基于LSTM(长短期记忆)网络的城市轨道交通列车停站精度预测
谢嘉琦1邹喜华1汪小勇2毕文峰2华志辰2
Prediction of Urban Rail Transit Train Stop Accuracy Based on LSTM Network
XIE JiaqiZOU XihuaWANG XiaoyongBI WenfengHUA Zhichen
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作者信息:1.1.西南交通大学信息科学与技术学院， 610031， 成都；
2.2.卡斯柯信号有限公司， 200072， 上海
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Affiliation:School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2022.11.014
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中图分类号/CLCN:U284.48
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栏目/Col:研究报告
摘要:
城市轨道交通列车进站自动停车过程中常会出现冲标和欠标问题，结合行车日志大数据分析和LSTM(长短期记忆)网络算法，提出了有效的解决方案。首先针对行车日志中大量的列车停车精度历史信息进行大数据分析，按1 d为一个统计周期对数据进行分期，并对数据进行预处理和多类拟合，对比后获得最佳拟合参数的时间序列。然后通过LSTM网络算法构建深度学习模型，对列车进站自动停车精度的分布进行预测。最后基于成都某地铁线列车停车精度的历史数据，对该LSTM预测模型进行训练与验证。结果表明：该预测模型可满足统计学上对相似度大于0-9的要求，从而验证了该模型的有效性和准确性。
Abstracts:
In order to solve the problems of standard deviation and non-standard deviation in the process of automatic rail transit train parking, an effective solution is proposed based on the analysis of running log big data and the LSTM (long and short term memory) network algorithm. Firstly, the big data analysis is conducted for a large amount of historical information on train parking accuracy in the running log. In which the data is divided into stages with 1 d as a statistical cycle, then,the data is preprocessed and multi-type fitted. After comparison, the time series of the best fitting parameters is obtained. On this basis, a deep learning model is built by LSTM network algorithm to predict the distribution of automatic train parking accuracy. Finally, based on the historical data of train parking accuracy of a subway line in Chengdu, the LSTM prediction model is trained and verified. Results show that the prediction model can meet the statistical requirements for the similarity greater than 0.9. Thus, the effectiveness and accuracy of the model are verified.
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