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2024 | OriginalPaper | Chapter

9. Improving On-Time Performance: Predicting Train Delays with Machine Learning Techniques

Authors : Rakesh Chandmal Sharma, Ismail Hossain, Amit Kumar

Published in: Dynamics of Transportation Ecosystem, Modeling, and Control

Publisher: Springer Nature Singapore

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Abstract

The paper discusses the Indian railway system, the fourth-largest rail network in the world, and identifies a prevalent issue experienced by railway passengers—train delays. The primary focus of this research is to leverage advanced machine learning techniques to address this challenge, with a specific emphasis on predicting train delays using a Random Forest Classifier model. By developing this predictive model, the study offers significant potential benefits for both passengers and railway authorities. Passengers will be able to plan their journeys more effectively, taking into account potential delays, thereby enhancing their overall travel experience. Meanwhile, for the Indian railways, this research provides a valuable tool to identify bottlenecks and other contributing factors that lead to delays for specific trains. This research aims to significantly improve the performance and efficiency of the Indian railway system. Through the integration of cutting-edge machine learning methods with comprehensive real-world railway data, the paper aims to create a solution that not only enhances passenger satisfaction but also enables railway authorities to optimize operations and elevate service quality. The paper's multidimensional approach endeavors to pave the way for a more dependable and efficient railway network. By addressing the challenge of train delays, this work aims to create a positive impact on all stakeholders involved, fostering smoother journeys and improved services for the entire ecosystem.

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Sharma SK, Saini U, Kumar A (2019) Semi-active control to reduce lateral vibration of passenger rail vehicle using disturbance rejection and continuous state damper controllers. J Vib Eng Technol 7(2):117–129. https://doi.org/10.1007/s42417-019-00088-2

Sharma RC, Sharma SK, Sharma N, Sharma S (2020a) Analysis of ride and stability of an ICF railway coach. Int J Veh Noise Vib 16:127. https://doi.org/10.1504/IJVNV.2020.117820CrossRef

Sharma RC, Sharma S, Sharma SK, Sharma N (2020b) Analysis of generalized force and its influence on ride and stability of railway vehicle. Noise Vib Worldw 51:95–109. https://doi.org/10.1177/0957456520923125CrossRef

Sharma S, Sharma RC, Sharma SK, Sharma N, Palli S, Bhardawaj S (2020c) Vibration isolation of the quarter car model of road vehicle system using dynamic vibration absorber. Int J Veh Struct Syst 12. https://doi.org/10.4273/ijvss.12.5.05

Sharma RC, Sharma SK, Palli S (2020d) Linear and non-linear stability analysis of a constrained railway wheelaxle. Int J Veh Struct Syst 12. https://doi.org/10.4273/ijvss.12.2.04

Sharma SK, Sharma RC, Sharma N (2020e) Combined multi-body-system and finite element analysis of a rail locomotive crashworthiness. Int J Veh Struct Syst 12. https://doi.org/10.4273/ijvss.12.4.15

Sharma SK, Sharma RC, Lee J (2021a) Effect of rail vehicle-track coupled dynamics on fatigue failure of coil spring in a suspension system. Appl Sci 11:2650. https://doi.org/10.3390/app11062650CrossRef

Sharma RC, Palli S, Sharma N, Sharma SK (2021b) Ride behaviour of a four-wheel vehicle using H infinity semi-active suspension control under deterministic and random inputs. Int J Veh Struct Syst 13. https://doi.org/10.4273/ijvss.13.2.18

Sharma RC, Sharma S, Sharma N, Sharma SK (2021c) Linear and nonlinear analysis of ride and stability of a three-wheeled vehicle subjected to random and bump inputs using bond graph and simulink methodology. SAE Int J Commer Veh 14:02-15-01-0001. https://doi.org/10.4271/02-15-01-0001

Sharma RC, Sharma S, Sharma SK, Sharma N, Singh G (2021d) Analysis of bio-dynamic model of seated human subject and optimization of the passenger ride comfort for three-wheel vehicle using random search technique. Proc Inst Mech Eng Part K J Multi-body Dyn 235:106–121. https://doi.org/10.1177/1464419320983711

