DOI: 10.11908/j.issn.0253-374x.20061

作者:

作者单位: 1.西南交通大学 交通运输与物流学院，四川 成都 610031;2.西南交通大学 综合交通运输智能化国家地方联合工程实验室，四川 成都 610031;3.中国中铁二院工程集团有限责任公司，四川 成都 610031


作者简介: 彭其渊（1962—)，男，教授，博士生导师，工学博士，主要研究方向为铁路运输组织优化。E-mail： qiyuan-peng@swjtu.edu.cn


通讯作者: 鲁工圆(1983—)，男，副教授，工学博士，主要研究方向为轨道交通运输组织仿真。E-mail： lugongyuan@swjtu.edu.cn

中图分类号: U29


基金项目: 国家重点研发计划（2017YFB1200701）;高铁联合基金（U1834209）




Intelligent Generation Model of Dispatching Command Based on Early- warning Text Information
Author:

Affiliation: 1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China;2.National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University，Chengdu 610031, China;3.China Railway Eryuan Engineering Group Co.，Ltd., Chengdu 610031,China


Fund Project:




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摘要:提出调度命令智能生成模型,该模型由神经网络和调度命令修正模块两部分组成。基于长短时记忆（LSTM)网络构建序列?序列（seq2seq）模型，将预警文本信息作为模型的输入进行训练，提出五种调度命令修正策略并分别对五种易错信息进行修正，最终得到调度命令。结果表明：该模型具有利用预警文本信息生成调度命令的能力，引入的调度命令修正模块能够有效提升调度命令生成质量。



Abstract:An intelligent generation model of dispatching commands is proposed in this paper. The model consists of a neural network and a dispatching command modification module. A sequence-sequence (seq2seq) model based on a long-term and short-term memory（LSTM) network is built, the training is performed using early-warning text information as input into the model. Five scheduling command modification strategies are put forword and the error-prone information is modified to obtain the final scheduling command. It is shown that the model has the ability to generate dispatching commands by using early-warning text information. The introduction of a command correction module can effectively improve the quality of dispatching command generation.





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