Skip to main content

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems


Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Pandit, K., Ghosal, D., Zhang, H. M., & Chen-Nee, C. (2013). Adaptive traffic signal control with vehicular ad-hoc networks. IEEE Transactions on Vehicular Technology, 62(4), 1459–1471.

    Article  Google Scholar 

  2. 2.

    Bani Younes, M., Boukerche, A. (2014). An intelligent traffic light scheduling algorithm through VANETs. In IEEE 39th Conference, in local computer networks workshops (LCN workshops) (pp. 637–642).

  3. 3.

    Bani Younes, M., Boukerche, A. (2015). Intelligent traffic light controlling algorithms using vehicular networks. In IEEE transactions on vehicular technology (vol no. 99, pp. 1–1).

  4. 4.

    Younes, M. B., Boukerche, A., & Roman-Alonso, G. (2014). An intelligent path recommendation protocol (ICOD) for VANETs. Computer Networks, 64, 225–242.

    Article  Google Scholar 

  5. 5.

    U.S. Fire Administration. (2014). Emergency vehicle safety initiative, FA-336/February 2014.

  6. 6.

    U.S. Department of Transportation. (2012). TRAFFIC SAFETY FACTS 2012.

  7. 7.

    Li, L., Wen, D., & Yao, D. (2014). A survey of traffic control with vehicular communications. IEEE Transactions on in Intelligent Transportation Systems, 15(1), 425–432.

    MathSciNet  Article  Google Scholar 

  8. 8.

    Chen, B., & Cheng, H. H. (2010). A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems, 11(2), 485–497.

    Article  Google Scholar 

  9. 9.

    Pau, G., & Scata, G. (2014). Smart traffic light junction management using wireless sensor networks. WSEAS transactions on communication, 14, 2224–2864.

    Google Scholar 

  10. 10.

    Khalil, Y. M., Al-Karaki, M. N., & Shatnawi, A. M. (2010). Intelligent traffic light flow control system using wireless sensors networks. Journal of Information Science and Engineering, 26(3), 753–768.

    Google Scholar 

  11. 11.

    Ghaffarian, H., Fathy, M., & Soryani, M. (2012). Vehicular ad hoc networks enabled traffic controller for removing traffic lights in isolated intersections based on integer linear programming. Intelligent Transport Systems, 6(2), 115–123.

    Article  Google Scholar 

  12. 12.

    Askerzade, I. N., & Mahmood, M. (2010). Control the extension time of traffic light in single junction by using fuzzy logic. International Journal of Electrical and Computer Sciences IJECSIJENS, 10(2), 48–55.

    Google Scholar 

  13. 13.

    Azimirad, E., Pariz, N., & Sistani, M. (2010). A novel fuzzy model and control of single intersection at urban traffic network. IEEE Systems Journal, 4(1), 107–111.

    Article  Google Scholar 

  14. 14.

    Henrique, D., Marranghello, N., & Damiani, F. (2014). Genetic algorithm-based traffic lights timing optimization and routes definition using Petri net model of urban traffic flow. World Congress, 19(1), 11326–11331.

    Google Scholar 

  15. 15.

    Wunderlich, R., Elhanany, I. & Urbanik, T. (2007). A stable longest queue first signal scheduling algorithm for an isolated intersection. In IEEE international conference on vehicular electronics and safety (pp. 1–6).

  16. 16.

    Garca-Nieto, J., Alba, E., & Olivera, A. C. (2012). Swarm intelligence for traffic light scheduling: Application to real urban areas. Engineering Applications of Artificial Intelligence, 25(2), 274–283.

    Article  Google Scholar 

  17. 17.

    Collotta, M., & Pau, G. (2015). New solutions based on wireless networks for dynamic traffic lights management: A comparison between IEEE 802.15. 4 and bluetooth. Transport and Telecommunication Journal, 16(3), 224–236.

    Article  Google Scholar 

  18. 18.

    Tessa, T., Killat, M., Hartenstein, H., Luz, R., Hausberger, S., & Benz, T. (2010). The impact of traffic-light-to-vehicle communication on fuel consumption and emissions. In IEEE In Internet of Things (IOT) (pp. 1–8).

  19. 19.

    Ferreira, M., & d’Orey, P. (2012). On the impact of virtual traffic lights on carbon emissions mitigation. IEEE Transactions on Intelligent Transportation Systems, 13(1), 284–295.

    Article  Google Scholar 

  20. 20.

    Pasin, M., Scheuermann, B., & Moura, R. (2015). VANET-based Intersection control with a throughput/fairness tradeoff. In 2015 8th IFIP wireless and mobile networking conference (WMNC) (pp. 208–215), IEEE.

  21. 21.

    Tung, L., Mena, J., Gerla, M., & Sommer, C. (2013). A cluster based architecture for intersection collision avoidance using heterogeneous networks. In Ad Hoc Networking Workshop (MED-HOC-NET), 2013 12th Annual Mediterranean (pp. 82–88). IEEE.

  22. 22.

    Bellavista, P., Foschini, L., & Zamagni, E. (2014). V2x protocols for low-penetration-rate and cooperative traffic estimations. In Vehicular technology conference (VTC Fall), 2014 IEEE 80th (pp. 1–6), IEEE.

  23. 23.

    Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). SUMO simulation of urban mobility: An overview. In: The third international conference on advances in system simulation (SIMUL) (pp. 63–68).

  24. 24.

    Network Simulator ns-2.

Download references

Author information



Corresponding author

Correspondence to Maram Bani Younes.

Additional information

This work is partially supported by NSERC DIVA Strategic Research Network, CREAT-TRANSIT Network, Canada Research Chairs Program and Philadelphia University, Jordan.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Younes, M.B., Boukerche, A. An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems. Wireless Netw 24, 2451–2463 (2018).

Download citation


  • Road network
  • Vehicular network
  • Emergency vehicle
  • Traffic light phases
  • Traffic light scheduling