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Capacity vs Reliability in railways: A stochastic micro-simulation approach

LONGO, GIOVANNI
•
MEDEOSSI, GIORGIO
2012
  • conference object

Abstract
Railway transport is increasing its strategic role at urban, national and international level both for passenger and freight mobility. In fact, road traffic congestion causes decreasing levels of service and railways can become more and more reliable thanks to recent investments in infrastructures and technology. In recent years, the unexpected economic crisis is forcing planners to find less expensive and easier to build measures, which effectiveness has to be demonstrated before being approved. As a result, quantitative methods have to be used, which allow a precise capacity estimati- on, also considering different timetable scenarios, interlocking systems and infrastructure layouts. Moreover, since a high traffic reliability level has to be offered, the effects of incre- asing traffic on punctuality have to be taken into consideration while estimating capacity. In this paper a methodology is presented, one which allows a precise estimation of the tra- de–off between capacity and reliability on railway networks and identifies the system bottle- necks. This methodology is based on stochastic micro–simulation of rail traffic, which has been calibrated using extensive real life data. The successful results obtained using the met- hodology in important sections of two Pan–European corridors are described and discussed in the second part of the paper. The first case study deals with the network between Trieste and Venice, on the Corridors N.5 and 23; it plays a crucial role at a continental level, since it represents the connection between Italy and all countries of Central and Eastern Europe. The second application focuses on the Croatian part of the 9 Corridor (Dobova–Zagreb–Tovarnik), which connects Germany and Austria with the Balkan Area.
Archivio
http://hdl.handle.net/11368/2721890
Diritti
metadata only access
Soggetti
  • Railway capacity

  • timetable reliability...

  • stochastic simulation...

Visualizzazioni
2
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
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