Urban Rail Transit Train Dwell Time Analysis Based on Random Forest Algorithm: A Case Study on the Beidajie Station of the Xi’an Metro in China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 7
Abstract
The dwell time (DT) is an essential element in the compilation of urban rail transit train diagrams. To improve the reliability of urban rail transit operations, it is necessary to identify the major factors that influence the actual DT and formulate effective measures. Via data mining, this study proposes a random forest (RF) based identification model to diagnose the major problems that affect the DT at different stations in different periods and put forward corresponding targeted measures. Considering the Beidajie (BDJ) station of Xi’an Metro in the upstream direction of Line 2, this model sensitively identifies major factors leading to the variation in DT during the morning peak, consistent with the detailed analysis. Then, the targeted improvement measures are proposed for the BDJ station. The obtained application results indicate that the established influencing factor identification model can effectively dig out the factors causing a discrepancy between actual and scheduled DT, which can provide auxiliary decision making in the compilation of train diagrams and daily operation organization.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research of this paper was supported by the National Natural Science Foundation of China (No. 72171174), and the National Key R&D Program of China (2018YFB1201402) funded by the Ministry of Science and Technology. The acquisition of the operational data for the study was supported by the Xi’an Metro Co., Ltd. The authors are grateful for this support.
References
Buchmüller, S., U. Weidmann, and A. Nash. 2008. “Development of a dwell time calculation model for timetable planning.” WIT Trans. Built Environ. 103 (Sep): 525–534. https://doi.org/10.2495/CR080511.
Christoforou, Z., E. Chandakas, and I. Kaparias. 2020. “Investigating the impact of dwell time on the reliability of urban light rail operations.” Urban Rail Transit 6 (2): 116–131. https://doi.org/10.1007/s40864-020-00128-1.
Chu, W.-J., X.-C. Zhang, J.-H. Chen, and B. Xu. 2015. “An elm-based approach for estimating train dwell time in urban rail traffic.” Math. Probl. Eng. 2015. https://doi.org/10.1155/2015/473432.
Cornet, S., C. Buisson, F. Ramond, P. Bouvarel, and J. Rodriguez. 2019. “Methods for quantitative assessment of passenger flow influence on train dwell time in dense traffic areas.” Transp. Res. Part C: Emerging Technol. 106 (Sep): 345–359. https://doi.org/10.1016/j.trc.2019.05.008.
D’Acierno, L., M. Botte, A. Placido, C. Caropreso, and B. Montella. 2017. “Methodology for determining dwell times consistent with passenger flows in the case of metro services.” Urban Rail Transit 3 (2): 73–89. https://doi.org/10.1007/s40864-017-0062-4.
Hong, L., W. Li, and W. Zhu. 2017. “Assigning passenger flows on a metro network based on automatic fare collection data and timetable.” Discrete Dyn. Nat. Soc. 2017. https://doi.org/10.1155/2017/4373871.
Jiang, Z. 2018. Train operation organization and management of urban rail transit network. Shanghai, China: Tongji University Press.
Jiang, Z., J. Gu, Y. Han, W. Fan, and J. Chen. 2018. “Modeling actual dwell time for rail transit using data analytics and support vector regression.” J. Transp. Eng. Part A: Syst. 144 (11): 04018071. https://doi.org/10.1061/JTEPBS.0000189.
Jiang, Z., C. Xie, T. Ji, and X. Zou. 2015. “Dwell time modelling and optimized simulations for crowded rail transit lines based on train capacity.” PROMET-Traffic Transp. 27 (2): 125–135. https://doi.org/10.7307/ptt.v27i2.1487.
Krzywinski, M., and N. Altman. 2014. “Visualizing samples with box plots: Use box plots to illustrate the spread and differences of samples.” Nat. Methods 11 (2): 119–120. https://doi.org/10.1038/nmeth.2813.
Li, D., W. Daamen, and R. M. Goverde. 2016. “Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station.” J. Adv. Transp. 50 (5): 877–896. https://doi.org/10.1002/atr.1380.
Li, D., Y. Yin, and H. He. 2018. “Testing the generality of a passenger disregarded train dwell time estimation model at short stops: Both comparison and theoretical approaches.” J. Adv. Transp. 2018. https://doi.org/10.1155/2018/8521576.
Li, Z., S. Lo, J. Ma, and X. Luo. 2020. “A study on passengers’ alighting and boarding process at metro platform by computer simulation.” Transp. Res. Part A: Policy Pract. 132 (Feb): 840–854. https://doi.org/10.1016/j.tra.2019.12.017.
Oh, Y., Y.-J. Byon, J. Y. Song, H.-C. Kwak, and S. Kang. 2020. “Dwell time estimation using real-time train operation and smart card-based passenger data: A case study in Seoul, South Korea.” Appl. Sci. 10 (2): 476. https://doi.org/10.3390/app10020476.
Palmqvist, C.-W., N. Tomii, and Y. Ochiai. 2020. “Explaining dwell time delays with passenger counts for some commuter trains in Stockholm and Tokyo.” J. Rail Transp. Plann. Manage. 14 (Jun): 100189. https://doi.org/10.1016/j.jrtpm.2020.100189.
Pedersen, T., T. Nygreen, and A. Lindfeldt. 2018. “Analysis of temporal factors influencing minimum dwell time distributions.” WIT Trans. Built Environ. 181 (Nov): 447–458. https://doi.org/10.2495/CR180401.
Qi, W., Z. Wang, R. Tang, and L. Wang. 2018. “Driving risk detection model of deceleration zone in expressway based on generalized regression neural network.” J. Adv. Transp. 2018. https://doi.org/10.1155/2018/8014385.
Shi, A., B. Dong, F. Zhao, and Y. Wang. 2019. “Prediction of dwell time of railway freight cars at the terminal based on Gradient Boosting Regression Tree.” In Proc., 2019 3rd Int. Conf. on Mechanical and Electronics Engineering (ICMEE 2019), 220–227. Ottawa: Clausius Scientific Press. https://doi.org/10.23977/icmee.2019.2736.
Suazo-Vecino, G., M. Dragicevic, and J. C. Muñoz. 2017. “Holding boarding passengers to improve train operation on basis of an economic dwell time model.” Transp. Res. Rec. 2648 (1): 96–102. https://doi.org/10.3141/2648-11.
Zhang, Q., B. Han, and D. Li. 2008. “Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations.” Transp. Res. Part C: Emerging Technol. 16 (5): 635–649. https://doi.org/10.1016/j.trc.2007.12.001.
Zhou, F., W. Wang, F. Wang, and R. Xu. 2022. “Urban rail transit train dwell time analysis based on random forest algorithm: A case study on Xi’an metro.” In Proc., 101st Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Mar 24, 2022
Accepted: Jan 10, 2023
Published online: May 10, 2023
Published in print: Jul 1, 2023
Discussion open until: Oct 10, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.