离散选择高阶段Logit模型的建模与预测——以上海城市外围大型居住社区为例
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摘要
离散选择Logit系列模型的发展层次顺序是:二项、多项、巢式与交叉巢式、混合Logit(连续及离散潜在种类模型)、嵌入潜在变量的混合选择模型、动态Logit模型。建立高阶段Logit模型算法是当前研究快速城市化进程中居民交通方式选择与预测的最新技术手段。2000年,Daniel M教授,以"对分析离散选择的原理和方法所做出的发展和贡献"获诺贝尔经济学奖。2012—2014年,作者深入学习了离散选择Logit系列模型及标定预测,结合上海城市外围大型居住社区在中国"十二·五"期间,快速城市化进程中的演变,开展了基于高阶段Logit建模算法研究,取得了理想的成果。针对高阶段Logit模型(混合Logit模型、混合选择模型、动态Logit模型)进行了建模、算法、选择概率与消费者剩余预测的理论分析,建立了上海市外围大型居住社区居民交通方式高阶段选择模型。结果显示,6种政策方案中,附近设商业能最大改善出行质量并保证公交优先。高阶段Logit模型理论实际结合后,能很好地发挥其为政策方案决策定量支持之作用。
The series of discrete choice Logit models,from basic to advanced are: binominal,multinomial,nested,cross nested,mixed Logit model,latent class model and hybrid choice model with latent variables and dynamic Logit model.Developing and using advanced Logit models is the latest key technique when the residents'mode choice is studied in the rapid urbanization process.As a professor at University of California,Berkeley,Daniel L Mc Fadden contributes to the discrete choice theory and method and won the Nobel Economic Prize in 2000.From year 2012 to 2014,the author worked with Joan Walker and Kenneth Train seized the opportunity of learning the series of Logit models in-depth.With theory applying to practice,this paper presents the advanced Logit models and the results in the case of the large scale residential area on Shanghai city periphery in the period of Chinese"the Twelfth Five-Year Plan"and the process of rapid urbanization.With the data from the residents in the large scale residential area on Shanghai city periphery,an advanced Logit mode choice model's estimation and forecast are developed.Results show that in the six policy scenarios,building retails closer improves the residents'travel quality the most and supports transit priority.Combining theory with practice,advanced Logit model can support policy decision making process quantitatively and excellently.
The series of discrete choice Logit models,from basic to advanced are: binominal,multinomial,nested,cross nested,mixed Logit model,latent class model and hybrid choice model with latent variables and dynamic Logit model.Developing and using advanced Logit models is the latest key technique when the residents'mode choice is studied in the rapid urbanization process.As a professor at University of California,Berkeley,Daniel L Mc Fadden contributes to the discrete choice theory and method and won the Nobel Economic Prize in 2000.From year 2012 to 2014,the author worked with Joan Walker and Kenneth Train seized the opportunity of learning the series of Logit models in-depth.With theory applying to practice,this paper presents the advanced Logit models and the results in the case of the large scale residential area on Shanghai city periphery in the period of Chinese"the Twelfth Five-Year Plan"and the process of rapid urbanization.With the data from the residents in the large scale residential area on Shanghai city periphery,an advanced Logit mode choice model's estimation and forecast are developed.Results show that in the six policy scenarios,building retails closer improves the residents'travel quality the most and supports transit priority.Combining theory with practice,advanced Logit model can support policy decision making process quantitatively and excellently.
引文
[1]Train K.Discrete choice models with simulation[M].Cambridge:Cambridge University Press,2002.
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[5]Mc Fadden D.Conditional Logit analysis of qualitative choice behavior[M]//Frontiers in Econometrics.New York:Academic Press,1974:105-142.
[6]Mc Fadden D.Economic choices[J].American Economic Review,2001,91:351-378.
[7]Ben-Akiva M.The structure of travel demand models[D].MIT,1972.
[8]Train K.Qualitative choice analysis[M].Cambridge,MA:MIT Press,1986.
[9]Train K,Mc Fadden D,Ben-Akiva M.The demand for local telephone service:A fully discrete model of residential calling patterns and service choice[J].Rand Journal of Economics,1987,18:109-123.
[10]Mc Fadden D,Train K.Mixed mnl models of discrete response[J].Journal of Applied Econometrics,2000,15:447-470.
[11]Walker J.Extended discrete choice models:Integrated framework,flexible error structures,and latent variables[D].MIT,2001.
[12]Lewbel A,Mc Fadden D,Linton O.Estimating features of a distribution from binomial data[J].Journal of Econometrics,2011,162(2):170-188.
[13]Fosgerau M,Mc Fadden D,Bierlaire M.Choice probability generating functions[J].Journal of Choice Modelling,2013,8(9):1-18.
