A Comprehensive Study of Intelligent Transportation System Architectures for Road Congestion Avoidance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10542)

Abstract

Road congestion is considered as the bottleneck in Intelligent Transportation System (ITS). It has serious impact on human security, the environment and the economy. Thus, congestion avoidance is one of the main challenges facing ITS. In the aim of reducing the congestion problem, different ITS schemes were proposed. In this paper, we present a comprehensive study of the recent approaches dealing with the congestion issue. This study brings to the scientific community a new classification of the previous schemes based on their specific features. To this end, we introduce some new metrics to evaluate all the studied approaches. We found that the majority of the new congestion management approaches are cooperative and efficient in decreasing the travel delay. However, they are generally focusing on vehicles control and ignoring other elements, such as the use of the road in daily life. The current study presents a new direction of future researches on congestion management systems.

References

  1. 1.
    Pan, J., Popa, I.S., Zeitouni, K., Borcea, C.: Proactive vehicular traffic rerouting for lower travel time. IEEE Trans. Veh. Technol. 62(8), 3551–3568 (2013)CrossRefGoogle Scholar
  2. 2.
    Liu, R., et al.: Balanced traffic routing: design, implementation, and evaluation. Ad Hoc Netw. 37, 14–28 (2016)CrossRefGoogle Scholar
  3. 3.
    Wang, S., Djahel, S., McManis, J.: A multi-agent based vehicles re-routing system for unexpected traffic congestion avoidance. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2541–2548 (2014)Google Scholar
  4. 4.
    Desai, P., Loke, S.W., Desai, A., Singh, J.: CARAVAN: congestion avoidance and route allocation using virtual agent negotiation. IEEE Trans. Intell. Transp. Syst. 14(3), 1197–1207 (2013)CrossRefGoogle Scholar
  5. 5.
    Djahel, S., Jabeur, N., Barrett, R., Murphy, J.: Toward V2I communication technology-based solution for reducing road traffic congestion in smart cities. In: 2015 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6 (2015)Google Scholar
  6. 6.
    Vallati, M., Magazzeni, D., De Schutter, B., Chrpa, L., McCluskey, T.L.: Efficient macroscopic urban traffic models for reducing congestion: a PDDL+ planning approach. In: Proceedings of the Thirtieth Conference on Artificial Intelligence (AAAI-16). AAAI Press (2016)Google Scholar
  7. 7.
    Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V., Iftode, L.: Adaptive traffic lights using car-to-car communication. In: 2007 IEEE 65th Vehicular Technology Conference-VTC2007-Spring, pp. 21–25 (2007)Google Scholar
  8. 8.
    Li, C., Shimamoto, S.: A real time traffic light control scheme for reducing vehicles CO2 emissions. In: 2011 IEEE Consumer Communications and Networking Conference (CCNC), pp. 855–859 (2011)Google Scholar
  9. 9.
    Younis, O., Moayeri, N.: Cyber-physical systems: a framework for dynamic traffic light control at road intersections. In: Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2016)Google Scholar
  10. 10.
    Chen, C., Rickert, M., Knoll, A.: Combining task and motion planning for intersection assistance systemsGoogle Scholar
  11. 11.
    Wei, X., Tan, G., Ding, N.: Batch-light: an adaptive intelligent intersection control policy for autonomous vehicles. In: 2014 International Conference on Progress in Informatics and Computing (PIC), pp. 98–103 (2014)Google Scholar
  12. 12.
    Dresner, K., Stone, P.: A multiagent approach to autonomous intersection management. J. Artif. Intell. Res. 31, 591–656 (2008)Google Scholar
  13. 13.
    Azimi, R., Bhatia, G., Rajkumar, R., Mudalige, P.: Intersection management using vehicular networks. SAE Technical paper (2012)Google Scholar
  14. 14.
    Van Middlesworth, M., Dresner, K., Stone, P.: Replacing the stop sign: unmanaged intersection control for autonomous vehicles. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3. International Foundation for Autonomous Agents and Multiagent Systems (2008)Google Scholar
  15. 15.
    Dresner, K., Stone, P.: Multiagent traffic management: a reservation-based intersection control mechanism. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 530–537 (2004)Google Scholar
  16. 16.
    Dresner, K., Stone, P.: Multiagent traffic management: an improved intersection control mechanism. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 471–477 (2005)Google Scholar
  17. 17.
    Huang, S., Sadek, A.W., Zhao, Y.: Assessing the mobility and environmental benefits of reservation-based intelligent intersections using an integrated simulator. IEEE Trans. Intell. Transp. Syst. 13(3), 1201–1214 (2012)CrossRefGoogle Scholar
  18. 18.
    Zhu, M., Li, X., Huang, H., Kong, L., Li, M., Wu, M.-Y.: LICP: a look-ahead intersection control policy with intelligent vehicles. In: 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, pp. 633–638 (2009)Google Scholar
