Research on Demand Prediction and Scheduling Methods for Shared Bicycles Based on Deep Learning
As a convenient and environmentally friendly means of transportation,shared bicycles are increasingly favored by more and more people.However,with the popularization and increasing demand for shared bicycles,how to accurately predict user needs and schedule vehicles reasonably has become an important issue.To solve this problem,firstly,through data collection and preprocessing,a large amount of shared bicycle usage data is obtained and normalized.Then,a deep learning model is used to establish an accurate demand prediction model,and the model is trained through training data.For the design of scheduling algorithms,this article proposes static scheduling strategies,dynamic scheduling strategies,and combined scheduling strategies,and designs corresponding scheduling algorithms.Finally,the collaborative optimization of demand forecasting and scheduling,as well as the optimization of shared bicycle resource management strategies,are achieved through comprehensive optimization methods.
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