Day-to-day mobility among the population has increased with economic growth. Smart
cities are renovated with advanced technologies to admire modern life in which intelligent
transportation becomes highly focused. Because the traffic signal control systems
are fixed at a constant time. They split the traffic signal into predetermined intervals
and function inefficiently; they result in long wait times, waste fuel and increased
carbon emissions. This research study introduces a novel technique for traffic light
management to reduce the uncertainties in the system. A dynamic and intelligent traffic
light adaptive optimal management system (DITLAOCS) is implemented in this research.
It does this by modifying the traffic signal duration in run time and using real-time
traffic data as input. Furthermore, the proposed DITLAOCS executes based on three
modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness
mode (FM), all vehicles are prioritized equally, while vehicles in different categories
receive varying priority levels. Emergency vehicles, on the other hand, receive the
highest priority. Furthermore, a fuzzy inference method based on traffic data is shown
to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement
learning to switch traffic lights in three different phases (red, green and yellow).
We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using
Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results
illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms
on several performance measures.
