A neural network model for real-time prediction of high-speed railway delays

doi:10.16265/j.cnki.issn1003-3033.2019.S1.005

China Safety Science Journal ›› 2019, Vol. 29 ›› Issue (S1): 20-26.doi: 10.16265/j.cnki.issn1003-3033.2019.S1.005

• Safety Systematology • Previous Articles Next Articles

A neural network model for real-time prediction of high-speed railway delays

HUANG Ping^1,2,3, WEN Chao^1,2,3, LI Zhongcan^1,2, YANG Yuxiang^1,2, PENG Qiyuan^1,2

1 National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
2 National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
3 Railway Research Centre, University of Waterloo, Waterloo N2L3G1, Canada

Received:2019-03-03 Revised:2019-05-25 Online:2019-06-30 Published:2020-10-28

Abstract

Abstract: Accurately forecasting train delays has great significance for improving the real-time dispatching ability and the quality of transport service. By using the train operation records of Wuhan-Guangzhou HSR, a deep learning model based on RNN was established to predict train delays. In this model, the trains were fed into RNN according to their operating orders to use the feed-back mechanism of RNN to capture train interaction. The model performance was evaluated based on mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics. The results demonstrate that deep learning model proposed in this paper distinctly outperform other models widely used delay prediction, including Artificial Neural Network, Support Vector Regression, and Markov Model.

Key words: high-speed railway (HSR), train operation records, delay prediction, recurrent neural network (RNN), train interaction

CLC Number:

X928.03

HUANG Ping, WEN Chao, LI Zhongcan, YANG Yuxiang, PENG Qiyuan. A neural network model for real-time prediction of high-speed railway delays[J]. China Safety Science Journal, 2019, 29(S1): 20-26.

References

[1] NABIAN M A, ALEMAZKOOR N, MEIDANI H. Predicting near-term train schedule performance and delay using Bi-Level random forests[J]. Transportation Research Record, 2019,DOI: 10.1177/0361198119840339.
[2] HANSEN I A, GOVERDE R M, VAN DER MEER D J. Online train delay recognition and running time prediction[C]. 13^th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010: 1783-1788.
[3] KECMAN P, GOVERDE R M. Online data-driven adaptive prediction of train event times[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 465-474.
[4] ALLAN J J, ARIAS E. Computers in railways XI: computer system design and operation in the railway and other transit systems[M]. Southampton: WIT Press, 2008:199.
[5] 杨肇夏, 董中英. 列车晚点传播模拟系统[J]. 铁道学报, 1995, 17(2): 17-24.
YANG Zhaoxia, DONG Zhongying. Simulation system of train delay propagation[J]. Journal of the China Railway Society, 1995, 17(2): 17-24.
[6] 刘岩, 郭竞文, 罗常津, 等. 列车运行实绩大数据分析及应用前景展望[J]. 中国铁路, 2015(6): 70-73.
LIU Yan, GUO Jingwen, LUO Changjin, et al. Analysis of large data of train operation performance and prospect of its application[J]. China Railway, 2015 (6): 70-73.
[7] 黄平, 彭其渊, 文超, 等. 高速铁路故障分类及其影响列车数模型[J]. 中国安全科学学报, 2018, 28(增2): 46-53.
HUANG Ping, PENG Qiyuan, WEN Chao, et al. Study on high-speed railway disruption clustering method and their affected train amount[J]. China Safety Science Journal, 2018, 28(S2):46-53.
[8] BRIGGS K, BECK C. Modelling train delays with q-exponential functions[J]. Physica A: Statistical Mechanics and its Applications, 2007, 378(2): 498-504.
[9] WEN C, LI Z, LESSAN J, et al. Statistical investigation on train primary delay based on real records: evidence from Wuhan-Guangzhou HSR[J]. International Journal of Rail Transportation, 2017, 5(3): 170-189.
[10] ŞAHIN $\dot{I}$. Markov chain model for delay distribution in train schedules: Assessing the effectiveness of time allowances[J]. Journal of Rail Transport Planning & Management, 2017, 7(3): 101-113.
[11] MALAVASI G, RICCI S. Simulation of stochastic elements in railway systems using self-learning processes[J]. European Journal of Operational Research, 2001, 131(2): 262-272.
[12] YAGHINI M, KHOSHRAFTAR M M, SEYEDABADI M. Railway passenger train delay prediction via neural network model[J]. Journal of Advanced Transportation, 2013, 47(3): 355-368.
[13] 曾壹, 陈峰, 金博汇. 一种调度区段晚点时长的神经网络预测模型[J]. 铁道标准设计, 2019, 63(3):152-157.
ZENG Yi, CHEN Feng, JIN Bohui. A prediction model for timetable delays in dispatching area using neural network[J]. Railway Standard Design, 2019, 63(3):152-157.
[14] MARKOVIĆ N, MILINKOVIĆ S, TIKHONOV K S, et al. Analyzing passenger train arrival delays with support vector regression[J]. Transportation Research Part C: Emerging Technologies, 2015, 56:251-262.
[15] WALLANDER J, M KITALO M. Data mining in rail transport delay chain analysis[J]. International Journal of Shipping and Transport Logistics, 2012, 4(3): 269-285.
[16] ELMAN J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211.
[17] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Cognitive Modeling, 1986,323:533-536.

A neural network model for real-time prediction of high-speed railway delays

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