An ensemble prediction model for train delays

Nair, Rahul; Hoang, Thanh Lam; Laumanns, Marco; Chen, Bei; Cogill, Randall; Szabó, Jácint; Walter, Thomas

doi:10.1016/j.trc.2019.04.026

Cited by 62 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their papers, they proposed the application of shallow and deep extreme learning machines for trains' delays. Nair et al [21] developed a large-scale ensemble passenger train delay model in German railways. By combining a statistical random forest-based model, a kernel regression model, and a mesoscopic simulation model, they demonstrated a 25% improvement potential in the prediction accuracy and 50% reduction in root mean squared errors compared to the published schedule.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, delayed departures have a different distribution from early departures [25,28], which may result in distinct models for delayed departures. Once delayed departures are classified, the actual delay can be predicted in the second level [21]. In the third level, delayed departures can be mitigated by rebooking delayed wagons to different trains in order to minimize the departure delays.…”

Section: Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

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

Minbashi

Bohlin

Palmqvist

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

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

Minbashi

Bohlin

Palmqvist

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…One recent study provide valuable insights into the use of combination of machine learning and other approaches to predict train delays in Germany. Nair et al [ 14 ] developed a prediction model for forecasting train delays for the nationwide Deutsche Bahn passenger network, which runs approximately 25,000 trains per day. The data sources provided a rich characterisation of the network’s operational state, some of which was collected using track-side train passing messages to reconstruct current network states, including train position, delay information and key conflict indicators.…”

Section: Related Workmentioning

confidence: 99%

“…Thirdly, a mesoscopic simulation model was applied to account for differences in dwell time and conflicts in track occupation. Nair et al [ 14 ] model demonstrated a 25% improvement in prediction accuracy and a reduction of root mean squared errors of 50% in comparison with the published timetable. The strength of their system was their use of ensembles which, as expected, showed was superior to constituent models.…”

Section: Related Workmentioning

confidence: 99%

Weighted Ensemble Methods for Predicting Train Delays

Ghamdi

Parr

Wang

2020

Computational Science and Its Applications – ICCSA 2020

View full text Add to dashboard Cite

Train delays have become a serious and common problem in the rail services due to the increasing number of passengers and limited rail network capacity, so being able to predict train delays accurately is essential for train controllers to devise appropriate plans to prevent or reduce some delays. This paper presents a machine learning ensemble framework to improve the accuracy and consistency of train delay prediction. The basic idea is to train many different types of machine learning models for each station along a chosen journey of train service using historical data and relevant weather data, and then with certain criteria to choose some models to build an ensemble. It then combines the outputs from its member models with an aggregation function to produce the final prediction. Two aggregation functions were devised to combine the outputs of individual models: averaging and weighted averaging. These ensembles were implemented with a framework and their performance was tested with the data from an intercity train service as a case study. The accuracy was measured by the percentages of correct prediction of the arrival time for a train and correct prediction within one minute to the actual arrival time. The mean accuracies and standard deviations are 42.3%( ) from the individual models, 57.8%( ) from the averaging ensembles, and 72.8%( ) from the weighted ensembles. For the predictions within one minute of the actual times, they are 86.4%( ), 94.6%( ) and 96.0%( ) respectively. So overall, the ensembles significantly improved not only the prediction accuracies but also the consistency and the weighted ensembles are clearly the best.

show abstract

“…According to the data on train delays [24][25][26], there are four major sources of the TOCs: equipment facilities, human behaviour, external environment, and organization and management.…”

Section: Causes Of Tocsmentioning

confidence: 99%

Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway

Wang¹,

Kang²,

Song³

et al. 2020

IJSSE

View full text Add to dashboard Cite

To detect and mitigate the operation conflicts of high-speed trains, it is critical to clarify the generation mechanism of train operation conflicts (TOCs) in high speed railway (HSR). Taking train delays as the precondition of the TOCs, this paper sorts out the main causes of the TOCs into four aspects, namely, equipment facilities, human behaviour, external environment, and organization management, and obtains a total of direct and indirect impact factors for HSR TOCs. Then, the interpretative structural modelling (ISM) was adopted to construct the adjacency matrix between these factors, and calculate the reachability matrix. On this basis, a directed hierarchical graph was plotted for the TOC causes based on the hierarchical relationship between the factors. The results show that HSR TOCs are directly caused by equipment facilities, transport organizations, and dispatcher professionality, and indirectly induced by natural environment, equipment operating environment, unexpected passenger flow, as well as the psychological quality, educational level, and years of service of dispatchers; in addition, the working environment, and the management of equipment and dispatchers are the deep-seated reasons for the TOCs. The research results provide new insights into the intelligent dispatching command of the HSR.

show abstract

An ensemble prediction model for train delays

Cited by 62 publications

References 16 publications

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

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

Weighted Ensemble Methods for Predicting Train Delays

Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway

Contact Info

Product

Resources

About