The effect of temporary speed restrictions, analyzed by using real train traffic data

Kooij, Rimmert B.K. van der; Landmark, Andreas D.; Seim, Andreas Amdahl; Olsson, Nils O.E.

doi:10.1016/j.trpro.2017.03.047

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Previous research has found that train length has an impact on both passenger and freight train punctuality [70], [71]. However, Van Der Kooij et al, discovered that enforcing temporary speed restrictions on longer and heavier passenger trains to safeguard the use of infrastructure could produce significant network delays [72].…”

Section: Discussionmentioning

confidence: 99%

Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models

et al. 2023

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Despite rail's growing popularity as a mode of freight transportation due to its role in intermodal transportation and numerous economic and environmental benefits, optimizing all aspects of rail infrastructure use remains a significant challenge. To address this issue, various methods for developing train disruption prediction models have been used. However, these models continue to struggle with accurately predicting short-term arrival delay times, as well as identifying the causes of delays and the expected impact on operations. The lack of information available to operators makes it difficult for them to effectively mitigate the effects of disruptions. The goal of this study is to investigate a set of data-driven models for the short-term prediction of arrival delay time using data from the National Railway Company of Luxembourg of freight rail operations between Bettembourg (Luxembourg) and other nine terminal stations across the EU, and then investigate the effects of the features associated with the arrival delay time. For our dataset, the lightGBM model outperformed other models in predicting the arrival delay time in freight rail operations, with departure delay time, trip distance, and train composition appearing to be the most influential features in predicting the arrival delay time in the short-term. The National Railway Company of Luxembourg can use the short-term prediction model developed in this study as a decision-support system. For example, knowing a train's arrival delay time allows you to estimate future operational time, providing more support to reduce disruptions and subsequent operational delays via a simple web service.INDEX TERMS Data-driven models, delays forecasting, freight transport, gradient boosting, rail operation delays.

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Section: Discussionmentioning

confidence: 99%

Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models

et al. 2023

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“…Trackwork refers to the maintenance or renewal of railway infrastructure components that require planned temporary capacity restrictions for the section on the track where the activity is taking place. Such limitations can include complete track closures, reduced speed limits, or switching to single-track operations [3,4]. These restrictions might lead to train delays, thus often requiring adjustments in train schedules [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing body of research focusing on identifying the factors behind train delays, with notable contributions from various studies [3,5,[15][16][17][18]. Among the methodologies employed, statistical regression models, machine learning, neural networks, and hybrid approaches are frequently used to analyse and predict railway operation delays [18].…”

Section: Introductionmentioning

confidence: 99%

The Downside of Upkeep: Analysing Railway Infrastructure Maintenance Impact on Train Operations in Sweden

Ivina,

Palmqvist

2023

Applied Sciences

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Efficient and seamless railway operations depend on the systematic and well-coordinated maintenance of both rolling stock and infrastructure. However, track maintenance, or ‘trackwork’, can cause substantial delays if not properly aligned with train schedules. This study comprehensively investigates how trackwork influences train operations in Sweden. It involves an in-depth analysis of an extensive dataset comprising over 225,000 recorded instances of planned trackwork and approximately 32.5 million train passages throughout the year 2017. Multiple logistic and negative binomial regression models showed that train running time delay occurrence is higher in the sections with scheduled trackwork. Trains passing through trackwork are 1.43 times more likely to experience delays compared to trains that do not pass through scheduled trackwork. The likelihood of an opportunity for the train delay recovery passing the section with scheduled trackwork is reduced by 11%. Additionally, the frequency of train delay increase is 16% higher, and delayed recovery is 4% lower in relation to trackwork. With the number of trackwork set to increase over the coming years, these results bring attention to train scheduling and the performance of trackwork.

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“…The development of speed over time in a restricted sector is described as: braking until the restricted speed is reached, travel at a constant but reduced speed and an acceleration at the exit from the restricted area until the set speed is reached. In addition to delays, speed restrictions directly cause increases in the electricity consumption by the rolling stock, proportional to the number of affected sections and the speed limit in force [3].…”

Section: Introductionmentioning

confidence: 99%

Calculation algorithm and method for optimizing electricity consumption for the railway traction system

Popov

Fratu

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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This article presents a calculation algorithm for establishing the electricity demand consumed by the railway electric traction system on a railway section, as well as methods for lowering power consumption and cost. In order to conceive the calculation algorithm, the following variables are taken into account: the number of trains in circulation on the section, the slope of the section, the train speed and the speed restrictions. For factors that substantially increase electricity consumption, but can be controlled, optimal values are computed using specific equations in the MATLAB program. The optimal values are implemented using the train’s timetable or a railway control system (SCADA). A decrease in power consumption of 5% can be expected with this method.

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The effect of temporary speed restrictions, analyzed by using real train traffic data

Cited by 10 publications

References 13 publications

Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models

Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models

The Downside of Upkeep: Analysing Railway Infrastructure Maintenance Impact on Train Operations in Sweden

Calculation algorithm and method for optimizing electricity consumption for the railway traction system

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