Skip to main content

AGAMAS: A New Agent-Oriented Traffic Simulation Framework for SUMO

  • Conference paper
  • First Online:
Multi-Agent Systems (EUMAS 2023)

Abstract

Simulating everyday traffic scenarios is not an easy task. Many aspects have to be taken into consideration and properly modelled, from static components, like traffic lights, to dynamic components, like vehicles. Due to their intrinsic autonomy and distribution, such components have already been designed as software agents, and integrated into existing traffic simulators, such as SUMO. The needing for agent-based modelling is even more evident when autonomous vehicles are present in the simulation. In this paper, we present an Agent-Based Traffic Simulation framework, where the simulation components can be defined as JADE agents. We present the engineering of our framework, and we show how it represents a new alternative for creating Agent-Based simulations in the largely used SUMO traffic simulator. We also demonstrate its applicability by employing the framework in one case study involving autonomous vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sumo.dlr.de/docs/Libtraci.html.

  2. 2.

    This feature has been recently added to libtraci thanks to the effort of the SUMO team and the authors of this work.

  3. 3.

    Naturally, this value depends on the simulation parameters (e.g., vehicles’ speed).

References

  1. Barceló, J., Casas, J.: Dynamic network simulation with AIMSUN. In: Kitamura, R., Kuwahara, M. (eds.) Simulation Approaches in Transportation Analysis. Operations Research/Computer Science Interfaces Series, vol. 31, pp. 57–98. Springer, Boston, MA (2005). https://doi.org/10.1007/0-387-24109-4_3

  2. Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. John Wiley & Sons, Hoboken (2007)

    Google Scholar 

  3. Chao, Q., et al.: A survey on visual traffic simulation: models, evaluations, and applications in autonomous driving. Comput. Graph. Forum 39(1), 287–308 (2020). https://doi.org/10.1111/cgf.13803

  4. Chu, V.H., Görmer, J., Müller, J.P.: ATSim: combining AIMSUM and jade for agent-based traffic simulation. In: Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA) (2011)

    Google Scholar 

  5. Čičić, M., Pasquale, C., Siri, S., Sacone, S., Johansson, K.H.: Platoon-actuated variable area mainstream traffic control for bottleneck decongestion. Eur. J. Control 68, 100687 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  6. Čičić, M., Xiong, X., Jin, L., Johansson, K.H.: Coordinating vehicle platoons for highway bottleneck decongestion and throughput improvement. IEEE Trans. Intell. Transp. Syst. 23(7), 8959–8971 (2021)

    Article  Google Scholar 

  7. Dong, J., Chen, S., Ha, P.Y.J., Li, Y., Labi, S.: A DRL-based multiagent cooperative control framework for CAV networks: a graphic convolution Q network. \(\text{arXiv}\): Artificial Intelligence (2020)

    Google Scholar 

  8. Gerostathopoulos, I., Pournaras, E.: Trapped in traffic?: A self-adaptive framework for decentralized traffic optimization. In: Litoiu, M., Clarke, S., Tei, K. (eds.) Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2019, Montreal, QC, Canada, 25–31 May 2019, pp. 32–38. ACM (2019). https://doi.org/10.1109/SEAMS.2019.00014

  9. Hamilton, A., Waterson, B., Cherrett, T., Robinson, A., Snell, I.: The evolution of urban traffic control: changing policy and technology. Transp. Plan. Technol. 36(1), 24–43 (2013)

    Article  Google Scholar 

  10. Johansson, O., Pearce, D., Maddison, D.: Blueprint 5: True Costs of Road Transport. Routledge, Abingdon (2014)

    Google Scholar 

  11. Lopez, P.A., et al.: Microscopic traffic simulation using sumo. In: The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE (2018). www.elib.dlr.de/124092/

  12. Nguyen, J., Powers, S.T., Urquhart, N., Farrenkopf, T., Guckert, M.: An overview of agent-based traffic simulators. CoRR abs/2102.07505 (2021). www.arxiv.org/abs/2102.07505

