Optimal electric bus frequency setting combining LSTM prediction and two-layer planning

As an environmentally friendly and ef icient public transport, the optimization of the operating frequency of electric buses is of great signi icance for improving passenger satisfaction and reducing operating costs.This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning.First, LSTM neural network is utilized to predict the passenger low of electric buses.Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting.Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting.The inbound and outbound passenger low data of the 5th station of 363 electric bus in Q city are used for practical veri ication.The prediction results of the LSTM model on inbound and outbound passenger low on weekdays and natural days are basically consistent with the actual values.The optimal frequency of 62 trips was solved using genetic algorithm.The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is veri ied under the scenario of thousands of passenger lows.From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and twolayer planning model.