Forecast Of Shared Bicycle Rides Based On Weather Factors

In recent years,shared bicycles have been rising rapidly,but there are many problems in the development process,especially the mismatch between supply and demand of bicycles,which results in resource mismatch and waste.In this paper,based on the weather indicators,the riding indicators of regional bicycles are predicted.In the research,the ride volume is selected as the riding indicators,and the temperature and rain are selected as the weather indicators.Neural networks based on machine learning have many advantages: learning ability,generalization ability,high adaptability and nonlinear mapping ability.By comparing with several traditional methods such as linear regression and moving smoothing method,this paper selects BP neural network for prediction Research and build a model to conduct empirical research on the cycling data of the Jianshe Road area of Chenghua District,Chengdu.Through the comparative study of network parameters,the network structure is gradually optimized,and the input indicators are optimized to improve the effectiveness of the network model.with this optimization model,This paper made a prediction study on the riding volume with the "day" as the granularity,and the prediction performance was verified by the five-fold cross-validation method.Compared with the multiple linear regression model and ARIMA model,the results shows that the quantitative prediction performance of the BP neural network is significantly better than the latter models.Finally,the cycling characteristics of the cycling data with "hour" as the granularity were studied based on the spatio-temporal variables,and the influence characteristics of the weather factors on the ride volume at different locations or different periods in the region were quantitatively analyzed.The ride volume per hour at 8 typical locations or the interval between 06:00~23:59 was predicted and compared.The research shows that the characteristics of different locations affected by weather factors vary greatly,so as the characteristics of different periods of time,These research results provide a more detailed reference for the vehicle delivery and scheduling management of shared bicycle companies.