Contributing Factors for Delays during the Morning Commute Hours and the Impact of the Spread of COVID-19 for Metropolitan Train Lines in Japan — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Contributing Factors for Delays during the Morning Commute Hours and the Impact of the Spread of COVID-19 for Metropolitan Train Lines in Japan
Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
,
Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
1 Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
2 Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan
The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of the virus in the metropolitan train lines in Japan. First of all, the result of the present study clearly revealed the changes in contributing factors for train delays caused by the spread of COVID-19. Specifically, the contributing factors for train delays changed due to the decrease of passengers by the effect of the outbreak of the virus. Additionally, though large terminal stations were considered to be a major contributing factor in causing and increasing train delays in the past, this was not the case after the spread of COVID-19. Therefore, under such conditions, it is more effective to make improvements in small to medium stations and tracks rather than terminal stations. Furthermore, as the decrease in passengers also decreased train delays in commuter lines going to the suburbs due to the spread of COVID-19, the contributing factor for such lines is the excessive number of passengers. Therefore, as for countermeasures for train delays after the effects of COVID-19, it is necessary to disperse passengers in order to avoid passengers concentrating in the same time zones and train lines.
Ministry of Land, Infrastructure, Transport and Tourism (2019) Starting the Visualization of Train Delay. http://www.mlit.go.jp/common/001215328.pdf
Tokyo Metro Co., Ltd. (2014) Ranking of the Average Daily Number of Passengers of Each Station in Fiscal Year 2013. https://www.tokyometro.jp/corporate/enterprise/passenger_rail/transportation/passengers/2013.html
East Japan Railway Company (2021) Changes in Year-on-Year Rate of Revenue of Railway Service in Fiscal Year 2020. https://www.jreast.co.jp/investor/monthly/pdf/report.pdf
Uematsu, S. and Iwakura, S. (2009) A Multi-Agent Simulation Model for Estimating Knock-on Delay of Tokyo Metropolitan Railway. Proceedings of Infrastructure Planning, 40, 19-23.
Landex, A. and Nielsen, O.A. (2010) Simulation of Disturbances and Modelling of Expected Train Passenger Delays. In: Hansen, I.A., Ed., Timetable Planning and Information, WIT Press Publishing, Southampton, 85-93.
Dollevoet, T., Huisman, D., Schmidt, M. and Schöbel, A. (2011) Delay Management with ReRouting of Passengers. Transportation Science, 46, 74-89. https://doi.org/10.1287/trsc.1110.0375
Jiang, Z., Li, F., Xu, R. and Gao, P. (2012) A Simulation Model for Estimating Train and Passenger Delays in Large-Scale Rail Transit Networks. Journal of Central South University, 19, 3603-3613. https://doi.org/10.1007/s11771-012-1448-9
Iwakura, S., Takahashi, I. and Morichi, S. (2013) A Multi Agent Simulation Model for Estimating Knock-on Train Delays under High-Frequency Urban Rail Operation. Transport Policy Studies’ Review, 15, 31-40.
Kobayashi, W. and Iwakura, S. (2016) Development of Train Boarding Door Choice Model for Knock-on Urban Train Delay Analysis. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 72, I_1067-I_1074. https://doi.org/10.2208/jscejipm.72.I_1067
Kobayashi, W. and Iwakura, S. (2019) Agent-Based Model for Assessing Techniques to Reduce Knock-on Urban Train Delay. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 75, 273-288. https://doi.org/10.2208/jscejipm.75.273
Kunimatsu, T., Hirai, C. and Tomii, N. (2012) Train Timetable Evaluation from the Viewpoint of Passengers by Microsimulation of Train Operation and Passenger Flow. Electrical Engineering in Japan, 181, 51-62. https://doi.org/10.1002/eej.21264
Corman, T. (2020) Interactions and Equilibrium between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study. Transportation Science, 54, 785-822. https://doi.org/10.1287/trsc.2020.0979
König, E. and Schön, C. (2021) Railway Delay Management with Passenger Rerouting Considering Train Capacity Constraints. European Journal of Operational Research, 288, 450-465. https://doi.org/10.1016/j.ejor.2020.05.055
