Applied analysis for improving rail-network operations

Ceder, Avishai; Hassold, Stephan

doi:10.1016/j.jrtpm.2015.06.001

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Calendar variables intend to capture train delay distribution over different periods of time. For instance, trains dwell longer at stations to serve more passengers during peak hours (Li et al, 2016); arrival delays vary with seasons (Laifa et al, 2021), months of the year (Grandhi et al, 2021), and days of the week (Ceder and Hassold, 2015). Weather variables refer to variables that quantify the state of the atmosphere, such as temperature, wind speed, snow depth, and rainfall.…”

Section: Explainatory Variablesmentioning

confidence: 99%

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Tiong

Palmqvist

2023

Preprint

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Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper proposes a set of equations conditional on the train location for analyzing train arrival delay factors at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between regression residuals caused by shared unobserved variables among equations. The railway data from 2017 to 2020 in Sweden are used to validate the proposed model and explore the effects of various factors on train arrival delays. The results confirm the necessity of developing a set of station-specific train arrival delay models to understand the heterogeneous impact of explanatory variables. The results show that the significant factors impacting train arrival delays are primarily train operations, including dwell times, running times, and operation delays from previous trains and upstream stations. The factors of the calendar, weather, and maintenance are also significant in impacting delays. Importantly, different train operating management strategies should be targeted at different stations since the impacts of these factors could vary depending on where the station is.

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Section: Explainatory Variablesmentioning

confidence: 99%

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Tiong

Palmqvist

2023

Preprint

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“…Timetable performance measures are reliability, punctuality and robustness. Reliability is the ability of a system or a component to perform its required functions under stated conditions for a specified period of time [47]; punctuality is usually defined as the probability of a train arriving less than x minutes late [48]; and robustness refers to the capability of avoiding delay propagation as much as possible [49]. A robust timetable is generally designed by suitably introducing buffer times for absorbing potential delays.…”

Section: The Timetabling Phasementioning

confidence: 99%

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Botte

D’Acierno

2018

Urban Rail Transit

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The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown.

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“…Much preparation activity falls under the jurisdiction of the NR with responsibility for track infrastructure. Thus, gamification requirements for preparedness of a TOC should be focused on their employees: crew scheduling (Ceder et al, 2015), frontline platform staff, passengers and supporting systems. Herein lies the scope for improvement inspired by the professional emergency services.…”

Section: Preparednessmentioning

confidence: 99%

Analysis of a train-operating company's customer service system during disruptions: Conceptual requirements for gamifying frontline staff development

Clegg

Orme

Owen

et al. 2018

Journal of Rail Transport Planning & Management

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This paper provides an account of an action research study into the systemic success factors which help frontline staff react to and recover from a rail service disruption. This study focuses on the effective use of information during a disruption to improve customer service, as this is a priority area for train-operating companies (TOCs) in Great Britain.A novel type of systems thinking, known as Process-Oriented Holonic Modelling (PrOH), has been used to investigate and model the 'Passenger Information During Disruption' (PIDD) system. This paper presents conceptual requirements for a gamified learning environment; it describes 'what'; 'how' and 'when' these systemic success factors could be gamified using a popular disruption management reference framework known as the Mitigate, Prepare, React and Recover (MPRR) framework. This paper will interest managers of and researchers into customer service system disruptions, as well as those wishing to develop new gamified learning environments to improve customer service systems.

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Applied analysis for improving rail-network operations

Cited by 10 publications

References 22 publications

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Analysis of a train-operating company's customer service system during disruptions: Conceptual requirements for gamifying frontline staff development

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