Abstract
While online car-hailing provides fast and convenient services, it has the same problems in the process of ‘seeking customers’ as traditional taxis. With the research goal of the demand for online ride-hailing, an online ride-hailing demand forecast model based on G & LSTM is proposed. Firstly, grey relational degree analysis and other methods are used to analyze which and how the relevant factors affect the demand for online car-hailing. Secondly, the demand prediction model of online car-hailing based on GRU&LSTM is trained an adjusted. Through the adjustment and comparison experiment, the values of each parameter in the model are properly adjusted. Finally, based on the running data of Chengdu, the model proposed is verified and evaluated. The experimental results show that the GRU&LSTM prediction model has good prediction effect. By comparing the prediction effect of weighting data and unweighting data, it can be seen that weighted data has better prediction effect.
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