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Alternative Paths Reordering Using Probabilistic Time-Dependent Routing

  • Martin GolasowskiEmail author
  • Jakub Beránek
  • Martin Šurkovský
  • Lukáš Rapant
  • Daniela Szturcová
  • Jan Martinovič
  • Kateřina Slaninová
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

In this paper we propose an innovative routing algorithm which takes into account stochastic properties of the road segments using the Probabilistic Time-Dependent Routing (PTDR). It can provide optimal routes for vehicles driving in a smart city based on a global view of the road network. We have implemented the algorithm in a distributed on-line service which can leverage heterogeneous resources such as Cloud or High Performance Computing (HPC) in order to serve a large number of clients simultaneously and efficiently. A preliminary experimental results using a custom traffic simulator are presented.

Notes

Acknowledgements

This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPS II) project ‘IT4Innovations excellence in science - LQ1602’, by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project ‘IT4Innovations National Supercomputing Center – LM2015070’, and partially by the SGC grant No. SP2019/108 ‘Extension of HPC platforms for executing scientific pipelines’, VŠB - Technical University of Ostrava, Czech Republic.

References

  1. 1.
    Bader, R., Dees, J., Geisberger, R., Sanders, P.: Alternative route graphs in road networks. In: International Conference on Theory and Practice of Algorithms in (Computer) Systems, pp. 21–32. Springer (2011)Google Scholar
  2. 2.
    Blumer, S., Eichelberger, M., Wattenhofer, R.: Efficient traffic routing with progress guarantees. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 953–957 (2018).  https://doi.org/10.1109/ICTAI.2018.00147
  3. 3.
    Golasowski, M., Tomis, R., Martinovič, J., Slaninová, K., Rapant, L.: Performance evaluation of probabilistic time-dependent travel time computation. In: IFIP International Conference on Computer Information Systems and Industrial Management, pp. 377–388. Springer, Cham (2016)CrossRefGoogle Scholar
  4. 4.
    Martinovič, J., Snášel, V., Dvorskỳ, J., Dráždilová, P.: Search in documents based on topical development. In: Advances in Intelligent Web Mastering-2, pp. 155–166. Springer (2010)Google Scholar
  5. 5.
    Martinovič, J., Golasowski, M., Slaninová, K., Beránek, J., Šurkovský, M., Rapant, L., Szturcová, D., Cmar, R.: A distributed environment for traffic navigation systems. In: The 13th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2019 (2019, accepted)Google Scholar
  6. 6.
    Miller-Hooks, E., Mahmassani, H.: Path comparisons for a priori and time-adaptive decisions in stochastic, time-varying networks. Eur. J. Oper. Res. 146(1), 67–82 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Mouhcine, E., Mansouri, K., Mohamed, Y.: Intelligent Vehicle Routing System Using VANET Strategy Combined with a Distributed Ant Colony Optimization: Methods and Protocols, pp. 230–237 (2019)CrossRefGoogle Scholar
  8. 8.
    Nie, Y.M., Wu, X.: Shortest path problem considering on-time arrival probability. Transp. Res. Part B Methodol. 43(6), 597–613 (2009)CrossRefGoogle Scholar
  9. 9.
    Nikolova, E., Kelner, J., Brand, M., Mitzenmacher, M.: Stochastic shortest paths via quasi-convex maximization. Algorithms-ESA 2006, 552–563 (2006)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Ptošek, V., Ševčík, J., Martinovič, J., Slaninová, K., Rapant, L., Cmar, R.: Real time traffic simulator for self-adaptive navigation system validation (2018)Google Scholar
  11. 11.
    Rapant, L., Golasowski, M., Martinovič, J., Slaninová, K.: Simulated probabilistic speed profiles for selected routes in Prague (2018).  https://doi.org/10.5281/zenodo.2275647
  12. 12.
    Silvano, C., Agosta, G., Bartolini, A., Beccari, A.R., Benini, L., Besnard, L., Bispo, J., Cmar, R., Cardoso, J.M., Cavazzoni, C., et al.: Antarex: a dsl-based approach to adaptively optimizing and enforcing extra-functional properties in high performance computing. In: 2018 21st Euromicro Conference on Digital System Design (DSD), pp. 600–607. IEEE (2018)Google Scholar
  13. 13.
    Tomis, R., Rapant, L., Martinovič, J., Slaninová, K., Vondrák, I.: Probabilistic time-dependent travel time computation using Monte Carlo simulation. In: Kozubek, T., Blaheta, R., Šístek, J., Rozložník, M., Čermák, M. (eds.) High Performance Computing in Science and Engineering, pp. 161–170. Springer, Cham (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Golasowski
    • 1
    Email author
  • Jakub Beránek
    • 1
  • Martin Šurkovský
    • 1
  • Lukáš Rapant
    • 1
  • Daniela Szturcová
    • 1
  • Jan Martinovič
    • 1
  • Kateřina Slaninová
    • 1
  1. 1.IT4InnovationsVSB - Technical University of OstravaOstrava-PorubaCzech Republic

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