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[1] M agnanti, T. & Wong, R., Network design and transportation planning: Models and algorithms. Transportation Science, 18(1), pp. 1–55, 1984. [Crossref]
[2] Y ang, H. & Bell, M.G.H., Models and algorithms for road network design: A review and some new developments. Transport Reviews: A Transnational Transdisciplinary Journal, 18(3), pp. 257–278, 1998. [Crossref]
[3] Feremans, C., Labbé, M. & Laporte, G., Generalized network design problems. European Journal of Operational Research, 148(1), pp. 1–13, 2003. [Crossref]
[4] B illheimer, J.W. & Gray, P., Network design with fixed and variable cost elements. Transportation Science, 7(1), pp. 49–74, 1973. [Crossref]
[5] G ao, Z., Wu, J. & Sun, H., Solution algorithm for the bi-level discrete network design problem. Transportation Research Part B, 39(6), pp. 479–495, 2005. [Crossref]
[6] Dantzig, G.B., Maier, S.F., Harvey, R.P., Lansdowne, Z.F. & Robinson, D.W. Formulating and solving the network design problem by decomposition. Transportation Research Part B, 13(1), pp. 5–17, 1979. [Crossref]
[7] C ascetta, E., Gallo, M. & Montella, B., Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment. Annals of Operations Research, 144(1), pp. 301–328, 2006. [Crossref]
[8] A dacher, L. & Cipriani, E. A surrogate approach for the global optimization of signal settings and traffic assignment problem. Proceedings of the 13th IEEE Conference on Intelligent Transportation Systems (ITSC), Funchal Island, Portugal, pp. 60–65, 2010. [Crossref]
[9] C aggiani, L. & Ottomanelli, M., Traffic equilibrium network design problem under uncertain constraints. Procedia – Social and Behavioral Sciences, 20, pp. 372–380, 2011. [Crossref]
[10] G allo, M., D’Acierno, L. & Montella, B., Global optimisation of signal settings: Metaheuristic algorithms for solving real-scale problems. Advances in Intelligent Systems and Computing, 262, pp. 177–193, 2014. [Crossref]
[11] C antarella, G. & Vitetta, A., The multi-criteria road network design problem in an urban area. Transportation, 33(6), pp. 567–588, 2006. [Crossref]
[12] C antarella, G., Pavone, G. & Vitetta, A., Heuristics for urban road network design: Lane layout and signal settings. European Journal of Operational Research, 175(3), pp. 1682–1695, 2006. [Crossref]
[13] Desaulniers, G. & Hickman, M., Public transit. Transportation, eds. C. Barnhart & G. Laporte, North-Holland: Amsterdam, The Netherlands, pp. 69–120, 2007.
[14] G uihaire, V. & Hao, J.K., Transit network design and scheduling: A global review. Transportation Research Part A, 42(10), pp. 1251–1273, 2008. [Crossref]
[15] Kepaptsoglou, K. & Karlaftis, M. Transit route network design problem: Review. Journal of Transportation Engineering, 135(8), pp. 491–505, 2009. [Crossref]
[16] Salzborn, F.J.M., Optimum bus scheduling. Transportation Science, 6(2), pp. 137–148, 1972. [Crossref]
[17] Y u, B., Yang, Z. & Yao, J., Genetic algorithm for bus frequency optimization. Journal of Transportation Engineering, 136(6), pp. 576–583, 2010. TE.1943-5436.0000119. [Crossref]
[18] G allo, M., D’Acierno, L. & Montella, B., A multimodal approach to bus frequency design. WIT Transactions on the Built Environment, 116, pp. 193–204, 2011. [Crossref]
[19] G allo, M., Montella, B. & D’Acierno, L., The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisation. Transportation Research Part C, 19(6), pp. 1276–1305, 2011. [Crossref]
[20] L aporte, G., Mesa, J.A. & Perea, F., A game theoretic framework for the robust railway transit network design problem. Transportation Research Part B, 44(4), pp. 447–459, 2010. [Crossref]
[21] Van Nes, R., Hamerslag, R. & Immers, B.H., Design of public transport networks. Transportation Research Record, 1202, pp. 74–83, 1988.