Sharma SK, Mohapatra S, Sharma RC, Alturjman S, Altrjman C, Mostarda L, Stephan T (2022a) Retrofitting existing buildings to improve energy performance. Sustainability 14:666. https://doi.org/10.3390/su14020666CrossRef

Sharma SK, Sharma RC, Lee J, Jang H-L (2022b) Numerical and experimental analysis of DVA on the flexible-rigid rail vehicle carbody resonant vibration. Sensors 22:1922. https://doi.org/10.3390/s22051922CrossRef

Sharma SK, Lee J, Jang H-L (2022c) Mathematical modeling and simulation of suspended equipment impact on car body modes. Machines 10:192. https://doi.org/10.3390/machines10030192CrossRef

Sharma SK, Sharma RC, Lee J (2022d) In situ and experimental analysis of longitudinal load on carbody fatigue life using nonlinear damage accumulation. Int J Damage Mech 31:605–622. https://doi.org/10.1177/10567895211046043CrossRef

Sharma RC, Gopala Rao LVV, Sharma SK, Palli S, Satyanarayana VSV (2022e) Analysis of lateral stability and ride of an Indian railway constrained dual-axle bogie frame. SAE Int J Commercial Veh 16(2):213–228, 02-16-02-0014. https://doi.org/10.4271/02-16-02-0014

Sharma RC, Palli S, Gopala Rao LVV, Duppala A, Sharma SK (2022f) Four-wheel vehicle response under bump, pothole, harmonic, and random excitations using bond graph/simulink technique. SAE Int J Commercial Veh 16(2):129–139, 02-16-02-0008. https://doi.org/10.4271/02-16-02-0008

Sharma RC, Palli S, Jha AK, Bhardawaj S, Sharma SK (2022g) Vibration and ride comfort analysis of railway vehicle system subjected to deterministic inputs. Resmilitaris 12(5):1345–1355

Sharma RC, Palli S, Bhardawaj S (2023a) A review of wheel-rail dynamic behavior on curved tracks. Int J Veh Struct Syst 15. https://doi.org/10.4273/ijvss.15.1.04

Sharma RC, Palli S, Sharma SK (2023b) Ride analysis of railway vehicle considering rigidity and flexibility of the carbody. J Chinese Inst Eng 46:355–366. https://doi.org/10.1080/02533839.2023.2194918CrossRef

Sharma RC, Bhardawaj S, Sharma SK, Bahl S (2023c) Ride behaviour of three-wheel vehicle using semi-active suspension using skyhook control strategy. Int J Veh Struct Syst 15(1). https://doi.org/10.4273/ijvss.15.1.05

Sharma SK, Sharma RC, Choi Y, Lee J (2023d) Experimental and mathematical study of flexible–rigid rail vehicle riding comfort and safety. Appl Sci 13(9):5252

Sharma SK, Sharma RC, Choi Y, Lee J (2023e) Modelling and dynamic analysis of adaptive neuro-fuzzy inference system-based intelligent control suspension system for passenger rail vehicles using magnetorheological damper for improving ride index. Sustainability 15(16):12529

Sharma SK, Sharma RC, Lavania S, Palli S, Avesh M (2023f) Industry-driven approach for ANFIS-based intelligent control suspension system with MR damper for enhanced ride quality in passenger rail vehicles for technological investigations. Natl Acad Sci Lett 1–4

Sharma SK, Sharma RC, Upadhyay RK, Lee J (2023g) Coupled torsional–transverse vibration reduction in marine propulsion dynamics with novel approach using magnetorheological damping and adaptive control system. J Vib Eng Technol 1–11

Sharma SK, Upadhyay RK, Kumar V, Valera H (2023h) Introduction to sustainable transportation system. In: Transportation energy and dynamics. Springer, pp 3–6

Sharma RC, Palli S, Dharmana L, Sharma SK, Sharma A, Kharola A, Sharma N (2024a) Simulation of transient dynamic response of a locomotive considering its flexibility and rigidity. Noise Vib Worldwide 55(1–2):33–47

Sharma RC, Palli S, Duppala A, Bhardawaj S, Sharma SK, Sharma N, Sharma A (2024b) Rigid body analysis of ride behaviour of roadway vehicle using Lagrangian dynamics. Int J Veh Struct Syst (IJVSS) 16(1)

