Energy savings in mixed rail traffic rescheduling: an RCG approach

Toletti, Ambra; Martinis, Valerio De; Weidmann, Ulrich

doi:10.1109/itsc.2016.7795947

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Their approach allows to specifically account for the different types of constraints induced by routing, scheduling, and optimization. In (Toletti et al, 2016) the authors design an enhanced version of the Resource Conflict Graph (RCG) approach for railway rescheduling that considers the reduction of tractive energy consumption in the rescheduling process. In Pellegrini et al (2012), the authors detail a possible solution to the rt-RTMP problem using MILP to minimize secondary delays in a case study that describes unexpected events which perturb the operations.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure Modeling and Optimization to Solve Real-time Railway Traffic Management Problems

Lindenmaier

Lövétei

Lukács

et al. 2021

Period. Polytech. Transp. Eng.

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Rail transportation helps to reach the global climate targets because it is characterized by low emission. The passenger and freight volumes on the railway increase yearly in line with EU targets. However, delays of passenger and freight trains decrease the punctuality and the reliability of the railway sector and the development of the infrastructure is not enough to increase the average speed of trains. Delays mean cost to the passengers, railway operators, infrastructure managers, and all railway undertakers. Therefore, the reason for the most significant optimization target is to minimize delays. In this paper, a possible solution has been described to solve the real-time railway traffic management problems by applying a mixed-integer linear programming approach. For validation of the research result, one simplified case study has been presented. Based on the result, the presented solution can provide effective support to dispatchers in solving real-time traffic management problems.

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Section: Related Workmentioning

confidence: 99%

Infrastructure Modeling and Optimization to Solve Real-time Railway Traffic Management Problems

Lindenmaier

Lövétei

Lukács

et al. 2021

Period. Polytech. Transp. Eng.

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“…In (Farooqi et al, 2018) a cooperative solution is presented by using Nonlinear Model Predictive Control, which was further extended with Dissension based Adaptive Law in (Farooqi et al, 2019.). Answer Set Programming was used in (Abels et al, 2019), whilt in (Toletti et al, 2016), the authors propose the utilization of Resource Conflict Graphs.…”

Section: Related Workmentioning

confidence: 99%

MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

Lövétei

Kővári

Bécsi

2021

Period. Polytech. Transp. Eng.

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Solving a real-time Railway Traffic Management Problem (rtRTMP) is a challenging task for human operators. To solve the traffic situation, many factors need to be considered. Traditionally, the most critical factor is the availability of the possible routes and the relative position of the vehicles to each other. Besides, additional constraints can be found, such as the velocity, the length, and railway company regulations. The human decision-making process is essential in case of any disturbance (deviation from the pre-planned timetable). The human operator may solve this situation, but generally, the solution is not optimal. In this paper, the authors present a new method, where they consider an MCTS based algorithm to solve the traffic situation in a fast way in a given station. The performance of the algorithm is examined in two abstraction levels. The main purpose is to execute an experimental study to examine the efficiency of the MCTS based algorithms to solve railway traffic situations.

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“…For this reason, several authors focused on the trade-off between energy saving and passenger satisfaction [292][293][294][295][296][297][298]. In general, [299][300][301] analysed the relation between energy-efficient strategies and stability of the planned timetable, while [302] analysed also the utilisation rate of train capacity resulting from the implementation of energy-saving strategies. Finally, [76] compared the minimum-energy timetable with those obtained by taking into account also rolling stock and other operational costs.…”

Section: Energy Issues Related To Rail Systemsmentioning

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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Energy savings in mixed rail traffic rescheduling: an RCG approach

Cited by 5 publications

References 20 publications

Infrastructure Modeling and Optimization to Solve Real-time Railway Traffic Management Problems

Infrastructure Modeling and Optimization to Solve Real-time Railway Traffic Management Problems

MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

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

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