[22] L ee, Y.J. & Vuchic, V.R., Transit network design with variable demand. Journal of Transportation Engineering, 131(1), pp. 1–10, 2006. [Crossref]
[23] M arìn, A.G. & Garcìa-Ròdenas, R., Location of infrastructure in urban railway networks. Computers & Operations Research, 36(5), pp. 1461–1477, 2009.
[24] D'Acierno, L., Gallo, M., Biggiero, L. & Montella, B., Replanning public transport services in the case of budget reductions. WIT Transactions on the Built Environment, 138, pp. 77–88, 2014. [Crossref]
[25] G allo, M., Simonelli, F., De Luca, G. & De Martinis, V., Estimating the effects of energy-efficient driving profiles on railway consumption. Proceedings of the 15th IEEE International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, pp. 1059–1064, 2015. [Crossref]
[26] Pariota, L. & Bifulco, G.N., Experimental evidence supporting simpler Action Point paradigms for car-following. Transportation Research Part F, 35, pp. 1–15, 2015. [Crossref]
[27] B ifulco, G.N., Galante, F., Pariota, L. & Russo-Spena, M., A linear model for the estimation of fuel consumption and the impact evaluation of Advanced Driving Assistance Systems. Sustainability, 7(10), pp. 14326–14343, 2015. [Crossref]
[28] Dell’Acqua, G., Russo, F. & Biancardo, S.A., Risk-type density diagrams by crash type on two-lane rural roads. Journal of Risk Research, 16(10), pp. 1297–1314, 2013. [Crossref]
[29] C antarella, G.E., A general fixed-point approach to multimode multi-user equilibrium assignment with elastic demand. Transportation Science, 31(2), pp. 107–128, 1997. [Crossref]
[30] Nguyen, S., Pallottino, S. & Gendreau, M., Implicit enumeration of hyperpaths in a logit model for transit networks. Transportation Science, 32(1), pp. 54–64, 1998. [Crossref]
[31] C ascetta, E., Transportation systems analysis: models and applications. Springer: New York, 2009.
[32] D'Acierno, L., Gallo, M. & Montella, B., A fixed-point model and solution algorithms for simulating urban freight distribution in a multimodal context. Journal of Applied Sciences, 11(4), pp. 647–654, 2011. [Crossref]
[33] Kettner, M. & Sewcyk, B., A model for transportation planning and railway network evaluation. Proceedings of the 9th World Congress on Intelligent Transport Systems, Chicago, IL, 14–17 October 2012.
[34] Prinz, R., Sewcyk, B. & Kettner, M., NEMO: Network evaluation model for the Austrian railroad (ÖBB). Eisenbahntechnische Rundschau, 50(3), pp. 117–121, 2001.
[35] M arinov, M. & Viegas, J., A mesoscopic simulation modelling methodology for analyzing and evaluating freight train operations in a rail network. Simulation Modelling Practice and Theory, 19(1), pp. 516–539, 2011. [Crossref]
[36] Nash, A. & Huerlimann, D., Railroad simulation using OpenTrack. Computers in Railways, 9, pp. 45–54, 2004.
[37] Siefer, T. & Radtke, A., Railway simulation: key for better operation and optimal use of infrastructure. Proceedings of the 1st International Seminar on Railway Operations Modelling and Analysis, Delft, The Netherlands, 8–10 June 2005.
[38] B atley, R., Dargay, J. & Wardman, M., The impact of lateness and reliability on passenger rail demand. Transportation Research Part E, 47(1), pp. 61–72, 2011. [Crossref]
[39] M ontella, B., Gallo, M. & D’Acierno, L., Multimodal network design problems. Advances in Transport, 5, pp. 405–414, 2000.
[40] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction between travel demand and rail capacity constraints. WIT Transactions on the Built Environment, 128, pp. 197–207, 2012. [Crossref]
[41] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model framework for managing rail systems in the case of breakdowns. Proceedings of the 16th IEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, pp. 1059–1064, 2013. [Crossref]
[42] D’Acierno, L., Placido, A., Botte, M. & Montella, B., A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, 10, pp. 80–86, 2016.
