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Transport Network Analysis for Smart Open Fleets

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Part of the Communications in Computer and Information Science book series (CCIS,volume 722)

Abstract

Current techniques for intelligent computing based on Multi-Agent Systems and Agreement Technologies can improve the management and control of transport fleets, both for human or goods mobility, in an urban environment. These technologies can offer services to users that are globally optimized and adapted to the changing needs and demands, but also, promoting an efficient use of available resources. In this way it is possible to improve the sustainability of traffic in urban areas, improve energy efficiency and increase the welfare of citizens. To do this it is necessary to provide complex services which offer critical information in order to reason and take decisions. This paper describes the use of complex network analysis as a way to predict the behaviour of the transport network in a city. This service can be used as a way to improve the use of incentives, argumentation or social reputation techniques for the automatic management of urban fleets.

Keywords

  • Public Transport
  • Voronoi Diagram
  • Transport Network
  • Task Allocation
  • Smart City

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-60285-1_37
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References

  1. Aboueljinane, L., et al.: A review on simulation models applied to emergency medical service operations. Comput. Ind. Eng. 66(4), 734–750 (2013)

    CrossRef  Google Scholar 

  2. Billhardt, H., Fernández, A., Lujak, M., Ossowski, S., Julián, V., Paz, J.F., Hernández, J.Z.: Towards smart open dynamic fleets. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS, vol. 9571, pp. 410–424. Springer, Cham (2016). doi:10.1007/978-3-319-33509-4_32

    CrossRef  Google Scholar 

  3. Castelfranchi, C., Falcone, R.: Trust Theory: A Socio-Cognitive and Computational Model. Wiley, New York (2010)

    CrossRef  MATH  Google Scholar 

  4. Kivelä, M., et al.: Multilayer networks. J. Complex. Netw. 2(3), 203–271 (2014)

    CrossRef  Google Scholar 

  5. Koster, A., Sabater-Mir, J., Schorlermmer, M.: Argumentation and trust. In: Ossowski, S. (ed.) Agreement Technologies, pp. 441–451. Springer, Netherlands (2012)

    Google Scholar 

  6. Modgil, S., et al.: The added value of argumentation. In: Ossowski, S. (ed.) Agreement Technologies, pp. 357–403. Springer, Netherlands (2012)

    Google Scholar 

  7. Nair, R., et al.: Large-scale vehicle sharing systems: analysis of Vélib. Int. J. Sustain. Transp. 7, 85–106 (2013)

    CrossRef  Google Scholar 

  8. Haznagy, A., Fi, I., London, A., Németh, T.: Complex network analysis of public transportation networks: a comprehensive study. In: Proceedings of 4th Models and Technologies for Intelligent Transportation Systems (MT-ITS) Conference, pp. 371–378 (2015)

    Google Scholar 

  9. Zhong, C., et al.: Detecting the dynamics of urban structure through spatial network analysis. Int. J. Geogr. Inf. Sci. 28(11), 2178–2199 (2014)

    CrossRef  Google Scholar 

  10. Tsiotas, D., Polyzos, S.: Decomposing multilayer transportation networks using complex network analysis: a case study for the Greek aviation network. J. Complex Netw. 3(4), 642–670 (2015)

    MathSciNet  CrossRef  Google Scholar 

  11. Aleta, A., Meloni, S., Moreno, Y. A multilayer perspective for the analysis of urban transportation systems. arXiv:1607.00072 [physics.soc-ph] (2016)

  12. Gallotti, R., Barthelemy, M.: The multilayer temporal network of public transport in Great Britain. Sci. Data 2, 140056 (2015)

    CrossRef  Google Scholar 

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Acknowledgements

This work was supported by the project TIN2015-65515-C4-1-R of the Spanish government.

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Correspondence to Vicente Julian .

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Rebollo, M., Carrascosa, C., Julian, V. (2017). Transport Network Analysis for Smart Open Fleets. In: , et al. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. PAAMS 2017. Communications in Computer and Information Science, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-60285-1_37

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  • DOI: https://doi.org/10.1007/978-3-319-60285-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60284-4

  • Online ISBN: 978-3-319-60285-1

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