Wireless Networks

, Volume 24, Issue 7, pp 2451–2463 | Cite as

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

  • Maram Bani Younes
  • Azzedine Boukerche


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.


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


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Philadelphia UniversityAmmanJordan
  2. 2.PARADISE Research Laboratory, DIVA Strategic Research CenterUniversity of OttawaOttawaCanada

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