Railway traffic on high density urban corridors: Capacity, signalling and timetable

Dicembre, Alessio; Ricci, Stefano

doi:10.1016/j.jrtpm.2011.11.001

Cited by 37 publications

(22 citation statements)

References 2 publications

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“…the Naples node) for comparison, validation and/or for a sensitivity analysis of the results of the proposed synthetic and macro methodology (as proposed for example in [42]). …”

Section: Resultsmentioning

confidence: 99%

Complex railway systems: capacity and utilisation of interconnected networks

Rotoli

Malavasi

Ricci

2016

Eur. Transp. Res. Rev.

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Introduction Worldwide the transport sector faces several issues related to the rising of traffic demand such as congestion, energy consumption, noise, pollution, safety, etc. Trying to stem the problem, the European Commission is encouraging a modal shift towards railway, considered as one of the key factors for the development of a more sustainable European transport system. The coveted increase in railway share of transport demand for the next decades and the attempt to open up the rail market (for freight, international and recently also local services) strengthen the attention to capacity usage of the system. This contribution proposes a synthetic methodology for the capacity and utilisation analysis of complex interconnected rail networks; the procedure has a dual scope since it allows both a theoretically robust examination of suburban rail systems and a solid approach to be applied, with few additional and consistent assumptions, for feasibility or strategic analysis of wide networks (by efficiently exploiting the use of Big Data and/or available Open Databases).Method In particular the approach proposes a schematization of typical elements of a rail network (stations and line segments) to be applied in case of lack of more detailed data; in the authors' opinion the strength points of the presented procedure stem from the flexibility of the applied synthetic methods and from the joint analysis of nodes and lines. The article, after building a quasiautomatic model to carry out several analyses by changing the border conditions or assumptions, even presents some general abacuses showing the variability of capacity/utilization of the network's elements in function of basic parameters. Results This has helped in both the presented case studies: one focuses on a detailed analysis of the Naples' suburban node, while the other tries to broaden the horizon by examining the whole European rail network with a more specific zoom on the Belgium area. The first application shows how the procedure can be applied in case of availability of fine-grained data and for metropolitan/regional analysis, allowing a precise detection of possible bottlenecks in the system and the individuation of possible interventions to relieve the high usage rate of these elements. The second application represents an on-going attempt to provide a broad analysis of capacity and related parameters for the entire European railway system. It explores the potentiality of the approach and the possible exploitation of different 'Open and Big Data' sources, but the outcomes underline the necessity to rely on proper and adequate information; the accuracy of the results significantly depend on the design and precision of the input database. Conclusion In conclusion, the proposed methodology aims to evaluate capacity and utilisation rates of rail systems at different geographical scales and according to data availability; the outcomes might provide valuable information to allow efficient exploitation and deployment of railway infrastructure, be...

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“…the Naples node) for comparison, validation and/or for a sensitivity analysis of the results of the proposed synthetic and macro methodology (as proposed for example in [42]). …”

Section: Resultsmentioning

confidence: 99%

Complex railway systems: capacity and utilisation of interconnected networks

Rotoli

Malavasi

Ricci

2016

Eur. Transp. Res. Rev.

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“…Nystrom and Soderholm 63 Dadashi et al 59 Nystrom and Soderholm 63 Jiang et al 79 Selvik and Aven 77 Nash et al 36 Jabri et al 50 Lv et al 57 Data mining Goverde and Meng 15 Kecman and Goverde 17 Kecman and Goverde 17 Kecman and Goverde 17 Autonomous systems Firlik et al 32 Li et al 65 Kuckelberg and Wendler 37 Xun et al 52 Dominguez et al 12 Wackrow and Slamen 13 Expert and decision support systems Dadashi et al 59 Bouillaut et al 61 Filip et al 70 Lai and Wang 73 Saa et al 29 Bouillaut et al 61 Guler 62 Schö bel and Maly 75 Soh et al 78 Zhang 31 Firlik et al 32 Schlake et al 84 Wegele et al 35 Kuckelberg and Wendler 37 Forsgren et al 39 Ho et al 38 Albrecht et al 40 Hu et al 5 Peng et al 80 Palte 30 Beugin et al 34 Kecman and Goverde 17 Filip et al 71 Lai and Wang 73 disruption such as faulty trains and damage within the infrastructure (Briola et al 23 ).…”

Section: Kementioning

confidence: 99%

A review of key planning and scheduling in the rail industry in Europe and UK

Turner

Tiwari

Starr

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part F:

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Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.

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“…An adequate method of evaluating how a system reacts to delays and how changes affect this is simulation, as described for example in [3,10]. But capacity might also include a wider perspective, for example market aspects: do the services on a specific line sequence during a specific time period satisfy the desired frequencies and departure or arrival times for all services on the line?…”

Section: Definition Of Track Capacitymentioning

confidence: 99%