Sharma RC, Sharma N, Palli S, Sharma SK, Joshi S, Rallabandi SR, Sharma A (2024c) Dynamic behavior of three-wheel vehicle with inerter-based dynamic absorber suspension system. Noise Vib Worldwide 55(4–5):253–263. https://doi.org/10.1177/09574565241243398

Sharma SK, Kumar N, Avesh M, Sharma RC, Siddiqui MIH, Lee J (2024d) Navigating tranquillity with H∞ controller to mitigate ship propeller shaft vibration. J Vib Eng Technol 1–11

Sharma SK, Sharma RC, Lee J (2024e) Modelling and simulation of MR damper characterization and uncertainty assessment in marine engine vibration isolation with FLPID control. Int J Model Simul 1–11

Sharma SK, Sharma RC, Lee J (2024f) Propelling precision of longitudinal vibration mitigation in ship propeller shafts through advanced nonlinear intelligent semi-active control leveraging adaptive neuro-fuzzy inference system with linear quadratic regulator. J Vib Control. https://doi.org/10.1177/10775463241244836

Sharma SK, Sharma RC, Upadhyay RK, Lee J (2024g) Enhanced ship engine vibration reduction using magnetorheological dampers and adaptive neuro-fuzzy control system. Ships Offshore Struct 1–17

Sinha AK, Sengupta A, Gandhi H, Bansal P, Agarwal KM, Sharma SK, Sharma RC, Sharma SK (2019) Performance enhancement of an all-terrain vehicle by optimizing steering, powertrain and brakes. In Advances in engineering design . lecture notes in mechanical engineering. Springer, Singapore, pp 207–215. https://doi.org/10.1007/978-981-13-6469-3_19

Swapna Rekha H, Nayak J, Behera HS (2020) Pi-sigma neural network: survey of a decade progress. In: Computational intelligence in pattern recognition. Advances in intelligent systems and computing, 1120. Springer, Singapore, pp 429–441. https://doi.org/10.1007/978-981-15-2449-3_37

Upadhyay RK, Sharma SK, Kumar V, Valera H (2023a) Introduction to transportation systems technology and integrated management. In: Transportation systems technology and integrated management. Springer, pp 1–4. https://doi.org/10.1007/978-981-99-1517-0_1

Upadhyay RK, Sharma SK, Kumar V, Valera H (2023b) Transportation systems technology and integrated management. Edited by RK Upadhyay, SK Sharma, V Kumar, H Valera. Springer Nature, Singapore. https://doi.org/10.1007/978-981-99-1517-0

Upadhyay RK, Sharma SK, Kumar V (Eds) (2024a) Intelligent transportation system and advanced technology. Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0515-3

Upadhyay RK, Sharma SK, Kumar V (2024b) Introduction to intelligent transportation system and advanced technology. In: Intelligent transportation system and advanced technology. Energy, Environment, and Sustainability. pp 3–6. Springer, Singapore. https://doi.org/10.1007/978-981-97-0515-3_1

Vishwakarma PN, Mishra P, Sharma SK (2022a) Characterization of a magnetorheological fluid damper a review. Mater Today: Proc 56:2988–2994. https://doi.org/10.1016/j.matpr.2021.11.143

Vishwakarma PN, Mishra P, Sharma SK (2022b) Formulation of semi-active suspension system and controls in rail vehicle. SSRN Electron J. https://doi.org/10.2139/ssrn.4159616

Wu Q, Cole C, Spiryagin M, Chang C, Wei W, Ursulyak L, Shvets A, Murtaza MA, Mirza IM, Zhelieznov К, Mohammadi S, Serajian H, Schick B, Berg M, Sharma RC, Aboubakr A, Sharma SK, Melzi S, Di Gialleonardo E, Cantone L et al (2023) Freight train air brake models. Int J Rail Transp 11(1):1–49. https://doi.org/10.1080/23248378.2021.2006808

Title: Improving On-Time Performance: Predicting Train Delays with Machine Learning Techniques
Authors: Rakesh Chandmal Sharma
Ismail Hossain
Amit Kumar
Publisher: Springer Nature Singapore
Book: Dynamics of Transportation Ecosystem, Modeling, and Control
Print ISBN: 978-981-9704-36-1

Electronic ISBN: 978-981-9704-37-8

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-97-0437-8_9

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