位置识别率影响下基于博弈的共享单车停车奖惩策略研究
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摘要
为解决共享单车停放问题,部分共享单车企业对用户停车行为进行奖惩,引导用户文明停车.本文研究了共享单车停车过程中,企业和用户的博弈过程.首先建立用户寻找规范停车点的时间成本函数,基于获得效用理论建立优惠券奖励效用函数,基于展望理论建立用户惩罚效用函数;其次,建立奖励和惩罚两种机制下的停车选择的混合策略博弈模型,得到均衡状态下位置识别率、奖惩额度、停车点平均间距等影响因素的函数关系.最后,对两种机制的用户效用进行灵敏度分析,对不同出行时间和出行目的的停车行为引导效果的差异性进行分析.研究结果表明:两种机制均存在博弈均衡解,但惩罚机制的条件更严格;当位置识别率低于临界值时,不能使用惩罚机制.企业可综合考虑投入与效益,选择合适的奖惩方案,在不同的市场运营时期采取不同的策略.
In order to solve the problem of bicycle-sharing parking, some bicycle-sharing enterprises give reward and punishment to users according to their parking behavior, so as to guide users to choose the civilized one. This paper studies the game process between enterprises and users in the process of bicycle-sharing parking. Firstly, it sets up the time cost function for the user to find a parking station, establishes the reward utility function of the coupon based on acquisition utility theory, and builds the user punishment utility function based on the outlook theory. Secondly, it establishes the mixed strategy game model of the parking behavior choice under the mechanisms of reward and punishment; the functional relationship of factors such as position recognition rate,reward and punishment amount and average distance between parking stations is also analyzed. Finally, the sensitivity analysis of the user utility of the two mechanisms and the difference of the guiding effect of parking behavior with different travel time and purpose is carried out. The results show that the two mechanisms have an equilibrium solution, but the condition of punishment is more stringent, and the penalty mechanism can't be used when the position recognition rate is lower than the critical value. The enterprise should choose the appropriate reward and punishment scheme by considering the input and benefit comprehensively and take different strategies in different market operation period.
In order to solve the problem of bicycle-sharing parking, some bicycle-sharing enterprises give reward and punishment to users according to their parking behavior, so as to guide users to choose the civilized one. This paper studies the game process between enterprises and users in the process of bicycle-sharing parking. Firstly, it sets up the time cost function for the user to find a parking station, establishes the reward utility function of the coupon based on acquisition utility theory, and builds the user punishment utility function based on the outlook theory. Secondly, it establishes the mixed strategy game model of the parking behavior choice under the mechanisms of reward and punishment; the functional relationship of factors such as position recognition rate,reward and punishment amount and average distance between parking stations is also analyzed. Finally, the sensitivity analysis of the user utility of the two mechanisms and the difference of the guiding effect of parking behavior with different travel time and purpose is carried out. The results show that the two mechanisms have an equilibrium solution, but the condition of punishment is more stringent, and the penalty mechanism can't be used when the position recognition rate is lower than the critical value. The enterprise should choose the appropriate reward and punishment scheme by considering the input and benefit comprehensively and take different strategies in different market operation period.
引文
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[2]AHMADREZA F I,ROBERT H,LAVANYA M.An empirical analysis of bike sharing usage and rebalancing:Evidence from Barcelona and Seville[J].Transportation Research Part A,2017(97):177-191.
[3]HAMILTON T L,WICHMAN C J.Bicycle infrastructure and traffic congestion:Evidence from DC's capital bikeshare[J].Journal of Environmental Economics and Management,2018(87):72-93.
[4]邓力凡,谢永红,黄鼎曦.基于骑行时空数据的共享单车设施规划研究[J].规划师,2017(10):82-88.[DENGL F,XIE Y H,HUANG D X.Bicycle-sharing facility planning base on riding spatio-temporal data[J].Planners,2017(10):82-88.]
[5]尤冬梅.基于博弈论的城市道路违章停车治理[J].交通运输研究,2017,3(3):30-35.[YOU D M.Urban parking violation governance based on game theory[J].Transport Research,2017,3(3):30-35.]
[6]肖海燕,度巍.出行者出行方式选择行为的重复博弈分析[J].交通运输系统工程与信息,2015,15(2):24-28.[XIAO H Y,DU W.Repeated games analysis of trip model choice behavior[J].Journal of Transportation Systems Engineering and Information Technology,2015,15(2):24-28.]
[7]张思佳,贾顺平,麻存瑞,等.闭合通勤链中基于博弈的小汽车拥有者出行方式选择研究[J].交通运输系统工程与信息,2017,17(2):14-20.[ZHANG S J,JIA SP,MA C R,et al.Travel mode choice behavior of private car owners in commuters'closed trip-chain based on the game theory[J].Journal of Transportation Systems Engineering and Information Technology,2017,17(2):14-20.]
[8]李学文,徐丽群.城市轨道交通运营效率评价:基于改进的博弈交叉效率方法[J].系统工程理论与实践,2016,36(4):973-980.[LI X W,XU L Q.Evaluating the operational efficiency of rail transit:An application of improved game cross efficiency approach[J].Systems engineering-Theory&Practice,2016,36(4):973-980.]
[9]丁剑.基于优势出行距离的方式分担率模型及软件实现[D].南京:东南大学,2017.[DING J.Travelmodal spilt model based on dominant distance an software design[D].Nanjing:Southeast University,2017.]
[10]北京市统计局.北京统计年鉴[R].北京:中国统计出版社,2017.[Bureau of statistics of Beijing.Beijing statistical yearbook[R].Beijing:China Statistics Press,2017.]
[11]ZEITHAML V A.Consumer perceptions of price,Quality,and value:A means-end model and synthesis of evidence[J].Journal of Marketing,1988,52(3):2-22.
[12]陈斐,邓玉林,达庆利.基于展望理论的知识型员工激励机制[J].东南大学学报(自然科学版),2012,42(5):1016-1020.[CHEN F,DENG Y L,DA Q L.Incentive mechanism for knowledge workers based on prospect theory[J].Journal of Southeast University(Natural Science Edition),2012,42(5):1016-1020.]
[13]邵昀泓,王炜,程琳.出行方式决策的随机效用模型研究[J].公路交通科技,2006,23(8):110-115.[SHAOY H,WANG W,CHENG L.Study on the individual travel mode choice based on the discrete choice theory[J].Journal of Highway and Transportation Research and Development,2006,23(8):110-115.]