IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p929-d103630.html
   My bibliography  Save this article

Stochastic Navigation in Smart Cities

Author

Listed:
  • Rubén Martín García

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

  • Francisco Prieto-Castrillo

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
    Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA
    Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA)

  • Gabriel Villarrubia González

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

  • Javier Prieto Tejedor

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
    StageMotion, R&D Department, C/Orfebres 10, 34005 Palencia, Spain)

  • Juan Manuel Corchado

    (BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain)

Abstract

In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies.

Suggested Citation

  • Rubén Martín García & Francisco Prieto-Castrillo & Gabriel Villarrubia González & Javier Prieto Tejedor & Juan Manuel Corchado, 2017. "Stochastic Navigation in Smart Cities," Energies, MDPI, vol. 10(7), pages 1-11, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:929-:d:103630
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/929/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/929/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    3. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    4. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. S. Scellato & L. Fortuna & M. Frasca & J. Gómez-Gardeñes & V. Latora, 2010. "Traffic optimization in transport networks based on local routing," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(2), pages 303-308, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed WA Hammad & Ali Akbarnezhad & Assed Haddad & Elaine Garrido Vazquez, 2019. "Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities," Energies, MDPI, vol. 12(7), pages 1-23, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nan Ding & Manman Li & Jianming Hao, 2023. "A Two-Phase Approach to Routing a Mixed Fleet with Intermediate Depots," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    2. Yusuf Yilmaz & Can B. Kalayci, 2022. "Variable Neighborhood Search Algorithms to Solve the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery," Mathematics, MDPI, vol. 10(17), pages 1-22, August.
    3. Grigorios D. Konstantakopoulos & Sotiris P. Gayialis & Evripidis P. Kechagias, 2022. "Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification," Operational Research, Springer, vol. 22(3), pages 2033-2062, July.
    4. Herbert Kopfer & Benedikt Vornhusen, 2019. "Energy vehicle routing problem for differently sized and powered vehicles," Journal of Business Economics, Springer, vol. 89(7), pages 793-821, September.
    5. Gitae Kim, 2024. "Electric Vehicle Routing Problem with States of Charging Stations," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    6. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    7. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    8. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    9. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    10. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    11. Azra Ghobadi & Mohammad Fallah & Reza Tavakkoli-Moghaddam & Hamed Kazemipoor, 2022. "A Fuzzy Two-Echelon Model to Optimize Energy Consumption in an Urban Logistics Network with Electric Vehicles," Sustainability, MDPI, vol. 14(21), pages 1-31, October.
    12. Koyuncu, Işıl & Yavuz, Mesut, 2019. "Duplicating nodes or arcs in green vehicle routing: A computational comparison of two formulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 605-623.
    13. LIAN, Ying & LUCAS, Flavien & SÖRENSEN, Kenneth, 2022. "The electric on-demand bus routing problem with partial charging and nonlinear functions," Working Papers 2022005, University of Antwerp, Faculty of Business and Economics.
    14. Joonyup Eun & Byung Duk Song & Sangbok Lee & Dae-Eun Lim, 2019. "Mathematical Investigation on the Sustainability of UAV Logistics," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    15. Sistig, Hubert Maximilian & Sauer, Dirk Uwe, 2023. "Metaheuristic for the integrated electric vehicle and crew scheduling problem," Applied Energy, Elsevier, vol. 339(C).
    16. Wu, Guoyuan & Peng, Dongbo & Boriboonsomsin, Kanok, 2024. "Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification," Institute of Transportation Studies, Working Paper Series qt2qz0n2gv, Institute of Transportation Studies, UC Davis.
    17. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    18. Masmoudi, Mohamed Amine & Hosny, Manar & Demir, Emrah & Genikomsakis, Konstantinos N. & Cheikhrouhou, Naoufel, 2018. "The dial-a-ride problem with electric vehicles and battery swapping stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 392-420.
    19. Alexandre M. Florio & Nabil Absi & Dominique Feillet, 2021. "Routing Electric Vehicles on Congested Street Networks," Transportation Science, INFORMS, vol. 55(1), pages 238-256, 1-2.
    20. Muhammad Usama & Yongjun Shen & Onaira Zahoor, 2019. "Towards an Energy Efficient Solution for Bike-Sharing Rebalancing Problems: A Battery Electric Vehicle Scenario," Energies, MDPI, vol. 12(13), pages 1-21, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:929-:d:103630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.