变化,行动不协调,大都市地区的传统出租车系统通常效率低下。通过在现代车辆中广泛部署网络传感器,可以实时收集有关客户需求和系统状态的大量信息。该信息为大型智能运输系统提供了执行各种类型的控制和协调的机会。在本文中,我们提出了一种用于调度出租车的后方视野控制(RHC)框架,该框架结合了高度时空相关的需求/供应模型以及实时全球定位系统(GPS)的位置和占用信息。目标包括使服务质量的需求与供应之间的时空比例与最小电流和预期的未来出租车空转行驶距离相匹配。广泛的跟踪驱动分析以及包含美国加利福尼亚州滑行运营记录的数据集,表明我们的解决方案将平均总空转距离减少了52%,并在一个实验时段内减少了整个城市的供需比率误差45%。此外,我们的RHC框架与各种预测模型和优化问题公式兼容。这种兼容性特性使我们能够使用相应的需求不确定性模型来解决鲁棒的优化问题,该模型可提供破坏性事件信息。

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