A delay estimation technique for single and double-track railroads

Murali, Pavankumar; Dessouky, Maged; Ordóñez, Fernando; Palmer, Kurt

doi:10.1016/j.tre.2009.04.016

Cited by 66 publications

(33 citation statements)

References 19 publications

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“…Pachl [31] divided the capacity methodologies into two major classes: analytic and simulation. Similar categorization was used in research conducted by Murali on delay estimation technique [32]. Khadem Sameni, and Preston et al [9] categorized capacity methods to timetable-based and non-timetable-based approaches.…”

Section: Rolling Stock Characteristicsmentioning

confidence: 99%

“…Today, the simulation process utilizes computer tools to handle sophisticated computations and stochastic models in a faster and more efficient way. The simulation approaches use either general simulation tools, such as AweSim, Minitab, and Arena [32,38]; or commercial railroad simulation software specifically designed for rail transportation, such as RTC, MultiRail, RAILSIM, OpenTrack, RailSys, and CMS [9,30]. The use of general simulation tools requires the user to develop all models, equations, and constraints step by step (often manually).…”

Section: Simulation Approachmentioning

confidence: 99%

“…These studies were broken into several categories and subcategories for a comparison between the studies conducted in Europe versus in the U.S. Table 1 and the following discussion summarize the approach, tools used, Base and alternative results 1 study [32] 1 study [68] Railroad capacity tools and methodologies 39 study purpose, types of outcomes, and validation methods of the 25 studies selected for more detailed comparison. Approach Most studies used either simulation or combined analytical-simulation approaches.…”

Section: Detailed Assessment Of Selected Studiesmentioning

confidence: 99%

“…Approach Most studies used either simulation or combined analytical-simulation approaches. However, research conducted by AAR [2], University of Illinois at UrbanaChampaign (UIUC) [3,29], and University of Southern California (USC) [32] applied analytical-only methodologies.…”

Section: Detailed Assessment Of Selected Studiesmentioning

confidence: 99%

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Railroad capacity tools and methodologies in the U.S. and Europe

Pouryousef

Lautala

White³

2015

J. Mod. Transport.

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A growing demand for passenger and freight transportation, combined with limited capital to expand the United States (U.S.) rail infrastructure, is creating pressure for a more efficient use of the current line capacity. This is further exacerbated by the fact that most passenger rail services operate on corridors that are shared with freight traffic. A capacity analysis is one alternative to address the situation and there are various approaches, tools, and methodologies available for application. As the U.S. continues to develop higher speed passenger services with similar characteristics to those in European shared-use lines, understanding the common methods and tools used on both continents grows in relevance. There has not as yet been a detailed investigation as to how each continent approaches capacity analysis, and whether any benefits could be gained from cross-pollination. This paper utilizes more than 50 past capacity studies from the U.S. and Europe to describe the different railroad capacity definitions and approaches, and then categorizes them, based on each approach. The capacity methods are commonly divided into analytical and simulation methods, but this paper also introduces a third, ''combined simulationanalytical'' category. The paper concludes that European rail studies are more unified in terms of capacity, concepts, and techniques, while the U.S. studies represent a greater variation in methods, tools, and objectives. The majority of studies on both continents use either simulation or a combined simulation-analytical approach. However, due to the significant differences between operating philosophy and network characteristics of these two rail systems, European studies tend to use timetable-based simulation tools as opposed to the non-timetable-based tools commonly used in the U.S. rail networks. It was also found that validation of studies against actual operations was not typically completed or was limited to comparisons with a base model.

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Section: Rolling Stock Characteristicsmentioning

confidence: 99%

Section: Simulation Approachmentioning

confidence: 99%

Section: Detailed Assessment Of Selected Studiesmentioning

confidence: 99%

Section: Detailed Assessment Of Selected Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Railroad capacity tools and methodologies in the U.S. and Europe

Pouryousef

Lautala

White³

2015

J. Mod. Transport.

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“…The algorithms used to solve a system of equations may not be ideal for simple and accurate delay propagation. The effects of the interactions cannot be effectively captured in an analytical delay model [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Neuron Network in Analysis of Railway Delays

Hu¹,

Noche²

2016

JSS

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Punctuality is a key performance indicator of train freight transport. However, train delay arises often in the practice. To improve the efficiency of cargo train, prediction of train-delay is always an important research area. In this paper, a prediction model is established on the base of artificial neural network (ANN). Due the endogen drawback of ANN, Genetic Algorithm is adopted to improve the performance of ANN. Consequently, an experiment is design to train and test the ANN-based model by a set of data from the practice. The results of the experiment demonstrate the significant propagation ability of the model.

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SPSTN: Sequential Precoding Spatial-Temporal Networks for Railway Delay Prediction

Zhong

et al. 2023

Lecture Notes in Computer Science

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A delay estimation technique for single and double-track railroads

Cited by 66 publications

References 19 publications

Railroad capacity tools and methodologies in the U.S. and Europe

Railroad capacity tools and methodologies in the U.S. and Europe

Application of Artificial Neuron Network in Analysis of Railway Delays

SPSTN: Sequential Precoding Spatial-Temporal Networks for Railway Delay Prediction

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