Research On Pricing And Delivery Scheme Optimization Of Bike-Sharing Connecting To Urban Rail Transit

Under the new situation that the urban transportation structure is gradually transforming into smart and green,the bike-sharing,as an environment-friendly travel mode,has become an important means to solve the "last kilometer" travel problem of the urban rail transit system by virtue of its convenient and efficient service advantages.However,the relevant theoretical research and practical experience under the scenario of bike-sharing connecting to urban rail transit still need to be deepened,which is mainly reflected in the analysis of connecting demand characteristics,travel behavior and preferences,the impact on the transportation environment system,and refined operation management.This study responds to the requirements of green and low-carbon development of urban transportation.Based on the analysis of the spatial-temporal characteristics of bike-sharing connecting to subway,the bi-level programming optimization model is established to propose the optimal pricing and delivery scheme of connecting bike-sharing under the consideration of the travel mode choice behavior,so as to support reasonable station planning and high-quality operation of bike-sharing.First of all,the connecting ride data are identified based on the bike-sharing order data.The spatial-temporal characteristics such as the riding demand,time,and distance are analyzed from the overall,regional,and station distribution respectively.The results show that the connecting ride characteristics are significantly affected by date attributes,weather,and station location.The hot spots show a certain spatial agglomeration effect.The riding demand in the morning peak is more concentrated than that in the evening peak,attracting a wider range of services.The average riding distance and time increased during the night compared to daytime.Secondly,considering the connecting mode choice behaviors,a Stated Preference(SP)questionnaire is designed to obtain the respondents’ choice results under different service levels and travel scenarios,as well as their personal and attitude attributes.These provide support for subsequent mode choice model construction.The statistical analysis results show that groups of different gender,ages,education,and income levels have different preferences for the choice proportion of the combined travel of subway and bike-sharing,and the choice proportion is higher in the scenarios of commuting and medium distance(10-20km)trips.Thirdly,the Nested Logit(NL)travel mode choice model considering the connecting behavior is constructed.Moreover,considering the influence of travelers’ attitudes on choice behavior,the Hybrid Choice Model(HCM)is built based on the structural equation model with latent attitude variables.The results show that the travel scenario(travel purpose and distance),transportation service level(time,cost,etc.),personal attributes(sex,age,income,education,occupation),and latent variables(social benefits and service level attitude)significantly impact on travel mode choice.Fourthly,based on the constructed travel mode choice model to describe the competition and cooperation relationship between modes,taking the metro stations’ connecting bike-sharing as the research object,a multi-objective bilevel programming model considering elastic demand is constructed to realize the joint optimization of the pricing and the delivery scheme.The upper layer of the model is designed to maximize the profit of the enterprise and the environmental improvement benefit of the transportation system.The lower layer uses the variational inequality model to describe the balanced distribution of the travel mode demand.The genetic algorithm and successive weighted average algorithm are designed to solve the optimization model.Finally,taking Beijing Chegongzhuang subway station as a case study,based on the constructed bilevel programming model,the optimization schemes are solved and compared respectively for two scenarios that only consider the benefits of bike-sharing enterprise and comprehensively consider the overall public transportation enterprises.The optimization results show that when the overall public transportation enterprises are considered,the optimal pricing is lower.The connecting ride demand increases by26.9% compared with the current situation.There is more demand attracted to the rail transit system to achieve greater overall revenue.Meanwhile,the carbon emission of the transportation system is reduced by about 2 tons.The optimization model established in this study implements the development concept of low-carbon transportation.It also provides a reference for enterprises to optimize pricing level and delivery operation strategy,so as to further improve the service level of connecting bike-sharing.