[14]Guan J,Yang D.Residents’characteristics and transport policy analysis in large scale residential areas on the city periphery:A case study of Jinhexincheng,Shanghai,China[C]//Proceedings of the 94th Annual Meeting of the Transportation Research Board,Washington D C,2015.
[15]Guan J,Yang D.Travel behavior of two major groups in large scale residential areas in the periphery of Shanghai:A case study of Jinhexincheng,Jiading District[C]//Proceedings of the 92nd Annual Meeting of the Transportation Research Board,Washington D C,2013.
[16]Guan J.Travel characteristics of different social groups in large scale residential areas in the periphery of shanghai:A case study of Jinhexincheng[C]//Proceedings of the 12th Chinese Overseas Transportation Association International Conference of Transportation Professionals(CICTP 2012),Beijing,China,2012.
[17]Guan J,Zhang P.Shanghai periphery large scale residential area residents commuting characteristics:A case study of Jinhexincheng,Jiangqiao,Jiading District[C]//Proceedings of 2011 Annual Meeting of China Urban Transport Planning,Wuhan,China,2011.
[18]Guan J.Mode choice of residents in large scale residential areas in the periphery of Shanghai:A case study of Jinhexincheng,Jiading District[C]//Proceedings of ICTPA,2015.
[19]Bierlaire M.BIOGEME:A free package for the estimation of discrete choice models[C]//Proceedings of the3rd Swiss Transportation Research Conference,Ascona,Switzerland,2003.
[2]Luce D.Individual choice behavior[M].New York:John Wiley and Sons,1959.
[3]Marschak J.Binary choice constraints on random utility indications[M]//Stanford Symposium on Mathematical Methods in the Social Science.California:Stanford University Press,1960:312-329.
[4]Luce D,Suppes P.Preferences,utility and subjective probability[M]//Handbook of Mathematical Psychology.New York:John Wiley and Sons,1965:249-410.
[5]Mc Fadden D.Conditional Logit analysis of qualitative choice behavior[M]//Frontiers in Econometrics.New York:Academic Press,1974:105-142.
[6]Mc Fadden D.Economic choices[J].American Economic Review,2001,91:351-378.
[7]Ben-Akiva M.The structure of travel demand models[D].MIT,1972.
[8]Train K.Qualitative choice analysis[M].Cambridge,MA:MIT Press,1986.
[9]Train K,Mc Fadden D,Ben-Akiva M.The demand for local telephone service:A fully discrete model of residential calling patterns and service choice[J].Rand Journal of Economics,1987,18:109-123.
[10]Mc Fadden D,Train K.Mixed mnl models of discrete response[J].Journal of Applied Econometrics,2000,15:447-470.
[11]Walker J.Extended discrete choice models:Integrated framework,flexible error structures,and latent variables[D].MIT,2001.
[12]Lewbel A,Mc Fadden D,Linton O.Estimating features of a distribution from binomial data[J].Journal of Econometrics,2011,162(2):170-188.
[13]Fosgerau M,Mc Fadden D,Bierlaire M.Choice probability generating functions[J].Journal of Choice Modelling,2013,8(9):1-18.
[14]Guan J,Yang D.Residents’characteristics and transport policy analysis in large scale residential areas on the city periphery:A case study of Jinhexincheng,Shanghai,China[C]//Proceedings of the 94th Annual Meeting of the Transportation Research Board,Washington D C,2015.
[15]Guan J,Yang D.Travel behavior of two major groups in large scale residential areas in the periphery of Shanghai:A case study of Jinhexincheng,Jiading District[C]//Proceedings of the 92nd Annual Meeting of the Transportation Research Board,Washington D C,2013.
[16]Guan J.Travel characteristics of different social groups in large scale residential areas in the periphery of shanghai:A case study of Jinhexincheng[C]//Proceedings of the 12th Chinese Overseas Transportation Association International Conference of Transportation Professionals(CICTP 2012),Beijing,China,2012.
[17]Guan J,Zhang P.Shanghai periphery large scale residential area residents commuting characteristics:A case study of Jinhexincheng,Jiangqiao,Jiading District[C]//Proceedings of 2011 Annual Meeting of China Urban Transport Planning,Wuhan,China,2011.
[18]Guan J.Mode choice of residents in large scale residential areas in the periphery of Shanghai:A case study of Jinhexincheng,Jiading District[C]//Proceedings of ICTPA,2015.
[19]Bierlaire M.BIOGEME:A free package for the estimation of discrete choice models[C]//Proceedings of the3rd Swiss Transportation Research Conference,Ascona,Switzerland,2003.