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Real-time dispatching of air taxis in metropolitan cities using a hybrid simulation goal programming algorithm

机译:利用混合仿真目标规划算法实时调度大都市城市空中出租车

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Expected to begin its operations in the coming years, air taxi aims to provide everyday transportation services to customers in metropolitan cities. These vehicles offer urban air mobility (UAM) services using the electric vertical takeoff and landing (eVTOL) technology. This research is the first to present a hybrid simulation goal programming (HSGP) approach to dispatch vehicles in a centralized air taxi network. After each customer drop-off, the model makes real-time decisions on (i) whether the air taxi must become idle or pick up customers, and (ii) the station to which the air taxi should be dispatched (if the air taxi is operational). The feasibility of the HSGP approach is tested using potential air taxi demands in New York City (NYC) provided by a prior study. The results of the experimentation suggest that the minimum number of air taxis required for efficient operation in NYC is 84, functioning with an average utilization rate of 66%. In addition, the impacts of commuter's "willingness to fly" rate, percentage of demand fulfillment, on-road travel limit, maximum customer wait time, and arrival distribution on the optimal number of air taxis, utilization rate, number of customers served, and cost incurred per customer are examined. Analyses show that the "willingness to fly" rate appears to have a linear influence on the number of air taxis and the efficiency, while on-mad travel distance has an exponential impact on the performance measures. The HSGP algorithm developed in this paper can be used by any company that is interested in venturing into the air taxi market.
机译:预计将在未来几年开始运营,Air Taxi旨在向大都市城市的客户提供日常运输服务。这些车辆提供了使用电动垂直起飞和着陆(EVTOL)技术的城市空移性(UAM)服务。该研究是第一个呈现混合模拟目标编程(HSGP)方法来调度集中式空中出租车网络。在每次客户下降后,该模型在(i)上的实时决定是空中出租车是否必须闲置或拾取客户,并且(ii)应该派遣空中出租车的车站(如果出租车是操作)。使用先前研究提供的纽约市(NYC)的潜在空中出租车需求进行HSGP方法的可行性。实验结果表明,NYC有效运作所需的最小空中出租车数量为84,其运作,平均利用率为66%。此外,通勤者的“愿意飞行”的影响,需求履行的百分比,路线旅行限额,最大客户等待时间,以及在最佳的空中出租车,利用率,客户数量的抵达分配,以及服务检查每位客户的费用。分析表明,“飞行愿意”率似乎对空中出租车数量和效率的数量具有线性影响,而疯狂的旅行距离对性能措施的指数影响。本文开发的HSGP算法可由任何有兴趣冒险进入Air Taxi市场的公司使用。

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