Skip to main content
View expanded cover

International Conference on Internet of Vehicles

IOV 2018: Internet of Vehicles. Technologies and Services Towards Smart City pp 165–178Cite as

AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11253)

Abstract

Urban transportation of next decade is expected to be disrupted by Autonomous Mobility on Demand (AMoD): AMoD providers will collect ride requests from users and will dispatch a fleet of autonomous vehicles to satisfy requests in the most efficient way. Differently from current ride sharing systems, in which driver behavior has a clear impact on the system, AMoD systems will be exclusively determined by the dispatching logic. As a consequence, a recent interest in the Operations Research and Computer Science communities has focused on this control logic. The new propositions and methodologies are generally evaluated via simulation. Unfortunately, there is no simulation platform that has emerged as reference, with the consequence that each author uses her own custom-made simulator, applicable only in her specific study, with no aim of generalization and without public release. This slows down the progress in the area as researchers cannot build on each other’s work and cannot share, reproduce and verify the results. The goal of this paper is to present AMoDSim, an open-source simulation platform aimed to fill this gap and accelerate research in future ride sharing systems.

Keywords

  • Smart mobility
  • Smart city
  • Shared mobility
  • Autonomous vehicles
  • Simulation

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-05081-8_12
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-05081-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    https://github.com/admaria/AMoDSim.

  2. 2.

    A particular version of SUMO, called SUMO MESO [2], is intended to reduce the details in vehicle movement simulation. However, we are not aware of any published study on AMoD systems based on SUMO MESO.

References

  1. BlaBla Car. https://www.blablacar.com/

  2. MESO: Mesoscopic version of SUMO. http://sumo.dlr.de/wiki/MESO

  3. OMNeT++. https://www.omnetpp.org/

  4. Toth, P., Vigo, D.: Vehicle Routing. SIAM-MOS, 2nd edn. (2014)

    Google Scholar 

  5. Agatz, N.A.H., Erera, A.L., et al.: Dynamic ride-sharing: a simulation study in metro Atlanta. Transp. Res. B-Method 45(9), 1450–1464 (2011)

    CrossRef  Google Scholar 

  6. Alazzawi, S., Hummel, M., Kordt, P., Sickenberger, T., Wieseotte, C., Wohak, O.: Simulating the impact of shared, autonomous vehicles on urban mobility - a case study of Milan. In: SUMO User Conference (2018)

    Google Scholar 

  7. Alonso-Mora, J., Samaranayake, S., et al.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. PNAS 114(3), 462–467 (2017)

    CrossRef  Google Scholar 

  8. Basu, R., et al.: Automated mobility-on-demand vs. mass transit: a multi-modal activity-driven agent-based simulation approach. Transp. Res. Rec. https://doi.org/10.1177/0361198118758630

  9. Bischoff, J., Maciejewski, M.: Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. In: ANT. Elsevier Masson SAS (2016)

    Google Scholar 

  10. Boesch, P.M., Ciari, F., et al.: Autonomous vehicle fleet sizes required to serve different levels of demand. Transp. Res. Rec. 2542, 111–119 (2016)

    CrossRef  Google Scholar 

  11. Burgstaller, S., Flowers, D., et al.: Rethinking mobility: The ‘pay as you go’ car: ride hailing just the start. Technical report (2017)

    Google Scholar 

  12. Clewlow, R.R., Mishra, G.S.: Disruptive transportation: the adoption, utilization, and impacts of ride-hailing in the United States. Technical report, UC Davis (2017)

    Google Scholar 

  13. Elpern-Waxman, J.: Transportation Terms: Dwell Time (2017)

    Google Scholar 

  14. Hyland, M., Mahmassani, H.: Dynamic autonomous vehicle fleet operations: optimization-based strategies to assign AVs to immediate traveler demand requests. Transp. Res. C-Emer. 92, 278–297 (2018)

    CrossRef  Google Scholar 

  15. Jaeyoung, J., Jayakrishnan, R., et al.: Design and modeling of real-time shared-taxi dispatch algorithms. In: TRB 92nd Annual Meeting (2013)

    Google Scholar 

  16. Jung, J., Jayakrishnan, R., et al.: Design and modeling of real-time shared-taxi dispatch algorithms. In: TRB Annual Meeting, vol. 8 (2013)

    Google Scholar 

  17. Lam, A.Y.S., Leung, Y., et al.: Autonomous-vehicle public transportation system: scheduling and admission control. IEEE Trans. Intell. Transp. Syst. 17(5), 1210–1226 (2016)

    CrossRef  Google Scholar 

  18. Linares, M.P., Montero, L., et al.: A simulation framework for real-time assessment of dynamic ride sharing demand responsive transportation models. In: WSC (2016)

    Google Scholar 

  19. Ma, S., Zheng, Y., et al.: T-share : a large-scale dynamic taxi ridesharing. In: ICDE (2013)

    Google Scholar 

  20. Martinez, L.M., Correia, G.H.A., et al.: An agent-based simulation model to assess the impacts of introducing a shared-taxi system: an application to Lisbon. JAT 49, 475–495 (2015)

    Google Scholar 

  21. Robinson, S.: Measuring bus stop dwell time and time lost serving stop with London ibus automatic vehicle location data. Transp. Res. Rec. 2352(1), 68–75 (2013)

    CrossRef  Google Scholar 

  22. Santi, P., Resta, G., et al.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014)

    CrossRef  Google Scholar 

  23. Skulimowski, A.M.J., Ćwik, A.: Communication quality in anticipatory vehicle swarms: a simulation-based model. In: Peng, S.-L., Lee, G.-L., Klette, R., Hsu, C.-H. (eds.) IOV 2017. LNCS, vol. 10689, pp. 119–134. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72329-7_11

    CrossRef  Google Scholar 

  24. Vazifeh, M.M., Santi, P., et al.: Addressing the minimum fleet problem in on-demand urban mobility. Nature 557, 534 (2018)

    CrossRef  Google Scholar 

Download references

Acknowledgement

This work is supported by the CLARA - CLoud plAtform and smart underground imaging for natural Risk Assessment - project, funded by the Italian Ministry of Education, Universities and Research, within the Smart Cities and Communities and Social Innovation initiative.

Author information

Authors and Affiliations

  1. Aucta Cognitio R&D Labs, 95123, Catania, Italy

    Andrea Di Maria & Giovanni Morana

  2. Réseaux et Services de Télécommunications, Télécom SudParis, Evry, 91011, France

    Andrea Araldo

  3. Universitá di Catania, 95125, Catania, Italy

    Antonella Di Stefano

Authors
  1. Andrea Di Maria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Araldo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Morana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonella Di Stefano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Araldo .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Prof. Andrzej M.J. Skulimowski

  2. University of Sussex, Brighton, UK

    Dr. Zhengguo Sheng

  3. Université de Versailles St Quentin, Versailles, France

    Sondès Khemiri-Kallel

  4. Université Paris 13, Villetaneuse, France

    Christophe Cérin

  5. National Chung Cheng University, Minxiong, Taiwan

    Ching-Hsien Hsu

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Di Maria, A., Araldo, A., Morana, G., Di Stefano, A. (2018). AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand. In: Skulimowski, A., Sheng, Z., Khemiri-Kallel, S., Cérin, C., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services Towards Smart City. IOV 2018. Lecture Notes in Computer Science(), vol 11253. Springer, Cham. https://doi.org/10.1007/978-3-030-05081-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05081-8_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05080-1

  • Online ISBN: 978-3-030-05081-8

  • eBook Packages: Computer ScienceComputer Science (R0)