Kanai, S., Shingo, K., Harada, S. and Tomii, N. (2011) An Optimal Delay Management Algorithm from Passengers’ Viewpoints Considering the Whole Railway Network. Journal of Rail Transport Planning & Management, 1, 25-37. https://doi.org/10.1016/j.jrtpm.2011.09.003
Börjesson, M. and Eliasson, J. (2011) On the Use of “Average Delay” as a Measure of Train Reliability. Transportation Research Part A: Policy and Practice, 45, 171-184. https://doi.org/10.1016/j.tra.2010.12.002
Sato, K., Tamura, K. and Tomii, N. (2013) A MIP-Based Timetable Rescheduling Formulation and Algorithm Minimizing Further Inconvenience to Passengers. Journal of Rail Transport Planning & Management, 3, 38-53. https://doi.org/10.1016/j.jrtpm.2013.10.007
Robenek, T., Maknoon, Y., Azadeh, S.S., Chen, J. and Bierlairea, M. (2016) Passenger Centric Train Timetabling Problem. Transportation Research Part B: Methodological, 89, 107-126. https://doi.org/10.1016/j.trb.2016.04.003
Li, W. and Zhu, W. (2016) A Dynamic Simulation Model of Passenger Flow Distribution on Schedule-Based Rail Transit Networks with Train Delays. Journal of Traffic and Transportation Engineering, 3, 364-373. https://doi.org/10.1016/j.jtte.2015.09.009
Xu, W., Zhao, P. and Ning, L. (2018) Last Train Delay Management in Urban Rail Transit Network: Bi-Objective MIP Model and Genetic Algorithm. KSCE Journal of Civil Engineering, 22, 1436-1445. https://doi.org/10.1007/s12205-017-1786-0
Yap, M. and Cats, O. (2020) Predicting Disruptions and Their Passenger Delay Impacts for Public Transport Stops. Transportation, 48, 1703-1731. https://doi.org/10.1007/s11116-020-10109-9
Kariyazaki, K., Hibino, N. and Morichi, S. (2010) Study on Mechanism of Worsening Punctuality in Urban Railway Services. Infrastructure Planning Review, 27, 871-879. https://doi.org/10.2208/journalip.27.871
Kariyazaki, K., Hibino, N. and Morichi, S. (2011) Simulation Analysis of Daily Service Delay Focusing on Train Headway. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 67, 67_I_1001-67_I_1010. https://doi.org/10.2208/jscejipm.67.67_I_1001
Kariyazaki, K., Hibino, N. and Morichi, S. (2013) Simulation Analysis of Train Operation to Recover Knock-on Delay Earlier. Asian Transport Studies, 2, 284-294.
Kariyazaki, K., Hibino, N. and Morichi, S. (2015) Simulation Analysis of Train Operation to Recover Knock-on Delay under High-Frequency Intervals. Case Studies on Transport Policy, 3, 92-98. https://doi.org/10.1016/j.cstp.2014.07.007
Yamamura, A. (2014) Delay Reduction Measures and their Effects in Dense Transportation Operation Using Train Traffic Record Data. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 70, 44-55. https://doi.org/10.2208/jscejipm.70.44
Yamamura, A. and Tomii, N. (2019) Train Traffic Simulation based on Analysis of Historical Train Traffic Records for Dense Railway Networks. EEJ Transactions on Industry Applications, 139, 206-214. https://doi.org/10.1541/ieejias.139.206
Miyazaki, K., Hibino, N. and Morichi, S. (2014) Analysis of Train Delay in Urban Railway Services Based on Characteristics of Each Line. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 70, I_477-I_486. https://doi.org/10.2208/jscejipm.70.I_477
Kobayashi, W., Fukuda, W. and Iwakura, S. (2020) Economic Evaluation for Delay and Punctuality of Urban Rail Transit Based on Scheduling Approach. Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), 76, 236-250. https://doi.org/10.2208/jscejipm.76.3_236
Ohshima, K. and Yamamoto, K. (2020) Contributing Factors for Train Delays during Morning Rush Hour in Japanese Metropolitan Areas. Journal of Transportation Technologies, 10, 154-168. https://doi.org/10.4236/jtts.2020.102010
Goverde, R.M.P. (2010) A Delay Propagation Algorithm for Large-Scale Railway Traffic Networks. Transportation Research Part C: Emerging Technologies, 18, 269-287. https://doi.org/10.1016/j.trc.2010.01.002
Corman, F., D’Ariano, A., Pacciarelli, D. and Pranzo, M. (2012) Optimal Inter-Area Coordination of Train Rescheduling Decisions. Transportation Research Part E: Logistics and Transportation Review, 48, 71-88. https://doi.org/10.1016/j.tre.2011.05.002
Dingler, M., Koenig, A., Sogin, S. and Barkan, C.P.L. (2010) Determining the Causes of Train Delay. Proceedings of the 2010 Annual AREMA Conference, Orlando, August 29-September 1 2010, 14 p.
