Abstract

The aim of this paper is to provide an analytical approach for determining operational parameters for metro systems so as to support the planning and implementation of energy-saving strategies. Indeed, one of the main targets of train operating companies is to identify and implement suitable strategies for reducing energy consumption. For this purpose, researchers and practitioners have developed energy-efficient driving profiles with the aim of optimising train motion. However, as such profiles generally entail an increase in travel times, the operating parameters in the planned timetable need to be appropriately recalibrated. Against this background, this paper develops a suitable methodology for estimating reserve times which represent the main rate of extra time needed to put ecodriving strategies in place. Our proposal is to exploit layover times (i.e., times spent by a train at the terminus waiting for the next trip) for energy-saving purposes, keeping buffer times intact in order to preserve the flexibility and robustness of the timetable in case of delays. In order to show its feasibility, the approach was applied in the case of a real metro context, whose service frequency was duly taken into account. In particular, after stochastic analysis of the parameters involved for calibrating suitable buffer times, different operating schemes were simulated by analysing the relationship between layover times, number of convoys, and feasible headway values. Finally, some operation configurations are analysed in order to quantify the amount of energy that can be saved.

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2018/5983250.xml,
http://dx.doi.org/10.1155/2018/5983250 under the license http://creativecommons.org/licenses/by/4.0
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
http://downloads.hindawi.com/journals/jat/2018/5983250.pdf,
https://core.ac.uk/display/154859019,
https://doi.org/10.1155%2f2018%2f5983250,
https://academic.microsoft.com/#/detail/2801314480
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1155/2018/5983250
Licence: Other

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