A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed ...

[1]  Harshad Khadilkar,et al.  Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks , 2017, Transp. Sci..

[2]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[3]  Masoud Yaghini,et al.  Railway Passenger Train Delay Prediction via Neural Network Model , 2013 .

[4]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[5]  Paul Schonfeld,et al.  Analyzing passenger train arrival delays with support vector regression , 2015 .

[6]  Liping Fu,et al.  Statistical investigation on train primary delay based on real records: evidence from Wuhan–Guangzhou HSR , 2017 .

[7]  C. Y. Peng,et al.  An Introduction to Logistic Regression Analysis and Reporting , 2002 .

[8]  Olov Lindfeldt.  Evaluation of Punctuality on a Heavily Utilised Railway Line with Mixed Traffic , 2008 .

[9]  W. Lam,et al.  A study of train dwelling time at the hong kong mass transit railway system , 1998 .

[10]  S. C. Wirasinghe,et al.  An investigation of passenger interchange and train standing time at LRT stations: (ii) Estimation of standing time , 1984 .

[11]  Jan Peters,et al.  Prediction of Delays in Public Transportation using Neural Networks , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[12]  Liping Fu,et al.  Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression , 2019, Canadian Journal of Civil Engineering.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Hadi Meidani,et al.  Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[15]  Rob M.P. Goverde,et al.  Estimation of train dwell time at short stops based on track occupation event data , 2015 .

[16]  Luke J. W. Martin.  Predictive Reasoning and Machine Learning for the Enhancement of Reliability in Railway Systems , 2016, RSSRail.