基于出行行为分析的地铁通勤票价折扣研究

Fare discount study for urban rail transit commuting based on travel behavior analysis

针对当前城市交通拥堵问题,对轨道交通票价的合理设定进行研究,以提升公共交通吸引力和缓解财政压力.首先,基于正交设计法设计问卷并开展意向调查(Stated Preference,SP),构建基于巢式Logit(Nested Logit,NL)模型的通勤者出行方式选择模型;其次,通过分析通勤通道内地面公交、地铁接驳公交以及地铁等公交票价对小汽车和出租车的分担率的交叉弹性,选用地铁票价作为优惠对象;再次,基于Pareto最优理论,以公共交通分担率和公共交通票务收入的双提升为目标,构建变动需求下的通勤地铁票价折扣模型;最后,以天通苑-北京五环内市辖区通勤通道为例,采用非支配排序算法(Non-dominated Sorting Algorithms,NSA)确定地铁票价的合理折扣范围.研究结果表明:当地铁票价的优惠幅度介于7%~13%之间时,可实现Pareto最优态,当地铁票价的优惠幅度为13%时,通勤通道地铁及地面公交分担率的最大涨幅为6.1%.

In response to the current urban traffic congestion issue,this research delves into the rational setting of urban rail transit fares to enhance the appeal of public transportation and alleviate financial pressure.Firstly,a questionnaire is designed based on the orthogonal design method and a Stated Pref-erence(SP)is conducted to establish a commuter travel mode selection model based on the Nested Logit(NL)model.Secondly,by analyzing the cross elasticity of the fares for ground buses,subway connecting buses,and subway within the commuting corridor concerning the sharing ratio of cars and taxis,the subway fare is selected as the discounted item.Thirdly,guided by the Pareto optimality theory and targeting the dual improvement of public transit share and ticket revenue,a model for dis-counted subway fares under changing demand conditions is formulated.Finally,taking the example of commuting corridor between Tiantongyuan and downtown Beijing(within 5th ring road),a Non-dominated Sorting Algorithm(NSA)is employed to determine the reasonable discount range for sub-way fares.Results indicate that achieving Pareto optimality is possible when the subway fare discount falls within the range of 7%to 13%;with a 13%discount,the maximum increase in the subway and ground transit sharing rate within the commuting corridor is 6.1%.