  19. 19.
    Bento, L.C., Parafita, R., Santos, S., Nunes, U.: Intelligent traffic management at intersections: legacy mode for vehicles not equipped with V2V and V2I communications. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 726–731 (2013)Google Scholar
  20. 20.
    Abdelhameed, M.M., Abdelaziz, M., Hammad, S., Shehata, O.M.: Development and evaluation of a multi-agent autonomous vehicles intersection control system. In: 2014 International Conference on Engineering and Technology (ICET), pp. 1–6 (2014)Google Scholar
  21. 21.
    Chen, G., Kang, K.-D.: Win-fit: efficient intersection management via dynamic vehicle batching and scheduling. In: 2015 International Conference on Connected Vehicles and Expo (ICCVE), pp. 263–270 (2015)Google Scholar
  22. 22.
    Draft sae j2735-200911 dedicated short range (DSRC) message set dictionaryGoogle Scholar
  23. 23.
    Leonard, N.E., Edward, F.: Virtual leaders, artificial potentials and coordinated control of groups. In: Proceedings of the 40th IEEE Conference on Decision and Control, vol. 3. IEEE (2001)Google Scholar
  24. 24.
    Olfati-Saber, R., Murray, R.M.: Distributed cooperative control of multiple vehicle formations using structural potential functions. In: IFAC World Congress, vol. 15, pp. 242–248 (2002)Google Scholar
  25. 25.
    Baras, J.S., Tan, X., Hovareshti, P.: Decentralized control of autonomous vehicles. In: Proceedings of 42nd IEEE Conference on Decision and Control vol. 2. IEEE (2003)Google Scholar
  26. 26.
    Roozbehani, H., Rudaz, S., Gillet, D.: On decentralized navigation schemes for coordination of multi-agent dynamical systems. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 4807–4812 (2009)Google Scholar
  27. 27.
    Makarem, L., Gillet, D.: Decentralized coordination of autonomous vehicles at intersections. In: IFAC Proceedings, vol. 44.1, pp. 13046-13051 (2011)Google Scholar
  28. 28.
    Makarem, L., Gillet, D.: Fluent coordination of autonomous vehicles at intersections. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2557–2562 (2012)Google Scholar
  29. 29.
    Makarem, L., Gillet, D.: Information sharing among autonomous vehicles crossing an intersection. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2563–2567 (2012)Google Scholar
  30. 30.
    Hassan, A., Rakha, H.: A fully-distributed heuristic algorithm for control of autonomous vehicle movements at isolated intersections. Int. J. Transp. Sci. Technol. 3(4), 297–310 (2014)CrossRefGoogle Scholar
  31. 31.
    Lu, G., Li, L., Wang, Y., Zhang, R., Bao, Z., Chen, H.: A rule based control algorithm of connected vehicles in uncontrolled intersection. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 115–120 (2014)Google Scholar
  32. 32.
    Azimi, S.R., Bhatia, G., Rajkumar, R.R., Mudalige, P.: Vehicular networks for collision avoidance at intersections. SAE Int. J. Passeng. Cars-Mech. Syst. 4(2011-01–0573), 406–416 (2011)CrossRefGoogle Scholar
  33. 33.
    Azimi, S., Bhatia, G., Rajkumar, R., Mudalige, P.: Reliable intersection protocols using vehicular networks. In: 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pp. 1–10 (2013)Google Scholar
  34. 34.
    Azimi, R., Bhatia, G., Rajkumar, R.R., Mudalige, P.: STIP: spatio-temporal intersection protocols for autonomous vehicles. In: ICCPS 2014: ACM/IEEE 5th International Conference on Cyber-Physical Systems (with CPS Week 2014), pp. 1–12 (2014)Google Scholar
  35. 35.
    Azimi, R., Bhatia, G., Rajkumar, R., Mudalige, P.: Ballroom intersection protocol: synchronous autonomous driving at intersections, pp. 167–175 (2015)Google Scholar
  36. 36.
    Hummer, J.E.: Intersection and interchange design. In: Handbook of Transportation Engineer, pp. 14.1–14.27 (2004)Google Scholar
  37. 37.
    Yang, X., Li, X., Xue, K.: A new traffic-signal control for modern roundabouts: method and application. IEEE Trans. Intell. Transp. Syst. 5(4), 282 (2004)CrossRefGoogle Scholar
  38. 38.
    Fouladvand, M.E., Sadjadi, Z., Shaebani, M.R.: Characteristics of vehicular traffic flow at a roundabout. Phys. Rev. E 70(4), 46132 (2004)CrossRefMATHGoogle Scholar
  39. 39.
    Rastelli, J.P., Milanés, V., De Pedro, T., Vlacic, L.: Autonomous driving manoeuvres in urban road traffic environment: a study on roundabouts. In: Proceedings of the 18th World Congress The International Federation of Automatic Control (2011)Google Scholar
  40. 40.
    Azimi, R., Bhatia, G., Rajkumar, R., Mudalige, P.: V2V-Intersection management at roundabouts. SAE Int. J. Passeng. Cars-Mech. Syst. 6(2013-01–0722), 681–690 (2013)CrossRefGoogle Scholar
  41. 41.
    Bosankić, I., Mehmedović, L.B.: Cooperative intelligence in roundabout intersections using hierarchical fuzzy behavior calculation of vehicle speed profile (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University Moulay IsmailMeknesMorocco

Personalised recommendations