[43] D’Acierno, L., Gallo, M. & Montella, B., Application of metaheuristics to large-scale transportation problems. Lecture Notes in Computer Science, 8353, pp. 215–222, 2014. [Crossref]
[44] E rcolani, M., Placido, A., D’Acierno, L. & Montella, B., The use of microsimulation models for the planning and management of metro systems. WIT Transactions on the Built Environment, 135, pp. 509–521, 2014. [Crossref]
[45] C artenì, A., Galante, G. & Henke, I., An assessment of models accuracy in predicting railways traffic flows: A before and after study in Naples. WIT Transactions on Ecology and the Environment, 191, pp. 783–794, 2014. [Crossref]
[46] C aropreso, C., Di Salvo, C., Botte, M. & D’Acierno, L., A long term analysis of passenger flows on a regional rail line. WIT Transactions on the Built Environment, 162, 2016.
[47] Di Mauro, R., Botte, M. & D’Acierno, L., An analytical methodology for extending passenger counts in a metro system. WIT Transactions on the Built Environment, 162, 2016.
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Open Access
Research article

A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures

m. botte,
c. di salvo,
a. placido,
b. montella,
l. d'acierno
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Italy
International Journal of Transport Development and Integration
|
Volume 1, Issue 1, 2016
|
Pages 63-73
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

In high-density contexts, such as urban or metropolitan areas, decision makers and mobility managers have to adopt suitable strategies to reduce the use of private cars and promote public transport. Indeed, such strategies may help abate the negative impacts of transportation systems (congestion, air and noise pollution, etc.). However, appropriate measures are only effective if based on the provision of high-quality public transport services. Such aims can be achieved by organizing public transport within an integrated framework where rail/metro services are the high-performing mobility backbone and bus services have a feeder function, increasing the geographical coverage of rail services. However, since a faulty train cannot be easily removed or overtaken, a rail/metro system is highly vulnerable to system breakdowns which could entail significant reductions in system quality. Suitable intervention strategies therefore have to be developed to manage rail system emergencies. The aim of this article is to provide a method to determine optimal intervention strategies in the case of a metro system failure. Since in real contexts an exhaustive approach has to be excluded due to the huge number of alternative solutions to be evaluated, it is necessary to adopt or develop appropriate algorithms to obtain sub-optimal solutions within suitable computational times. Hence a Neighbourhood Search Algorithm to identify the optimal solution is applied and tested in the case of a real metro line in order to show the feasibility of our proposal.

Keywords: Heuristic algorithms, Metro system management, Micro-simulation approach, Public transport, Optimization models, Real scale networks
Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
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[2] Y ang, H. & Bell, M.G.H., Models and algorithms for road network design: A review and some new developments. Transport Reviews: A Transnational Transdisciplinary Journal, 18(3), pp. 257–278, 1998. [Crossref]
[3] Feremans, C., Labbé, M. & Laporte, G., Generalized network design problems. European Journal of Operational Research, 148(1), pp. 1–13, 2003. [Crossref]
[4] B illheimer, J.W. & Gray, P., Network design with fixed and variable cost elements. Transportation Science, 7(1), pp. 49–74, 1973. [Crossref]
[5] G ao, Z., Wu, J. & Sun, H., Solution algorithm for the bi-level discrete network design problem. Transportation Research Part B, 39(6), pp. 479–495, 2005. [Crossref]
[6] Dantzig, G.B., Maier, S.F., Harvey, R.P., Lansdowne, Z.F. & Robinson, D.W. Formulating and solving the network design problem by decomposition. Transportation Research Part B, 13(1), pp. 5–17, 1979. [Crossref]
[7] C ascetta, E., Gallo, M. & Montella, B., Models and algorithms for the optimization of signal settings on urban networks with stochastic assignment. Annals of Operations Research, 144(1), pp. 301–328, 2006. [Crossref]
[8] A dacher, L. & Cipriani, E. A surrogate approach for the global optimization of signal settings and traffic assignment problem. Proceedings of the 13th IEEE Conference on Intelligent Transportation Systems (ITSC), Funchal Island, Portugal, pp. 60–65, 2010. [Crossref]
[9] C aggiani, L. & Ottomanelli, M., Traffic equilibrium network design problem under uncertain constraints. Procedia – Social and Behavioral Sciences, 20, pp. 372–380, 2011. [Crossref]
[10] G allo, M., D’Acierno, L. & Montella, B., Global optimisation of signal settings: Metaheuristic algorithms for solving real-scale problems. Advances in Intelligent Systems and Computing, 262, pp. 177–193, 2014. [Crossref]
[11] C antarella, G. & Vitetta, A., The multi-criteria road network design problem in an urban area. Transportation, 33(6), pp. 567–588, 2006. [Crossref]
[12] C antarella, G., Pavone, G. & Vitetta, A., Heuristics for urban road network design: Lane layout and signal settings. European Journal of Operational Research, 175(3), pp. 1682–1695, 2006. [Crossref]
[13] Desaulniers, G. & Hickman, M., Public transit. Transportation, eds. C. Barnhart & G. Laporte, North-Holland: Amsterdam, The Netherlands, pp. 69–120, 2007.
