Carbon Emission Measurement And Supply Optimization Of Free-Floating Bike Sharing

As an important part of low-carbon transportation system,free-floating bike sharing provides new options to meet the demand of short-distance trip and public transport connection,and has been developed rapidly in China.However,in the course of development,free-floating bike sharing system has excessive investment and waste of resources.Few studies estimate carbon emission reduction benefits of free-floating bike sharing from the perspective of life cycle.Based on multi-source data,this paper analyzes the travel characteristics and users’travel preferences,and establishes the supply optimization model of free-floating bike sharing on the basis of life cycle carbon emission calculation and demand forecasting.Firstly,using the GIS platform to integrate the geographic information data of Nanjing and the historical riding data of Mobike,the travel characteristics of free-floating bike sharing are explored.Based on the travel intention survey,the characteristics of users under the two service modes which are using for short-distance travel and public transport connection are analyzed.Then,this paper constructs the selected behavior model of free-floating bike sharing service mode,and discusses the substitution effect of free-floating bike sharing.Secondly,based on the life cycle assessment method,the system boundary of free-floating bike sharing is determined.Considering the four phases of production,use,operation,and disposal,the carbon emissions behaviors in different phases are defined,and the carbon emission measurement model of free-floating bike sharing is constructed with the life cycle database and Open LCA software.It was found that a total of 76.686 kg CO₂-eq was generated per bike.And most respondents only use free-floating bike sharing for short-distance trip with saving 63.726 g CO₂-eq/p·km,while the use pattern of connecting public transport could better replace the motorized transport trip and generate better low-carbon benefits with saving300.718 g CO₂-eq/p·km.Sensitivity analysis shows that different service modes and enterprise supply of free-floating bike sharing have a great impact on the carbon emission reduction effect of free-floating bike sharing.Then,taking Gulou district of Nanjing as an example,according to the distribution of free-floating bike sharing,K-means clustering algorithm is used to divide traffic zones.On this basis,the historical average method,long-short term memory neural network and random forest model are used to predict the demand of free-floating bike sharing.The RMSE and MAE indexes are used to evaluate the prediction results of these models.It is found that the random forest model has better prediction effect.When external factors such as weather data and POI data are introduced,the prediction accuracy can be improved.Finally,considering the usage demand of users,the carbon emissions of free-floating bike sharing and the operating costs of enterprises,under the constraints of the maximum supply amount of free-floating bike sharing,the number of available shared bikes,and the interval distance,a multi-objective optimization model of free-floating bike sharing is established.Taking Gulou district of Nanjing as an example,the genetic algorithm is used to solve the model.Considering the market share of the enterprise,the satisfaction rate of user demand is 80.87%,and the carbon emission reduction is 464.354 t,and enterprise cost is reduced by 31.96%.