Demand Prediction of Shared Bikes Based on Similar Days from the Perspective of Public Safety
Accurate prediction of the demand for shared bikes at each station can improve the efficiency of shared bike management and distribution,and effectively prevent the risk of public order caused by the imbalance of supply and demand.By comprehensively considering the influence of meteorological characteristics,time characteristics and historical data on the demand,a model based on similar days and PSO-Elman neural network was proposed.Firstly,the influence of time characteristics on bicycle demand was studied and the time characteristics were screened.Then,Pearson correlation coefficient was used to verify and select the key meteorological characteristics affecting the demand.Then the grey relational degree algorithm was used to calculate the similarity between the historical data and the day to be predicted and select the similar day.Finally,combined with similar daily data and historical data,the PSO-Elman neural network prediction model was constructed to simulate and forecast the demand of bicycles in peak hours.The results showed that compared with Elman and the bicycle demand prediction model which did not consider the meteorological characteristics and time characteristics comprehensively,the prediction results of the proposed model had higher accuracy.
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