[14] G uihaire, V. & Hao, J.K., Transit network design and scheduling: A global review. Transportation Research Part A, 42(10), pp. 1251–1273, 2008. [Crossref]
[15] Kepaptsoglou, K. & Karlaftis, M. Transit route network design problem: Review. Journal of Transportation Engineering, 135(8), pp. 491–505, 2009. [Crossref]
[16] Salzborn, F.J.M., Optimum bus scheduling. Transportation Science, 6(2), pp. 137–148, 1972. [Crossref]
[17] Y u, B., Yang, Z. & Yao, J., Genetic algorithm for bus frequency optimization. Journal of Transportation Engineering, 136(6), pp. 576–583, 2010. TE.1943-5436.0000119. [Crossref]
[18] G allo, M., D’Acierno, L. & Montella, B., A multimodal approach to bus frequency design. WIT Transactions on the Built Environment, 116, pp. 193–204, 2011. [Crossref]
[19] G allo, M., Montella, B. & D’Acierno, L., The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisation. Transportation Research Part C, 19(6), pp. 1276–1305, 2011. [Crossref]
[20] L aporte, G., Mesa, J.A. & Perea, F., A game theoretic framework for the robust railway transit network design problem. Transportation Research Part B, 44(4), pp. 447–459, 2010. [Crossref]
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[24] D'Acierno, L., Gallo, M., Biggiero, L. & Montella, B., Replanning public transport services in the case of budget reductions. WIT Transactions on the Built Environment, 138, pp. 77–88, 2014. [Crossref]
[25] G allo, M., Simonelli, F., De Luca, G. & De Martinis, V., Estimating the effects of energy-efficient driving profiles on railway consumption. Proceedings of the 15th IEEE International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, pp. 1059–1064, 2015. [Crossref]
[26] Pariota, L. & Bifulco, G.N., Experimental evidence supporting simpler Action Point paradigms for car-following. Transportation Research Part F, 35, pp. 1–15, 2015. [Crossref]
[27] B ifulco, G.N., Galante, F., Pariota, L. & Russo-Spena, M., A linear model for the estimation of fuel consumption and the impact evaluation of Advanced Driving Assistance Systems. Sustainability, 7(10), pp. 14326–14343, 2015. [Crossref]
[28] Dell’Acqua, G., Russo, F. & Biancardo, S.A., Risk-type density diagrams by crash type on two-lane rural roads. Journal of Risk Research, 16(10), pp. 1297–1314, 2013. [Crossref]
[29] C antarella, G.E., A general fixed-point approach to multimode multi-user equilibrium assignment with elastic demand. Transportation Science, 31(2), pp. 107–128, 1997. [Crossref]
[30] Nguyen, S., Pallottino, S. & Gendreau, M., Implicit enumeration of hyperpaths in a logit model for transit networks. Transportation Science, 32(1), pp. 54–64, 1998. [Crossref]
[31] C ascetta, E., Transportation systems analysis: models and applications. Springer: New York, 2009.
[32] D'Acierno, L., Gallo, M. & Montella, B., A fixed-point model and solution algorithms for simulating urban freight distribution in a multimodal context. Journal of Applied Sciences, 11(4), pp. 647–654, 2011. [Crossref]
[33] Kettner, M. & Sewcyk, B., A model for transportation planning and railway network evaluation. Proceedings of the 9th World Congress on Intelligent Transport Systems, Chicago, IL, 14–17 October 2012.
