Real-time Service Robustness And Norm-limit Intelligent Control Of Shared Bicycles

As a convenint way for short-distance travel,the bike-sharing system helps to allieviate traffic congestion and reduce carbon emission,which makes it plays an increasingly important role in the public transportation systems of many countries.In particular,the bike-sharing system provides urgent transportations during the pandemic of COVID-19.However,due to its massive scale,its operations and management remain a challenge.Based on the operation and management problems of bike-sharing systems,in this paper,we focus on the tidal phenomenon,robustness,dynamic rebalancing demand control of the docked bike-sharing system,and the dynamic rebalancing demand and location and allocation of the undocked bike-sharing system.Based on the real-time cycling data of public bike company,Mobike company,and Qingju company in Nanjing,we conduct empirical analyses of docked and undocked bike-sharing system.This paper consists of the following parts:(1)The spatio-temporal characteristics of cycling behavior of docked bike-sharing systems.Based on the tide phenomenon,we propose a coarse-grained method to construct the vector field of overall cycling behavior from the perspective of the vector.Combined with the theory of temporal network,we put forward the index and method to describe the spatial and temporal characteristics of cycling behavior and conduct empirical analysis based on 980,161 real-time cycling data of public bike company in Nanjing from 2017/3/20 to 2017/3/26.The empirical results show that in docked bike-sharing systems,the behavior of borrowing and returning shared bikes is consistent in time distribution,and they can be investigated together under the same temporal window.Besides,we find that the accumulation of the overall cycling frequency per unit time is the internal reason for the formation of the tidal phenomenon of cycling behavior under equal time intervals.That is,according to the spatio-temporal characteristics of cycling behavior from the vector perspective,a new temporal window can be constructed,which is an effective tool to solve the problems that caused by the tide phenomenon to the operation and management of docked bike-sharing systems.(2)The robustness and early warning of docked bike-sharing systems.We construct a new index to describe the real-time service of the docked bike-sharing systems.To describe the impact of cycling behavior on the real-time service of the system under regular operation,we propose a robust strategy and complete the robustness analysis based on 980,161 real-time cycling data on 40,101 stations of public bike company in Nanjing.Moreover,according to robustness analysis,we establish a method of calculating site thresholds to provide early warning of the real-time service of the docked shared bike system.The empirical results show that the system is robust,and the upper and lower limits of the station threshold representing the warning value are0.82 and 0.19,respectively.Under the new station threshold,the number of stations with rebalancing demand under the flow windows in a one-week will decrease by2.18%.(3)Norm-limit intelligent control for rebalancing demand of docked bike-sharing systems.Based on the research results of the tide phenomenon and station threshold in the previous two chapters,we first introduce the leek harvesting principle and propose a method to accurately control the upper limit of spatial scale by converting leek function harvesting mode.Then,we propose a method to control the lower limit of the dynamic window by changing the growth environment of the leek function.Finally,we construct a new norm-limit intelligent control method that combines structural control and spatiotemporal impulse control.At the same time,we build a nonlinear optimization model to calculate the optimal control parameters,and complete the empirical analysis by the large-scale optimization model based on 130679 real-time cycling data from a public bike company in Nanjing on March 20,2017.The results show that the new norm-limit intelligent control can control the dynamic rebalancing demand of the docked bike-sharing systems,and promote the matching between the limited rebalancing ability of the Nanjing public bike company and the dynamic rebalancing demand of the docked bike-sharing systems.(4)The local dynamic characteristics of rebalancing demand of undocked bike-sharing systems.Based on the square grid,we propose a visualization method and an index of rebalancing requirements.Through a new coarse-grained method,we construct a way and procedure to analyze the local dynamic characteristics of the rebalancing demand of the undocked bike-sharing systems.Based on 1,446,669real-time cycling data of Mobike company in Nanjing,we complete the empirical analysis.The results show that the commuting behavior during the rush time is not the key factor that constitutes the rebalancing demand of the undocked bike-sharing system.Moreover,the configuration optimization of undocked shared bikes can alleviate the rebalancing demand on the small-scale grid.(5)The grid management optimization strategy of undocked bike-sharing systems.Based on the research results of the previous chapter,we introduce the honeycomb grid,use walking tolerance limit to construct the upper limit of the European distance of the cycling demand transfer,and use the cycling OD route to construct the complex network between the honeycomb grids.The grid parking density limits the occupancy of public areas by the undocked shared bike system.We built a new station location and allocation model for the undocked bike-sharing system,and complete the empirical analysis based on the 1,446,669 real-time cycling data of Mobike company from 2017/3/20 to 2017/3/26 and the 3,007,999 real-time cycling data of Qingju company in June and October 2020.The results show that the new model can effectively reduce the area occupied by the undocked bike-sharing system in the public areas without losing the cycling demand,and has good promotion value.