Abstract
This paper presents a novel railway timetable diagnostic analysis framework based on train operation data. Firstly, we define the concept of train deviation and give a new definition of train delay. Secondly, we propose the delay high-incidence section, station and train identification methods On the basis of deviation-based visualization technology. Finally, we take Chengdu-Kunming railway line (from Puxiong-Xichangbei section) as an example to verify the proposed method. The results show that: (1) Our method can effectively identify stations and sections that are prone to delays; (2) Cargo trains are more likely to occur delay than passenger trains; (3) There is a strong linear correlation between arrival delay and departure delay.
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Acknowledgement
This research was supported by the National Key R&D Program of China (2017YFB1200702, 2016YFC0802208), National Natural Science Foundation of China (Project No. 61703351), Science and Technology Plan of Sichuan province (Project No. 2017ZR0149, 2017RZ0007, 2017015, 2018RZ0078), Science and Technology Plan of China Railway Corporation (Project No. 2016X006-D), Service Science and Innovation Key Laboratory of Sichuan Province (KL1701), Chengdu Soft Science Research Project (2017-RK00-00028-ZF, 2017-RK00-00378-ZF) and the Fundamental Research Funds for the Central Universities (2682017CX022, 2682017CX018).
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Xu, C., Ni, S., Li, S., Chen, D. (2019). Railway Timetable Diagnostic Analysis Based on Train Operation Data. In: Ni, S., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-030-04582-1_16
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DOI: https://doi.org/10.1007/978-3-030-04582-1_16
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