A dynamic model for real-time track assignment at railway yards

Schasfoort, Bram B.W.; Gkiotsalitis, Konstantinos; Eikenbroek, Oskar Adriaan Louis; Berkum, Eric C. van

doi:10.1016/j.jrtpm.2020.100198

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…Specifically, the identified industry 4.0 technologies used in this domain are AI, IoT, modelling and simulation, Big Data, SDSS, VR and Cloud Computing. Within these technologies we found the following algorithms for rail transport optimization: GA [ 104 ], Gaussian Kernel Ant Colony Optimization (GKACO) algorithm [ 103 ], MILPs [ 93 , 97 , 102 ], PCA algorithm [ 100 ], ML [ 86 , 96 ] and DL [ 87 , 89 ] techniques. For obtaining railway insights, we identified the following tools based on big data and cloud computing: Apache Hadoop, Tashi Cloud Middleware [ 111 ], Apache Spark and Google Cloud Infrastructure [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…In the train transport industry, AI applications include methods based on machine learning and deep learning being used for a diversified variety of problems. Principally, AI can be used for rail-track assignment [ 104 ], tackling the train-delay propagation problem [ 87 ], prediction of train flow [ 82 ], time-arrival prediction [ 86 ], rail-condition evaluation and management [ 17 ], fault detection in railway infrastructure [ 12 ], energy optimization [ 115 ], surveillance systems [ 76 ] and detection of strange objects in railroads [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

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Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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“…Schasfoort et al. studied the real‐time train assignment problem and modelled it as a mixed integer program that strove to minimize the total weighted delay of trains [49]. Königa and Schöna (2020) presented an optimization model for delay management in railway networks, taking limited capacities of tracks and stations into consideration [50].…”

Section: Related Workmentioning

confidence: 99%

“…Corman et al (2010) addressed the problem of train conflict detection and resolution, which was dealt with every day to adapt the timetable to delays and other unpredictable events occurring in real-time [48]. Schasfoort et al studied the real-time train assignment problem and modelled it as a mixed integer program that strove to minimize the total weighted delay of trains [49]. Königa and Schöna (2020) presented an optimization model for delay management in railway networks, taking limited capacities of tracks and stations into consideration [50].…”

Section: Related Workmentioning

confidence: 99%

An integrated model for train rescheduling and station track assignment

Meng

Wang

Xiang

et al. 2020

IET Intelligent Trans Sys

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Both train rescheduling and station track assignment have become hot topics in recent years. It is fundamentally important to do the rescheduling and track assignment work at the same time to avoid the feasibility risk of the re‐scheduled timetable. The purpose of this paper is to design an integrated model for train rescheduling and track assignment in order to provide an integrative plan for the trains to run on the railway sections and go through stations. Based on the existing train rescheduling model, the model is designed by adding the constraints and the optimization goal of track assignment. The goal of track assignment is to maximize the equilibrium of the track usage time, and the constraint is that two trains cannot occupy a same track at the same time. An artificial bee colony algorithm is used to solve the model to get the operation plan. A computing experiment was carried out to prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the developers of a railway dispatching system.

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“…Rail transport is becoming increasingly important in many countries. Owing to the increase in train travelers, increasing numbers of trains operate on the same infrastructure ( 1 ). This results in a smaller headway among successive trains and, thus, unexpected events, such as a switch failure, might have a significant impact on rail operations.…”

mentioning

confidence: 99%

Optimization Model for Rail Line Crossover Design Considering the Cost of Delay

Trommelen

Gkiotsalitis

Berkum

2021

Transportation Research Record: Journal of the Transportation R

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In this study, we introduce a method to optimally select the crossover locations of an independent rail line from a set of possible crossover locations considering a fixed number of crossovers that must be used in the design. This optimal selection aims to minimize the cost of passenger delay. Previous research showed that including passenger delay in the decision of rail design choices could be beneficial from economic and societal perspectives. However, those studies were only able to evaluate a few alternatives, because the degraded schedules had to be determined manually. In this research, we introduced an integer nonlinear model to find the best crossover design. We further developed an algorithm to evaluate a set of crossovers and determine the cost of delays for all segments on a rail line given a set of potential disruptions. The monetized cost of passenger delays was used to analyze the tradeoff between the unreliability costs emerging from the delay of passengers in the case of disruptions, and the total number of required crossovers. Our model was applied on a light rail line in Bergen (Norway) resulting in 10% reduction in relation to passenger delays without increasing the number of crossovers; thus, ensuring that there were no additional costs.

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A dynamic model for real-time track assignment at railway yards

Cited by 15 publications

References 30 publications

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

An integrated model for train rescheduling and station track assignment

Optimization Model for Rail Line Crossover Design Considering the Cost of Delay

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