  13. de Oliveira, L.F.P., Manera, L.T., Luz, P.D.G.D.: Smart traffic light controller system. In: Alsmirat, M.A., Jararweh, Y. (eds.) Sixth International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2019, Granada, Spain, 22–25 October 2019, pp. 155–160. IEEE (2019). https://doi.org/10.1109/IOTSMS48152.2019.8939239

  14. de Oliveira, L.F.P., Manera, L.T., Luz, P.D.G.D.: Development of a smart traffic light control system with real-time monitoring. IEEE Internet Things J. 8(5), 3384–3393 (2021). https://doi.org/10.1109/JIOT.2020.3022392

  15. Pasquale, C., Sacone, S., Siri, S., Ferrara, A.: Traffic control for freeway networks with sustainability-related objectives: review and future challenges. Annu. Rev. Control 48, 312–324 (2019)

    Article  MathSciNet  Google Scholar 

  16. Piacentini, G., Goatin, P., Ferrara, A.: Traffic control via platoons of intelligent vehicles for saving fuel consumption in freeway systems. IEEE Control Syst. Lett. 5(2), 593–598 (2020)

    Article  MathSciNet  Google Scholar 

  17. PTV, A.: VISSIM 5.30-05 user manual. Germany. Karlsruhe: PTV AG (2011)

    Google Scholar 

  18. Rieser, M., Dobler, C., Dubernet, T., Grether, D., Horni, A., Lammel, G., Waraich, R., Zilske, M., Axhausen, K.W., Nagel, K.: Matsim user guide. MATSim, Zurich (2014)

    Google Scholar 

  19. Sarné, G.M.L., Postorino, M.N.: Agents meet traffic simulation, control and management: a review of selected recent contributions. In: Santoro, C., Messina, F., Benedetti, M.D. (eds.) Proceedings of the 17th Workshop From Objects to Agents co-located with 18th European Agent Systems Summer School (EASSS 2016), Catania, Italy, 29–30 July 2016. CEUR Workshop Proceedings, vol. 1664, pp. 112–117. CEUR-WS.org (2016). www.ceur-ws.org/Vol-1664/w19.pdf

  20. da Silva, B.C., Junges, R., de Oliveira, D., Bazzan, A.L.C.: ITSUMO: an intelligent transportation system for urban mobility. Adaptive Agents and Multi-Agent Systems (2004)

    Google Scholar 

  21. Siri, S., Pasquale, C., Sacone, S., Ferrara, A.: Freeway traffic control: a survey. Automatica 130, 109655 (2021)

    Article  MathSciNet  Google Scholar 

  22. Soares, G., Kokkinogenis, Z., Macedo, J.L., Rossetti, R.J.F.: Agent-based traffic simulation using sumo and jade: an integrated platform for artificial transportation systems. In: International Conference on Simulation of Urban Mobility (2013)

    Google Scholar 

  23. Tan, D., Younis, M.F., Lalouani, W., Lee, S.: PALM: platoons based adaptive traffic light control system for mixed vehicular traffic. In: 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI), Atlanta, GA, USA, 18–21 October 2021, pp. 178–185. IEEE (2021). https://doi.org/10.1109/SWC50871.2021.00033

  24. Timóteo, I.J.P.M., Araujo, M.R., Rossetti, R.J.F., Oliveira, E.C.: TraSMAPI: an API oriented towards multi-agent systems real-time interaction with multiple traffic simulators. In: 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Madeira, Portugal, 19–22 September 2010, pp. 1183–1188. IEEE (2010). https://doi.org/10.1109/ITSC.2010.5625238

  25. Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation, pp. 983–1000. Springer-Verlag, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32460-4

  26. Wang, N., Wang, X., Palacharla, P., Ikeuchi, T.: Cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communications. In: IEEE Vehicular Networking Conference (VNC) (2017)

    Google Scholar 

  27. Zhang, K., Batterman, S.: Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450, 307–316 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Angelo Ferrando .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sadeghi Garjan, M., Chaanine, T., Pasquale, C., Paolo Pastore, V., Ferrando, A. (2023). AGAMAS: A New Agent-Oriented Traffic Simulation Framework for SUMO. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43264-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43263-7

  • Online ISBN: 978-3-031-43264-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics