基于动态定价的共享汽车自适应调度策略
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摘要
汽车共享在满足居民日益增长的出行需求的同时,也能有效缓解私家车快速发展带来的交通拥堵等问题.但由于用户出行随机性的特征,汽车共享供需平衡的解决成为关键问题.价格优惠激励的用户自适应调度作为一种有效的解决办法被提出,在450份用户问卷调查的基础上系统分析动态定价对消费者取还车行为的影响机制,并进一步利用自动控制原理以系统不平衡指数作为控制变量,构建动态定价机制下共享汽车自适应调度模型.最后,本文以上海汽车城运营的EVCARD汽车分时租赁项目为案例,证明基于动态定价的自适应调度策略可使得系统不平衡指数下降42%.
Car-sharing can meet the increasing travel needs of consumers, simultaneously solving traffic congestion caused by the large increase of private vehicle ownership. However, due to stochastic user travel, it is essential to solve system imbalance. Using the users through prince incentives is an excellent method for system balancing. Through 450 questionnaires, this paper studies how dynamic price influence users' behavior of picking up and returning vehicles. Then, using the principle of automatic control, a closed loop feed link to describe the adaptive scheduling strategy is built based on feedback dynamic pricing, where MBE(Mean Balancing Error)index is as the control variable. Finally, this paper takes the EVCARD electric car-sharing program in Shanghai as a case study to verify the feasibility and effectiveness of adaptive scheduling strategy. As the result of the adaptive scheduling strategy, MBE index is reduced by 42%.
Car-sharing can meet the increasing travel needs of consumers, simultaneously solving traffic congestion caused by the large increase of private vehicle ownership. However, due to stochastic user travel, it is essential to solve system imbalance. Using the users through prince incentives is an excellent method for system balancing. Through 450 questionnaires, this paper studies how dynamic price influence users' behavior of picking up and returning vehicles. Then, using the principle of automatic control, a closed loop feed link to describe the adaptive scheduling strategy is built based on feedback dynamic pricing, where MBE(Mean Balancing Error)index is as the control variable. Finally, this paper takes the EVCARD electric car-sharing program in Shanghai as a case study to verify the feasibility and effectiveness of adaptive scheduling strategy. As the result of the adaptive scheduling strategy, MBE index is reduced by 42%.
引文
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[2]JORGE D,CORREIA G.Carsharing systems demand estimation and defined operations:A literature review[J].European Journal of Transport&Infrastructure Research,2013,13(3):201-220.
[3]BALAC M,CIARI F,AXHAUSEN K W.Modeling the impact of parking price policy on free-floating carsharing:Case study for Zurich,Switzerland[J].Transportation Research Part C,2017(77):207-225.
[4]ZHOU S Z.Dynamic incentive scheme for rental vehicle fleet management[D].Cambridge:Massachusetts Institute of Technology,2012.
[5]XU M,MENG Q,LIU Z.Electric vehicle fleet size and trip pricing for one-way carsharing services considering vehicle relocation and personnel assignment[J]Transportation Research Part B Methodological,2018(111):60-82.
[6]JORGE D,MOLNAR G,CORREIA G H D A.Trip pricing of one-way station-based carsharing networks with zone and time of day price variations[J]Transportation Research Part B Methodological,2015(81):461-482.
[7]王喆,苗瑞,顾希垚,等.基于动态价格激励机制的租赁车辆配置研究[J].工业工程与管理,2015,20(4):80-85.[WANG Z,MIAO R,GU X Y,et al.The research of rental fleet deployment based on the dynamic price incentives[J].Industrial Engineering and Management,2015,20(4):80-85.]