Chinese Journal of Network and Information Security ›› 2020, Vol. 6 ›› Issue (6): 152-163.doi: 10.11959/j.issn.2096-109x.2020085

• Papers • Previous Articles     Next Articles

Charging pile recommendation method for idle electric taxis based on recurrent neural network

Jian JIA(),Linfeng LIU,Jiagao WU   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Revised:2020-07-03 Online:2020-12-15 Published:2020-12-16
  • Supported by:
    The National Natural Science Foundation of China(61872191)


A charging pile recommendation method for idle electric taxis (CPRM-IET) based on recursive neural network was proposed to recommend the optimal charging piles for idle electric taxis.Usually,the movement of each idle electric taxi depends on the subconscious movement tendency and driving habits of the driver.Therefore,it is necessary to predict the future movement based on its historical movement trajectories,so as to find the charging piles with the least extra movements.In CPRM-IET,a dual-stage attention-based recurrent neural network (DA-RNN) model was provided to predict the future trajectories of electric taxis.DA-RNN model includes two types of attention mechanisms which are input attention mechanism and temporal attention mechanism.The input attention mechanism assigns different weights to the input driving sequence at each time slot,and the temporal attention mechanism assigns weights to the hidden state of the encoder.Based on the predicted future trajectories,several charging piles with the least extra movements were selected and recommended for the idle electric taxis.The simulation results show that CPRM-IET can achieve preferable results in terms of charging extra movement and root mean square error,which reflects that CPRM-IET can accurately predict the future trajectories of idle electric taxis and recommend optimal charging piles for these electric taxis.

Key words: charging pile recommendation, recurrent neural network, input attention mechanism, time attention mechanism, trajectory prediction

CLC Number: