铁路列车席位控制理论和方法研究
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摘要
摘要：随着我国高速铁路的路网规模逐步扩大和运营里程的增加,铁路旅客运输能力得到逐步释放,铁路旅客运输供不应求的局面得到缓解,铁路运输生产正逐步由粗放型向精细化转换。如何更加充分利用铁路客运能力,提高列车上座率和运营收益已成为铁路客运部门亟待解决的重要问题之一。本论文在借鉴既有研究成果的基础上,结合中国铁路旅客运输的特点,对铁路列车席位控制的相关问题展开研究,主要进行了以下五项工作：
1)给出了铁路列车席位控制的一般模型。在对国内、外有关席位控制问题现状进行分析的基础上,总结了中国铁路列车席位控制的特点,给出了铁路列车席位控制的一般模型,并给出求解思路。
2)提出了面向不同研究需要的铁路短时客流预测方法。首先,针对于确定性的单列车动态席位控制问题研究的需要,研究了基于既有客票数据挖掘的客流预测方法,根据时间序列原理,利用ARIMA模型进行需求预测。然后,针对于随机需求多列车动态席位控制研究的需要,研究了面向个体的基于旅客选择行为的客流预测方法,利用铁路客票数据,通过Logit模型对旅客选择行为进行研究,并对旅客的异质性进行分析,得出目前乘坐京沪高铁的旅客时间价值约为100元/h。最后,对预售期内旅客购票行为进行了研究,得出旅客购票过程是一个非齐次泊松过程,并对该过程进行仿真,为动态席位控制提供基础。
3)构建了铁路单一票价下单列车非嵌套、嵌套和基于投标价格三种策略的滚动席位控制方法。首先基于独立需求,建立了单一票价下单列车非嵌套、嵌套和基于投标价格策略的席位控制的确定性多阶段模型和随机性多阶段模型。然后,基于仿真和求解软件Lingo12.0,给出不同分配策略的求解方法。最后,分别通过既有线上的列车和高速铁路列车对不同模型的控制效果进行了比较,给出不同分配策略的适用条件。
4)构建了铁路单一票价下多列车基于旅客出行选择行为的席位控制模型,并设计了列生成算法进行求解。首先,对单一票价下多列车旅客选择行为进行分析。其次,建立多列车席位控制的动态模型。然后,由于模型的变量数巨大,设计了列生成算法进行求解,求解过程中考虑了列车收益、客座率、满足的旅客数量以及计算时间开销等多个目标。最后,通过算例对模型进行了验证。
5)构建了多等级票价下多列车席位控制模型,设计了列生成算法进行求解。首先,对多等级票价下旅客的选择行为进行了分析。其次,建立了多等级票价下席位控制的动态模型。接着,给出列生成算法进行求解。然后,通过算例对模型和算法进行了验证。最后,提出多等级票价下席位控制经营上的建议。
ABSTRACT:With the gradual expansion of network scale and the increase in operating mileage of Chinese high-speed railway, railway passenger transport capacity will be gradually released and the shortage of railway passenger transportation situation will be eased. Production of rail transportation gradually shifts from extensive to intensive. Under these circumstances, how to fully take advantage of the railway passenger capacity, improve train load factors and operating revenue has become one of the important problems faced by railway passenger transportation sectors. Based on the existing studies and combined with the characteristics of the Chinese railway passenger transportation, this research mainly focuses on railway seat inventory control issues, and the main work in this thesis could be summarized as follows.
(1) We constructed a general model for train seat inventory control problems. The characteristics of the Chinese railway passenger transportation were first summarized based on analyzing related research of Chinese and foreign studies. After that, a general model was established for train seats inventory control problems, meanwhile, the solution approaches were discussed.
(2) This thesis proposed railway passenger transport short-term demand forecasting methods for different research needs. Firstly, for the research of the deterministic single train seats inventory control, ARIMA model was put forward based on historical passenger ticket booking data mining. Secondly, for the research of multi-trains stochastic demand seats inventory controls, a demand forecasting method of individual-oriented based on passenger choice behavior was proposed. Passenger ticket booking data was mined by Logit model, meanwhile, the heterogeneity of passenger choice behavior was analyzed, and the result showed that passengers'time value is around100Yuan per hour for those who take Beijing-Shanghai high-speed trains. Finally, the behavior of passenger ticket booking was analyzed in a ticket pre-sale period, and the result showed that passengers booking process is a non-homogeneous Poisson process. After that, the simulation approach of the process was proposed. The research of demand forecasting is the basis of the proposed seats inventory control methods.
(3) We further constituted seat inventory control methods based on three strategies, non-nested, nested, and bid price for a single train under single price. Firstly, based on independent demand, a deterministic multi-stage model and a stochastic multi-stage model were established for a single train under single price based on the non-nested, nested, and bid price strategies. Secondly, based on simulation and the software Lingo12.0, algorithms for different control strategies were given. Finally, a comparison of different control methods was made by the train on traditional lines and high-speed lines, and the applicable conditions of the control strategy were given.
(4) Moreover, this paper built a seats inventory control model based on passenger choice behavior for multi-train under single price. Meanwhile, a column generation algorithm was designed to solve the model. Firstly, passenger choice behavior was analyzed for multi-train under a single price. Secondly, a dynamic model of the multi-train seats inventory control was established. Thirdly, with the huge number of variables in the model, a column generation algorithm was proposed for solving the problem. In the solution process, multi-objectives were taken into account, including revenue, train load factor, passengers satisfied as well as computation time overhead. Finally, the model was validated by an example.