[34] Prinz, R., Sewcyk, B. & Kettner, M., NEMO: Network evaluation model for the Austrian railroad (ÖBB). Eisenbahntechnische Rundschau, 50(3), pp. 117–121, 2001.
[35] M arinov, M. & Viegas, J., A mesoscopic simulation modelling methodology for analyzing and evaluating freight train operations in a rail network. Simulation Modelling Practice and Theory, 19(1), pp. 516–539, 2011. [Crossref]
[36] Nash, A. & Huerlimann, D., Railroad simulation using OpenTrack. Computers in Railways, 9, pp. 45–54, 2004.
[37] Siefer, T. & Radtke, A., Railway simulation: key for better operation and optimal use of infrastructure. Proceedings of the 1st International Seminar on Railway Operations Modelling and Analysis, Delft, The Netherlands, 8–10 June 2005.
[38] B atley, R., Dargay, J. & Wardman, M., The impact of lateness and reliability on passenger rail demand. Transportation Research Part E, 47(1), pp. 61–72, 2011. [Crossref]
[39] M ontella, B., Gallo, M. & D’Acierno, L., Multimodal network design problems. Advances in Transport, 5, pp. 405–414, 2000.
[40] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction between travel demand and rail capacity constraints. WIT Transactions on the Built Environment, 128, pp. 197–207, 2012. [Crossref]
[41] D’Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model framework for managing rail systems in the case of breakdowns. Proceedings of the 16th IEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, pp. 1059–1064, 2013. [Crossref]
[42] D’Acierno, L., Placido, A., Botte, M. & Montella, B., A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, 10, pp. 80–86, 2016.
[43] D’Acierno, L., Gallo, M. & Montella, B., Application of metaheuristics to large-scale transportation problems. Lecture Notes in Computer Science, 8353, pp. 215–222, 2014. [Crossref]
[44] E rcolani, M., Placido, A., D’Acierno, L. & Montella, B., The use of microsimulation models for the planning and management of metro systems. WIT Transactions on the Built Environment, 135, pp. 509–521, 2014. [Crossref]
[45] C artenì, A., Galante, G. & Henke, I., An assessment of models accuracy in predicting railways traffic flows: A before and after study in Naples. WIT Transactions on Ecology and the Environment, 191, pp. 783–794, 2014. [Crossref]
[46] C aropreso, C., Di Salvo, C., Botte, M. & D’Acierno, L., A long term analysis of passenger flows on a regional rail line. WIT Transactions on the Built Environment, 162, 2016.
[47] Di Mauro, R., Botte, M. & D’Acierno, L., An analytical methodology for extending passenger counts in a metro system. WIT Transactions on the Built Environment, 162, 2016.
Cite this:
APA Style
IEEE Style
BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Botte, M., Salvo, C. D., Placido, A., Montella, B., & D'acierno, L. (2016). A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures. Int. J. Transp. Dev. Integr., 1(1), 63-73. https://doi.org/10.2495/TDI-V1-N1-63-73
M. Botte, C. D. Salvo, A. Placido, B. Montella, and L. D'acierno, "A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures," Int. J. Transp. Dev. Integr., vol. 1, no. 1, pp. 63-73, 2016. https://doi.org/10.2495/TDI-V1-N1-63-73
@research-article{Botte2016ANS,
title={A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures},
author={M. Botte and C. Di Salvo and A. Placido and B. Montella and L. D'Acierno},
journal={International Journal of Transport Development and Integration},
year={2016},
page={63-73},
doi={https://doi.org/10.2495/TDI-V1-N1-63-73}
}
M. Botte, et al. "A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures." International Journal of Transport Development and Integration, v 1, pp 63-73. doi: https://doi.org/10.2495/TDI-V1-N1-63-73
M. Botte, C. Di Salvo, A. Placido, B. Montella and L. D'Acierno. "A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures." International Journal of Transport Development and Integration, 1, (2016): 63-73. doi: https://doi.org/10.2495/TDI-V1-N1-63-73
BOTTE M， DI SALVO C， PLACIDO A， et al. A Neighbourhood Search Algorithm for Determining Optimal Intervention Strategies in the Case of Metro System Failures[J]. International Journal of Transport Development and Integration， 2016， 1(1)： 63-73. https://doi.org/10.2495/TDI-V1-N1-63-73