[1]谢 泽,朱建生,李 雯.基于门控循环单元的铁路客票业务流量数据预测[J].计算机技术与发展,2021,31(10):209-214.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 035]
　XIE Ze,ZHU Jian-sheng,LI Wen.Network Traffic Data Forecast of Railway Passenger Ticket Service System Based on Gated Recurrent Unit[J].,2021,31(10):209-214.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 035]
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基于门控循环单元的铁路客票业务流量数据预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
209-214
栏目:
应用前沿与综合
出版日期:
2021-10-10

文章信息/Info

Title:
Network Traffic Data Forecast of Railway Passenger Ticket Service System Based on Gated Recurrent Unit
文章编号:
1673-629X(2021)10-0209-06
作者:
谢 泽1 ; 朱建生2 ; 李 雯2
1. 中国铁道科学研究院,北京 100081;
2. 中国铁道科学研究院电子计算技术研究所,北京 100081
Author(s):
XIE Ze1 ; ZHU Jian-sheng2 ; LI Wen2
1. China Academy of Railway Sciences,Beijing 100081,China;
2. Institute of Computing Technologies,China Academy of Railway Sciences,Beijing 100081,China
关键词:
门控循环单元; 流量数据; 时序拟合; 趋势预测; 数据预警
Keywords:
gated recurrent unit; network traffic data; time series fitting; trend prediction; data early warning
分类号:
TP391. 9
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 035
摘要:
铁路客票业务流量数据是反映系统业务运行状态的重要记录,为加强流量数据异常预警, 针对流量数据具有历史规律性及突变性的特点,选用适于解析数据时间序列依赖度高的门控循环单元神经网络模型( GRU) ,对流量数据实现时序拟合及趋势预测。GRU 采用不同时间步长对流量数据进行拟合的结果在整点或半点周期时间步长具有局部最小特征,该特征与铁路售票时刻规则形成的时间序列依赖规律相一致。 在相同数据条件下, 使用 GRU 算法与自回归模型等主流预测算法进行拟合准确度对比,结果证明 GRU 在解析铁路客票业务流量数据依赖方面具备较高的准确性。经过对异常流量数据趋势预测及拟合,在数据异常区间,预测结果与真实数据的拟合近似验证了 GRU 算法能够为铁路客票业务流量数据异常预警提供可行性策略。
Abstract:
The traffic data of railway passenger ticket service is important record to reflect the operation status of the system. In order to strengthen the early warning of abnormal traffic data,according to the characteristics of historical regularity and mutation of traffic data,the gated recurrent unit neural network model ( GRU) which is suitable for analyzing data with high dependence on time series is selected to realize time series fitting and trend prediction. The results of GRU fitting the flow data with different time steps have the local minimum feature in the whole point or half point cycle time, which is consistent with the time series dependence rule formed by the railway ticketing time rule. Under the same data conditions,GRU algorithm and auto regression model and other mainstream prediction algorithms are used to compare the fitting accuracy. The results show that GRU has high accuracy in analyzing the traffic data dependence of railway passenger ticket service. After the trend prediction and fitting analysis of abnormal traffic data, the difference between the predicted results and the real data is obvious in the data abnormal interval, which verifies that GRU algorithm can provide feasible strategies for the early warning of abnormal traffic data of railway passenger ticket service.

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更新日期/Last Update: 2021-10-10