(5) Finally, we proposed a seat inventory control model for multi-train under multi-price. Meanwhile, a column generation algorithm was designed to solve the model. Firstly, passengers'choice behavior was discussed under multi-price. Secondly, a dynamic seat inventory control model for multi-train under multi-price was established. Thirdly, a column generation algorithm was given to solve it. Fourthly, the model and algorithm was validated by an illustrative example. Finally, managerial insights were proposed for multi-price seats inventory control strategies.
1)给出了铁路列车席位控制的一般模型。在对国内、外有关席位控制问题现状进行分析的基础上,总结了中国铁路列车席位控制的特点,给出了铁路列车席位控制的一般模型,并给出求解思路。
2)提出了面向不同研究需要的铁路短时客流预测方法。首先,针对于确定性的单列车动态席位控制问题研究的需要,研究了基于既有客票数据挖掘的客流预测方法,根据时间序列原理,利用ARIMA模型进行需求预测。然后,针对于随机需求多列车动态席位控制研究的需要,研究了面向个体的基于旅客选择行为的客流预测方法,利用铁路客票数据,通过Logit模型对旅客选择行为进行研究,并对旅客的异质性进行分析,得出目前乘坐京沪高铁的旅客时间价值约为100元/h。最后,对预售期内旅客购票行为进行了研究,得出旅客购票过程是一个非齐次泊松过程,并对该过程进行仿真,为动态席位控制提供基础。
3)构建了铁路单一票价下单列车非嵌套、嵌套和基于投标价格三种策略的滚动席位控制方法。首先基于独立需求,建立了单一票价下单列车非嵌套、嵌套和基于投标价格策略的席位控制的确定性多阶段模型和随机性多阶段模型。然后,基于仿真和求解软件Lingo12.0,给出不同分配策略的求解方法。最后,分别通过既有线上的列车和高速铁路列车对不同模型的控制效果进行了比较,给出不同分配策略的适用条件。
4)构建了铁路单一票价下多列车基于旅客出行选择行为的席位控制模型,并设计了列生成算法进行求解。首先,对单一票价下多列车旅客选择行为进行分析。其次,建立多列车席位控制的动态模型。然后,由于模型的变量数巨大,设计了列生成算法进行求解,求解过程中考虑了列车收益、客座率、满足的旅客数量以及计算时间开销等多个目标。最后,通过算例对模型进行了验证。
5)构建了多等级票价下多列车席位控制模型,设计了列生成算法进行求解。首先,对多等级票价下旅客的选择行为进行了分析。其次,建立了多等级票价下席位控制的动态模型。接着,给出列生成算法进行求解。然后,通过算例对模型和算法进行了验证。最后,提出多等级票价下席位控制经营上的建议。
ABSTRACT:With the gradual expansion of network scale and the increase in operating mileage of Chinese high-speed railway, railway passenger transport capacity will be gradually released and the shortage of railway passenger transportation situation will be eased. Production of rail transportation gradually shifts from extensive to intensive. Under these circumstances, how to fully take advantage of the railway passenger capacity, improve train load factors and operating revenue has become one of the important problems faced by railway passenger transportation sectors. Based on the existing studies and combined with the characteristics of the Chinese railway passenger transportation, this research mainly focuses on railway seat inventory control issues, and the main work in this thesis could be summarized as follows.
(1) We constructed a general model for train seat inventory control problems. The characteristics of the Chinese railway passenger transportation were first summarized based on analyzing related research of Chinese and foreign studies. After that, a general model was established for train seats inventory control problems, meanwhile, the solution approaches were discussed.
(2) This thesis proposed railway passenger transport short-term demand forecasting methods for different research needs. Firstly, for the research of the deterministic single train seats inventory control, ARIMA model was put forward based on historical passenger ticket booking data mining. Secondly, for the research of multi-trains stochastic demand seats inventory controls, a demand forecasting method of individual-oriented based on passenger choice behavior was proposed. Passenger ticket booking data was mined by Logit model, meanwhile, the heterogeneity of passenger choice behavior was analyzed, and the result showed that passengers'time value is around100Yuan per hour for those who take Beijing-Shanghai high-speed trains. Finally, the behavior of passenger ticket booking was analyzed in a ticket pre-sale period, and the result showed that passengers booking process is a non-homogeneous Poisson process. After that, the simulation approach of the process was proposed. The research of demand forecasting is the basis of the proposed seats inventory control methods.
(3) We further constituted seat inventory control methods based on three strategies, non-nested, nested, and bid price for a single train under single price. Firstly, based on independent demand, a deterministic multi-stage model and a stochastic multi-stage model were established for a single train under single price based on the non-nested, nested, and bid price strategies. Secondly, based on simulation and the software Lingo12.0, algorithms for different control strategies were given. Finally, a comparison of different control methods was made by the train on traditional lines and high-speed lines, and the applicable conditions of the control strategy were given.
(4) Moreover, this paper built a seats inventory control model based on passenger choice behavior for multi-train under single price. Meanwhile, a column generation algorithm was designed to solve the model. Firstly, passenger choice behavior was analyzed for multi-train under a single price. Secondly, a dynamic model of the multi-train seats inventory control was established. Thirdly, with the huge number of variables in the model, a column generation algorithm was proposed for solving the problem. In the solution process, multi-objectives were taken into account, including revenue, train load factor, passengers satisfied as well as computation time overhead. Finally, the model was validated by an example.
(5) Finally, we proposed a seat inventory control model for multi-train under multi-price. Meanwhile, a column generation algorithm was designed to solve the model. Firstly, passengers'choice behavior was discussed under multi-price. Secondly, a dynamic seat inventory control model for multi-train under multi-price was established. Thirdly, a column generation algorithm was given to solve it. Fourthly, the model and algorithm was validated by an illustrative example. Finally, managerial insights were proposed for multi-price seats inventory control strategies.
引文
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