Increasing metro line capacity by optimisation of track circuit in a speed code Automatic Train Protection system

Abstract

When analyzing a railway operation, multiple issues must be taken into account to obtain the best results in terms of safety, efficiency and flexibility. In this project, carried out in collaboration between CITEF and a European metro line, a tool has been developed and implemented. Based on the line data provided by the metro line, the signalling system (distance to go/speed signalling), the main characteristics of the rolling stock, etc., information can be obtained for modelling/modifying the line, changing or removing/inserting track circuits or even changing the maximum running speeds on those track circuits. Travel times, line frequencies, bottlenecks, and degraded conditions are also analyzed from the simulations, so that finally the best headway to be implemented on the line can be proposed for the user, who is able to know how to improve the main line features like capacity and frequency of trains. Integrating distance to go and speed signalling into the set of parameters, is another capability proposed. Fundamental information on the signalling system capacity can be obtained simply by running the algorithms designed for this task along with the changes in order to carry it out.

Keywords

Optimisation capacity distance to go speed signalling track circuits headway

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References

Brazelton SL, Curtiss DF, Julich PM, et al. Evaluating the potential benefits of a rail movement planning algorithm. In: Proceedings of the 1999 Winter simulation conference, Squaw Peak, Phoenix, 5–8 December 1999, Vol. 2, pp.1181–1185.

Rangaraj N and Vishnu BN. Node capacity and terminal management on indian railways. In: Vision 2025, Vadodra Railway Staff College, India, 2002.

Ditmeyer SR. Weather information and intelligent railroad systems. In: Proceedings of the symposium on enhanced weather information for improved railroad safety and productivity, Boulder, Colorado, 17–18 October 2001, p.22.

Herrman

Burkolter

Caimi

. Generating dense railway schedules. Advanced OR and AI Methods in Transportation 2005; 1: 290–297.

Abril M, Tormos P, Lova A, et al. A topological model based on railway capacity to manage periodic train scheduling. In: Applications and Innovations in Intelligent Systems. vol. XII, London: Springer, pp.107–120.

Leachman RC, Dessouky MM, Lu Q. Using simulation modeling to assess rail track infrastructure in densely trafficked metropolitan areas. In: Proceedings of the Winter simulation conference, San Diego, CA, USA, 8–11 December 2002, Vol. 1, pp.725–773.

Chang

. Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system. IEEE Proc Elec Power Applic 2000; 147: 206–212.

Feoftiskov

. Differential Evolution: In Search of Solutions, New York: Springer, 2006.

Jung M, Peters J, Emig B, et al. Prediction of delays in public transportation using neural networks. In: International conference on computational intelligence for modelling, control and automation and the international conference on intelligent agents, web technologies and Internet commerce (IAWTIC 2005), Institute of Electrical and Electronics Engineers Computer Society, Piscataway, p.92.

10.

Blanquer

Mera

Castellote

. Increase of metro line capacity by optimisation of track circuit length and location: In a distance to go system. J Adv Transport 2010; 44: 53–71.

11.

Arrival Project. Algorithms for robust and online railway optimization: Improving the validity and reliability of large scale systems. FET (Future and Emerging Technologies) Unit of the European Commission (EC), 2006.