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

Jian JIA; Linfeng LIU; Jiagao WU

doi:10.11959/j.issn.2096-109x.2020085

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

Papers | 更新时间：2024-06-03

- Charging pile recommendation method for idle electric taxis based on recurrent neural network
- Chinese Journal of Network and Information Security Vol. 6, Issue 6, Pages: 152-163(2020)
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- DOI：10.11959/j.issn.2096-109x.2020085
  CLC： TP393
- Online First：2020-12，
  
  Published：15 December 2020
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Jian JIA, Linfeng LIU, Jiagao WU. Charging pile recommendation method for idle electric taxis based on recurrent neural network[J]. Chinese Journal of Network and Information Security, 2020, 6(6): 152-163.
DOI：

Jian JIA, Linfeng LIU, Jiagao WU. Charging pile recommendation method for idle electric taxis based on recurrent neural network[J]. Chinese Journal of Network and Information Security, 2020, 6(6): 152-163. DOI： 10.11959/j.issn.2096-109x.2020085.

摘要

提出了一种基于循环神经网络的空载电动出租车的充电桩推荐方法（CPRM-IET，charging pile recommendation method for idle electric taxis），来为空载状态下的电动出租车推荐最佳充电桩。空载状态下的电动出租车移动一般依赖于驾驶人的潜意识移动倾向和驾驶习惯，因此需要根据其历史移动轨迹来预测其未来移动，从而找到充电额外移动最小的若干充电桩。在CPRM-IET中，使用了一种基于双阶段注意力机制的循环神经网络（DA-RNN，dual-stage attention-based recurrent neural network）模型来预测电动出租车的未来轨迹，DA-RNN模型包括输入注意力机制和时间注意力机制。输入注意力机制在每个时刻为输入的行驶记录分配权重，而时间注意机制为编码器的隐藏状态分配权重。根据预测轨迹，再选择额外移动最小的若干充电桩，并推荐给电动出租车驾驶人。仿真结果表明，CPRM-IET可以在额外移动和均方根误差方面取得较好的结果，反映了CPRM-IET可以准确地预测空载电动出租车的未来轨迹，并向这些电动出租车推荐合适的充电桩。

Abstract

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.

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references

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