Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities

Martinis, Valerio De; Corman, Francesco

doi:10.1016/j.trc.2018.08.008

Cited by 50 publications

(25 citation statements)

References 76 publications

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“…Nowadays, railway companies are focused on enhancing their performance during operation, for example, optimizing capacity utilization, reducing energy consumption for train operation, enhancing punctuality. Overall, having precise data on trains state has been proven required to achieve opportunities for improvement at system level (De Martinis & Corman, ). This paper has investigated the capabilities of EKF and PF to adopt power measurements as an additional source of information.…”

Section: Resultsmentioning

confidence: 99%

Filtering approaches for online train motion estimation with onboard power measurements

Sessa

Martinis

Corman

2019

Computer aided Civil Eng

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Future railway systems will need the support of more precise and reliable information on train motion during operation, to fully exploit the potential of new technologies. Train motion information are here intended as time series of train state characteristics, that is, position, speed, and acceleration. This paper investigates the use of two different filtering formulations for better online estimations of train state along the track. Specifically, extended Kalman filters (EKFs) and particle filters (PFs) are used to fuse kinematic measurements collected by means of Global Navigation Satellite Systems (GNSSs) with information collected by the trains on the used tractive power. The EKFs need linearity assumptions and are based on statistical evaluations of the train state, the PFs are instead conceived for nonlinear models and they are based on probability functions related to train state measurements and estimates. A set of experiments with real data of trains operating on a Swiss line is presented to analyze the capabilities of these two filters when applied to rail operation in non‐urban environments. We discuss the extent by which the proposed filtering approaches match the latest technical requirements for implementation. We expect such an enhanced train motion estimation to enable reliable and continuous positioning, available at train level and at the traffic control center, while trackside equipment will be gradually reduced.

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Section: Resultsmentioning

confidence: 99%

Filtering approaches for online train motion estimation with onboard power measurements

Sessa

Martinis

Corman

2019

Computer aided Civil Eng

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“…The presented discussion is intended to give a comprehensive picture of the key factors involved in reliable modelling of rail services and related issues. The underlying idea is that, ideally, a fully demand-oriented transport system, optimised for a full match between demand and vehicle capacity, can provide the best service with the minimum energy expenditure [359]. For this reason, dispatching tasks were reviewed together with travel demand estimation and energy issues.…”

Section: Discussionmentioning

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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“…The ideal brake curve assumes that the train will start to a fully brake regardless of the mechanical reaction time in the traction cut off and brake build up processes. Definition 11: The ideal brake curve of a train is a 1-manifold S i which is defined by (7). The s, s c , v and v c have the same meaning as above.…”

Section: Manifold-based Behaviour Of Trainsmentioning

confidence: 99%

“…The Shift2Rail aims to double the railway capacity in the future and virtual coupling technology is expected to prove an effective way to realise that goal [3]. A lot of research has explored towards the virtual coupling [4][5][6][7]. The capacity improvement under virtual coupling control strategy was assessed and demonstrated by an application in the UK [8].…”

Section: Introductionmentioning

confidence: 99%

Topological manifold‐based monitoring method for train‐centric virtual coupling control systems

Zhang

Wang

2020

IET Intelligent Transport Systems

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Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities

Cited by 50 publications

References 76 publications

Filtering approaches for online train motion estimation with onboard power measurements

Filtering approaches for online train motion estimation with onboard power measurements

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

Topological manifold‐based monitoring method for train‐centric virtual coupling control systems

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