Cule, B., Goethals, B., Tassenoy, S. and Verboven, S. (2011) Mining Train Delays. International Symposium on Intelligent Data Analysis, Porto, 29-31 October 2011, 113-124. https://doi.org/10.1007/978-3-642-24800-9_13
Liu, X., Saat, M.R. and Barkan, C.P.L. (2012) Analysis of Causes of Major Train Derailment and Their Effect on Accident Rates. Transportation Research Record, 2289, 154-163. https://doi.org/10.3141%2F2289-20
Bergström, A. and Krüger, A. (2013) Modeling Passenger Train Delay Distributions: Evidence and Implications. Proceedings of the 5th International Symposium on Transportation Network Reliability, Hong Kong, 18-19 December 2012, 31 p.
Markovica, N., Milinkovicb, S., Tikhonovc, K.S. and Schonfelda, P. (2015) Analyzing Passenger Train Arrival Delays with Support Vector Regression. Transportation Research Part C: Emerging Technologies, 56, 251-262. https://doi.org/10.1016/j.trc.2015.04.004
Wen, C., Li, Z., Lessan, J., Fu, L., Huang, P. and Jiang, C. (2017) Statistical Investigation on Train Primary Delay Based on Real Records: Evidence from Wuhan-Guangzhou HSR. International Journal of Rail Transportation, 5, 170-189. https://doi.org/10.1080/23248378.2017.1307144
Mussanov, D., Nishino, N. and Dick, C.T. (2017) Delay Performance of Different Train Types under Combinations of Structured and Flexible Operations on Single-Track Railway Lines in North America. Proceedings of the 7th International Conference on Railway Operations Modelling and Analysis, Lille, 4-7 April 2017, 759-776.
Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N. and Anguita, D. (2018) Train Delay Prediction Systems: A Big Data Analytics Perspective. Big Dara Research, 11, 54-64. https://doi.org/10.1016/j.bdr.2017.05.002
Arshad, M. and Ahmed, M. (2019) Prediction of Train Delay in Indian Railways through Machine Learning Techniques. International Journal of Computer Sciences and Engineering, 7, 405-411. https://doi.org/10.26438/ijcse/v7i2.405411
Wang, P. and Zhang, Q. (2019) Train Delay Analysis and Prediction Based on Big Data Fusion. Transportation Safety and Environment, 1, 79-88. https://doi.org/10.1093/tse/tdy001
Huang, P., Wen, C., Fu, L., Peng, Q. and Tang, Y. (2020) A Deep Learning Approach for Multi-Attribute Data: A Study of Train Delay Prediction in Railway Systems. Information Science, 516, 234-253. https://doi.org/10.1016/j.ins.2019.12.053
Huang, P., Li, Z., Wen, C., Lessan, J., Corman, F. and Fu, L. (2021) Modeling Train Timetables as Images: A Cost-Sensitive Deep Learning Framework for Delay Propagation Pattern Recognition. Expert Systems with Applications, 177, Article ID: 114996. https://doi.org/10.1016/j.eswa.2021.114996
Huang, P., Lessan, J., Wen, C., Peng, Q., Fu, L., Li, L. and Xu, X. (2020) A Bayesian Network Model to Predict the Effects of Interruptions on Train Operations. Transportation Research Part C: Emerging Technologies, 114, 338-358. https://doi.org/10.1016/j.trc.2020.02.021
Mohd, A. and Muqeem, A. (2021) Train Delay Estimation in Indian Railways by Including Weather Factors through Machine Learning Techniques. Recent Advances in Computer Science and Communications, 14, 1300-1307. https://doi.org/10.2174/2666255813666190912095739
Akaike, H. (1974) A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19, 716-723. https://doi.org/10.1109/TAC.1974.1100705
Kotsushibunsya (2018) My Line Tokyo Timetable (June, 2018).
Kotsushibunsya (2020) My Line Tokyo Timetable (June, 2020).
Ministry of Land, Infrastructure, Transport and Tourism (2017) Statistics Information Related to Congestion Rate Data. https://www.mlit.go.jp/common/001245351.pdf
Ministry of Land, Infrastructure, Transport and Tourism (2019) Statistics Information Related to Congestion Rate Data. https://www.mlit.go.jp/statistics/details/content/001365148.pdf