Research On Forecasting And Scheduling Of Shared Bicycle Demand Based On Data

Since the emergence of shared bicycles in 2016,it has provided people with a new way of green transportation.Due to the gradual improvement of people's living standards and the increasing awareness of environmental protection,in the face of a series of negative social impacts brought about by rapid economic growth,such as heavy traffic caused by heavy vehicle usage,traffic congestion,environmental degradation,and noise pollution,no doubt make There is an increasing demand for green and low-carbon travel.Bicycle sharing follows the trend,not only making a certain contribution to low-carbon environmental protection,but also alleviating the "last mile" public transportation "human transportation" problem to a certain extent.But at the same time,due to the spatial transfer of people in the process of using bicycles,bicycles cannot be evenly distributed in various regions and regions,which brings a series of transportation problems to the popularity of shared bicycles.Find the bicycles in a timely manner or stop and park in disorder to obstruct the traffic,etc.Therefore,systematic and reasonable scheduling of shared bicycles is an important method to solve the problem of uneven distribution of bicycles in time and space.This article first explains the shared bicycle system,and defines its functional requirements and research scope.Based on the travel data of Mobike's We Chat mini program in Nanjing during October-November 2018,the time and space laws for people using shared bicycle travel Carrying out research and using related technologies such as data mining and visualization,the analysis shows that the demand for shared bicycles has the characteristics of morning and evening peaks in daily use,and the duration of the peak period is about 1 hour.In addition,in terms of spatial location,bicycles are usually concentrated near public places such as subways and bus stops,and the concentration point of morning and evening peaks happens to be in the opposite state.According to the spatial and temporal distribution characteristics of shared bicycles,hierarchical clustering and k-means clustering are used to divide the shared bicycle scheduling area;then the BP neural network and RBF neural network prediction models are used to predict the shared bicycle scheduling area demand.Both neural network training results show that the predicted value is close to the actual value,and the prediction error is controlled within 5%,and by comparison,the RBF model has higher prediction accuracy in such problems.The accuracy rate of more than 80% of the dispatch area is greater than 90%,and the highest accuracy rate is 97%,and the model fits well;Then,based on the prediction data of the shared bicycle scheduling area,reasonable assumptions are made,a regional shared bicycle scheduling model with time window as the goal of cost minimization is established,and the constraints of the model are proposed according to the objective reality constraints;Finally,a genetic algorithm is designed to solve the scheduling model,and the real data of Mobike in Nanjing is used as an example to solve a more reasonable scheduling scheme.