A closed form railway line delay propagation model

Harrod, Steven; Cerreto, Fabrizio; Nielsen, Otto Anker

doi:10.1016/j.trc.2019.02.022

Cited by 31 publications

(17 citation statements)

References 26 publications

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“…Dataset [27] contains data on four small case scenarios for the timetable scheduling fairness problem. Dataset [28] contains simulation data for the evaluation of performance and delays of a suburban railway line and was published in delay propagation study [29]. The OpenTrack model simulates train departures according to a timetable and calculates speed and positioning of trains while taking into consideration safety rules, signaling and railway line constraints.…”

Section: Passenger Transportmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

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Section: Passenger Transportmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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“…Delay prediction models aim at predicting primary delays of train arrivals and/or departures [10,11]. Train delay propagation models express the development of secondary delays throughout a train journey by analyzing the impact of events, such as meetings and overtakings [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status

Minbashi

Bohlin

Palmqvist

et al. 2021

Journal of Advanced Transportation

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Shunting yards are one of the main areas impacting the reliability of rail freight networks, and delayed departures from shunting yards can further also affect the punctuality of mixed-traffic networks. Methods for automatic detection of departures, which are likely to be delayed, can therefore contribute towards increasing the reliability and punctuality of both freight and passenger services. In this paper, we compare the performance of tree-based methods (decision trees and random forests), which have been highly successful in a wide range of generic applications, in classifying the status of (delayed, early, and on-time) departing trains from shunting yards, focusing on the delayed departures as the minority class. We use a total number of 6,243 train connections (representing over 21,000 individual wagon connections) for a one-month period from the Hallsberg yard in Sweden, which is the largest shunting yard in Scandinavia. Considering our dataset, our results show a slight difference between the application of decision trees and random forests in detecting delayed departures as the minority class. To remedy this, enhanced sampling for minority classes is applied by the synthetic minority oversampling technique (SMOTE) to improve detecting and assigning delayed departures. Applying SMOTE improved the sensitivity, precision, and F-measure of delayed departures by 20% for decision trees and by 30% for random forests. Overall, random forests show a relative better performance in detecting all three departure classes before and after applying SMOTE. Although the preliminary results presented in this paper are encouraging, future studies are needed to investigate the computational performance of tree-based algorithms using larger datasets and considering additional predictors.

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“…Let u = b and r = (C i − 1)h, similar to [19], we can establish a relationship between cumulative delay and primary delay as…”

Section: Situation 1: the Blockage Of A Railway Segmentmentioning

confidence: 99%

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

et al. 2021

IET Intelligent Trans Sys

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This paper proposes an analytical delay propagation model for single railway lines based on the max-plus algebra theory. The scheduling measures taken by dispatchers, including re-timing and re-ordering, will be incorporated into our delay propagation model using a matrix transformation method. An analysis of delay propagation under some typical emergencies such as segment blockages and train speed limitation is performed. Numerical simulations show that the proposed train delay propagation model can predict emergencyinduced train delays under different scheduling strategies, thus may give a guidance to improve the traffic management. In the high-speed railway train system, the scheduling measures taken by dispatchers, such as re-timing and re-ordering, can be formulated as a delay propagation model using a matrix transformation method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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A closed form railway line delay propagation model

Cited by 31 publications

References 26 publications

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

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