城市出行结构优化的仿真研究

管理评论 ›› 2021, Vol. 33 ›› Issue (10): 289-296.

城市出行结构优化的仿真研究

侯立文

上海交通大学安泰经济与管理学院, 上海 200030

收稿日期:2018-09-10 出版日期:2021-10-28 发布日期:2021-11-29
作者简介:侯立文,上海交通大学安泰经济与管理学院副教授,博士。
基金资助:
国家自然科学基金项目（72171146；71372108）。

Simulation of Optimal Urban Travel Mode Structure

Hou Liwen

Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai 200030

Received:2018-09-10 Online:2021-10-28 Published:2021-11-29

摘要/Abstract

摘要： 为了尽可能满足多样化的出行需求，同时又能照顾到城市经济以及不同出行方式的发展需求，城市交通管理部门需要根据所有出行方式的长期价值贡献来优化其内部结构。为此，一方面基于状态依赖效用函数和Logit模型建立了能反映这种长期收益的动态规划模型，该模型的状态变量是具有Markov性质的方式占比，且引入了前次供给方式来反映其对当前决策的影响；另一方面则利用层次贝叶斯方法中的MCMC算法对供给概率模型中各因素的权重进行了估计，以体现因素权重的差异性。最后，根据新方式动态共乘的占比构造了四种出行方式的市场结构，并计算了前次供给为不同方式的状态转移概率矩阵，通过仿真实验展示了最优策略与总收益的关系、期初和期末出行结构的差异、总收益对方式供给概率的影响，以及动态共乘与前次供给和总收益的关系。

关键词: 出行结构, 动态共乘, 状态依赖效用, 动态规划模型

Abstract: In order to sufficiently satisfy various travel demands and in the meantime take into account the development of urban economy and different travel modes, transportation management department has to optimize the travel mode structure according to the long-term contribution of all modes. Based on state-dependent utility function and logit model, this study builds a dynamic programming model which incorporates mode shares as the state variable as well as previous supply to reflect the influence of state inertial. MCMC algorithm from hierarchical Bayes method is introduced to estimate the weights of factors in the logit model. Then a four-share structure by the given shares of dynamic ridesharing and a transition probability matrix given the previous supply are constructed respectively. The simulation experiment displays the relationship of optimal policy and total revenue, the relationship of DR with previous supply and total revenue, the difference of mode structure between the beginning and end of period and, influence of total revenue on mode supply probability.

Key words: travel model, dynamic ridesharing, state-dependent utility, dynamic programming model

侯立文. 城市出行结构优化的仿真研究[J]. 管理评论, 2021, 33(10): 289-296.

Hou Liwen. Simulation of Optimal Urban Travel Mode Structure[J]. Management Review, 2021, 33(10): 289-296.

参考文献

[1] 蒋恵园,黄永燊. 交通资源环境承载力预警研究[J]. 交通运输系统工程与信息, 2015,15(2):10-16
[2] Ambarwati L., Verhaeghe R., van Arem B., et al. The Influence of Integrated Space-Transport Development Strategies on Air Pollution in Urban Areas[J]. Transportation Research Part D, 2016,44:134-146
[3] Beirão G., Cabral J. A. S. Understanding Attitudes towards Public Transport and Private Car:A Qualitative Study[J]. Transport Policy, 2007,14(6):478-489
[4] Haywood L., Koning M., Monchambert G. Crowding in Public Transport:Who Cares and Why?[J]. Transportation Research Part A, 2017,100:215-227
[5] Anwar A. H. M. M., Yang J. Examining the Effects of Transport Policy on Modal Shift from Private Car to Public Bus[J]. Procedia Engineering, 2017,180:1413-1422
[6] Fahnenschreiber S., Gündling F., Keyhani M. H., et al. A Multi-Modal Routing Approach Combining Dynamic Ride-Sharing and Public Transport[J]. Transportation Research Procedia, 2016,13:176-183
[7] 曹辉. 关于提高城市公共交通分担率的思考[J]. 交通与运输(学术版), 2014,(1):110-113
[8] 吴倩. 城市轨道交通客流分担率模型分析[J]. 交通运输研究, 2009,(11):52-54
[9] 徐薇,范名才,徐红利. 多模式交通网络下考虑尾气排放的道路收费研究[J]. 系统工程理论与实践, 2016,36(9):2345-2354
[10] 许旺土,何世伟,宋瑞,等. 考虑拥挤收费的非集计交通方式分担模型研究[J]. 交通运输系统工程与信息, 2008,8(3):96-102
[11] 张岚,荣建,陈来荣,等. 北京城市道路拥挤收费可行性分析[J]. 交通运输系统工程与信息, 2006,6(2):124-128
[12] 李云辉,李飞,杨洋. 深圳市绿色交通三网融合的探索与实践[A]. 2017年中国城市交通规划年会论文集[C]. 北京:中国建筑工业出版社, 2017
[13] 李铁柱,林锦山,吴孟庭,等. 城市环境交通容量空间分析方法[J]. 东南大学学报(自然科学版), 2009,39(6):1274-1278
[14] Tørnblad S. H., Kallbekken S., Korneliussen S., et al. Using Mobility Management to Reduce Private Car Use:Results from a Natural Field Experiment in Norway[J]. Transport Policy, 2014,32:9-15
[15] 龙瀛,毛其智,杨东峰,等. 城市形态,交通能耗和环境影响集成的多智能体模型[J]. 地理学报, 2011,66(8):1033-1044
[16] 王贤. 城市公共交通与城市形态的互动关系研究——对无锡城市规划的启示[J]. 城市规划, 2007,31(7):85-88
[17] 杨励雅,邵春福,聂伟. 基于混合遗传算法的城市土地利用形态与交通结构的组合优化[J]. 上海交通大学学报, 2008,42(6):896-899
[18] 杨维. 城市土地利用与交通系统优化研究[J]. 铁道运输与经济, 2009,31(7):56-58
[19] Dubé J. P., Hitsch G. J., Rossi P. E., et al. Category Pricing with State-Dependent Utility[J]. Marketing Science, 2008,27(3):417-429
[20] Seetharaman P. B., Ainslie A., Chintagunta P. K. Investigating Household State Dependence Effects across Categories[J]. Journal of Marketing Research, November 1999,36:488-500
[21] Karni E. A Definition of Subjective Probabilities with State-Dependent Preferences[J]. Econometrica, 1993,61(1):187-198
[22] Allenby G. M., Rossi P. E. Marketing Models of Consumer Heterogeneity[J]. Journal of Econometrics, 1998,89(1-2):57-58
[23] Rossi P. E., Allenby G. M. Bayesian Statistics and Marketing[J]. Marketing Science, 2003,22(3):304-328