SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic

Emergency Vehicles (EVs) play a significant role in giving timely assistance to the general public by saving lives and avoiding property damages. The EV preemption models help the EVs to maintain their speed along their path by pre-clearing the normal vehicles from the path. However, few preemption models are designed in literature, and they lack in minimizing the negative impacts of EV preemption on normal vehicle traffic and also negative impacts of normal vehicle traffic on EV speed. To accomplish such goals, the work proposes a Segment-based Geographic routing and Traffic light Scheduling based EV preemption (SG-TSE) that incorporates two mechanisms: Segment based Geographic Routing (SGR) and Dynamic Traffic Light Scheduling and EV Preemption (DTSE) for efficient EV preemption. Firstly, the SGR utilized a geographic routing model through the Segment Heads (SHs) along the selected route and passed the EV arrival messages to the traffic light controller to pre-clear the normal traffic. Secondly, the DTSE designs effective scheduling at traffic lights by dynamically adjusting the green time phase based on the minimum detection distance of EVs to the intersections. Thus, the EVs are passed through the intersections quickly without negatively impacting normal traffic, even the signal head in the red phase. Moreover, the proposed SG-TSE activates the green phase time at the correct time and minimizes the negative impacts on the EV preemption model. Finally, the performance of SG-TSE is evaluated using Network Simulator-2 (NS-2) with different performance metrics and various network traffic scenarios.

@article{Kamble2021,
  title     = {SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Shridevi Jeevan Kamble and Manjunath R Kounte},
  doi       = {10.14569/IJACSA.2021.0121236},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121236}
}