Skip to main content
SpringerLink
Log in

A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways

  • Conference paper
  • First Online:
Ad Hoc Networks (ADHOCNETS 2020)

Included in the following conference series:

Abstract

Vehicular Ad Hoc Networks (VANETs) represent a significant leap forward in the deployment of intelligent transport systems. These networks enable vehicles to instantly exchange traffic information with the aim of smoothing traffic flows and intensifying drivers comfort. In this context, this study addresses the issue of traffic congestion description and detection in multi-lane highways. By making use of collected information, a Markov chain based mobility model is proposed to predict the future road traffic states. Based on the obtained stationary distribution probabilities, performance criteria in steady-state are inferred and computed for different road configurations. The numerical results validate the model demonstrated in the paper.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Kafi, M.A., Ben-Othman, J., Mokdad, L., Badache, N.: Performance analysis and evaluation of REFIACC using queuing networks. Simul. Model. Pract. Theory 71, 15–26 (2017)

    Article  Google Scholar 

  2. Mokdad, L., Ben-Othman, J., Nguyen, A.T.: DJAVAN: detecting jamming attacks in vehicle ad hoc networks. Perform. Eval. 87, 47–59 (2015)

    Article  Google Scholar 

  3. Mokdad, L., Ben-Othman, J., Yahya, B., Niagne, S.: Performance evaluation tools for QoS MAC protocol for wireless sensor networks. Ad Hoc Netw. 12, 86–99 (2014)

    Article  Google Scholar 

  4. Davies, V.A.: Evaluating mobility models within an ad hoc network. Master’s thesis, advisor: Tracy Camp. Department of Mathematical and Computer Sciences, Colorado School of Mines (2000)

    Google Scholar 

  5. Vetriselvi, V., Parthasarathi, R.: Trace based mobility model for ad hoc networks. In: Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007), p. 81. IEEE, October 2007

    Google Scholar 

  6. Förster, A., Bin Muslim, A., Udugama, A.: TRAILS-A trace-based probabilistic mobility model. In: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 295–302, October 2018

    Google Scholar 

  7. Beiró, M.G., Panisson, A., Tizzoni, M., Cattuto, C.: Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5(1), 1–15 (2016). https://doi.org/10.1140/epjds/s13688-016-0092-2

    Article  Google Scholar 

  8. Haklay, M., Weber, P.: OpenStreetMap: “user-generated street maps.”. IEEE Pervasive Comput. 7(4), 12–18 (2008)

    Article  Google Scholar 

  9. Kotz, D., Henderson, T.: CRAWDAD: a community resource for archiving wireless data at Dartmouth. IEEE Pervasive Comput. 4(4), 12–14 (2005)

    Article  Google Scholar 

  10. Bai, F., Sadagopan, N., Helmy, A.: The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks. Ad Hoc Netw. 1(4), 383–403 (2003)

    Article  Google Scholar 

  11. Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I 2(12), 2221–2229 (1992)

    Google Scholar 

  12. Zhang, G., Xu, W.: Cellular automaton traffic model considering driver’s reaction to velocity and headway distance with variable possibility of randomization. In: IOP Conference Series: Materials Science and Engineering, vol. 392, no. 6, p. 062024. IOP Publishing, July 2018

    Google Scholar 

  13. Lighthill, M.J., Whitham, G.B.: On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. Lond. Ser. A Math. Phys. Sci. 229(1178), 317–345 (1955)

    MathSciNet  MATH  Google Scholar 

  14. Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)

    Article  MathSciNet  Google Scholar 

  15. Liu, H.X., Wu, X., Ma, W., Hu, H.: Real-time queue length estimation for congested signalized intersections. Transp. Res. Part C Emerg. Technol. 17(4), 412–427 (2009)

    Article  Google Scholar 

  16. Yang, H., Rakha, H., Ala, M.V.: Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans. Intell. Transp. Syst. 18(6), 1575–1585 (2016)

    Google Scholar 

  17. Briesemeister, L.: Group membership and communication in highly mobile ad hoc networks (2001)

    Google Scholar 

  18. Krauß, S.: Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Doctoral dissertation (1998)

    Google Scholar 

  19. Pop, M.D., Proştean, O., Proştean, G.: Multiple lane road car-following model using Bayesian reasoning for lane change behavior estimation: a smart approach for smart mobility. In: Proceedings of the 3rd International Conference on Future Networks and Distributed Systems, pp. 1–8, July 2019

    Google Scholar 

  20. Legendre, F., Borrel, V., de Amorim, M.D., Fdida, S.: Modeling mobility with behavioral rules: the case of incident and emergency situations. In: Cho, K., Jacquet, P. (eds.) AINTEC 2006. LNCS, vol. 4311, pp. 186–205. Springer, Heidelberg (2006). https://doi.org/10.1007/11930181_14

    Chapter  Google Scholar 

  21. Gipps, P.G.: Behavioral car-following model for computer simulation. Transport. Res. 15(2), 105–111 (1981)

    Article  Google Scholar 

  22. Kharrazi, S., Almén, M., Frisk, E., Nielsen, L.: Extending behavioral models to generate mission-based driving cycles for data-driven vehicle development. IEEE Trans. Veh. Technol. 68(2), 1222–1230 (2018)

    Article  Google Scholar 

  23. Gawron, C.: An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int. J. Mod. Phys. C 9(03), 393–407 (1998)

    Article  Google Scholar 

  24. Mirchandani, P.B., Zou, N.: Queuing models for analysis of traffic adaptive signal control. IEEE Trans. Intell. Transp. Syst. 8(1), 50–59 (2007)

    Article  Google Scholar 

  25. Cremer, M., Landenfeld, M.: A mesoscopic model for saturated urban road networks. In: Traffic and Granular Flow, vol. 97, pp. 169–180 (1998)

    Google Scholar 

  26. Mohimani, G.H., Ashtiani, F., Javanmard, A., Hamdi, M.: Mobility modeling, spatial traffic distribution, and probability of connectivity for sparse and dense vehicular ad hoc networks. IEEE Trans. Veh. Technol. 58(4), 1998–2007 (2008)

    Article  Google Scholar 

  27. Grassmann, W.K., Taksar, M.I., Heyman, D.P.: Regenerative analysis and steady state distributions for Markov chains. Oper. Res. 33(5), 1107–1116 (1985)

    Article  MathSciNet  Google Scholar 

  28. Krajewski, R., Bock, J., Kloeker, L., Eckstein, L.: The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, pp. 2118–2125 (2018). https://doi.org/10.1109/ITSC.2018.8569552

Download references

Author information

Authors and Affiliations

  1. Université Paris Saclay, CNRS, CentraleSupélec, Laboratoire des signaux et systèmes, 91190, Gif-sur-Yvette, France

    El Joubari Oumaima, Ben Othman Jalel & Vèque Véronique

  2. Université Sorbonne Paris Nord, Villetaneuse, France

    Ben Othman Jalel

Authors
  1. El Joubari Oumaima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Othman Jalel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vèque Véronique
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to El Joubari Oumaima .

Editor information

Editors and Affiliations

  1. DISI, University of Bologna, Bologna, Bologna, Italy

    Luca Foschini

  2. Higher School of Technology, Hassan II University of Casablanca, Casablanca, Morocco

    Mohamed El Kamili

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oumaima, E.J., Jalel, B.O., Véronique, V. (2021). A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67369-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67368-0

  • Online ISBN: 978-3-030-67369-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation