AN AGENT BASED MODEL TO IMPROVE TRAFFIC FLOW AND ASSESS THE IMPACT OF SMART TRAFFIC LIGHT

The Traffic in developing country undergoes severs perturbations due to either a misunderstanding of the physical phenomenon or in bad calibration of equipments such as traffic light. Due to this high complexity, mathematical models fail to well represent different scenario. Those issues are mostly due to the high amount of parameter which should be taken into consideration when dealing with some mathematical model and the drivers behaviors. Since agent-based model is a computational simulation that represents individual entities (called agents) and their interactions within a defined environment. It is a powerful tools used to investigate human behavior impact in a mathematical model. Therefore, this paper proposed a novel agent-based approach framework for effectively making traffic light timing dynamic according to traffic density. Moreover, based on different rules such as lane changing, stop and goat the traffic light, we develop an Agent based model to represent the relationships between the traffic changing and its environment. The use of agent-based models for traffic light time optimization offers a more granular, adaptive, and integrative approach that better capture the complexities of real-world traffic scenarios. Finally, the experimental result is applied on synthetic data and compared to other results in almost the same context for both static and dynamic traffic light. The results show that the travel time distribution is less for dynamic traffic light.