ACO-DSOS Hybrid Approach to Enhance Traffic Signal Optimization

In the past few years, the global attention has been increasingly drawn towards various traffic issues due to the growing number of vehicles. Among these challenges, traffic congestion stands out as a significant concern. One important problem is traffic congestion.

However, traffic congestion can cause not only financial damages but also material and human losses through accidents. To overcome this problem, many technological and political efforts have been invested at different levels. A first solution consists in extending road transport infrastructures in the densest traffic areas. The disadvantage of this solution is its expensive nature [1].

The second proposed solution for a traffic signal control system involves managing traffic at intersections safely and efficiently using a sequence of alternating green, yellow and red lights. The issue of traffic signal operation can be addressed at three distinct levels: local, arterial, and network.

Local control implies that a signal control system takes into account only the traffic conditions specific to an isolated intersection. In contrast, the foundation of arterial control lies in the coordination of multiple signalized intersections.

Signal coordination aims for a "green wave," syncing traffic lights for smooth vehicle flow through intersections. Network signal control addresses predetermined routes, but adjusting signals may alter routing as drivers seek the fastest path.

Integrating vehicle routing and signal control is crucial to understanding traffic flow equilibrium in the network [2].

Meanwhile, research on traffic signals prompted the development by driving assistance systems, aimed at minimizing vehicle waiting time. Nevertheless, the majority of traffic systems continue to rely on fixed-time settings for lengthy cycles [3]. Such systems lack the ability to adaptively adjust traffic light timings when faced with unforeseen circumstances like accidents, natural disasters, or sudden incidents. However, those methods are not appropriate for centralized implementations in extensive traffic networks, resulting in bottlenecks at intersections [4].

The principal idea of this article is to implement an ACO-DSOS based traffic signal control system, this hybrid metaheuristics approach aims to find optimum values of traffic signal capable of providing the lowest average number of vehicles in several intersections.

The subsequent sections of the paper are organized as follows: Section 2 presents the related work of traffic signal control optimization based on metaheuristics. In Section 3 introduces problem definition and formalism. In Section 4 presents the suggested approach. The obtained results are carried out in Section 5. The last section concludes and gives some perspectives.

Traffic control system refers to a sophisticated network of technologies and algorithms designed to optimize the flow of traffic and improve overall transportation efficiency [5]. When considering traffic light problem as an optimization task, meta-heuristic methods can be utilized [6]. Consider the example from the study [7], The authors presented a framework for enhancing traffic signal optimization through the integration of Genetic Algorithm (GA) and Cellular Automata Simulation. They integrated this algorithm with Cellular Automata Simulation, including traffic flow. However, the methods fail to deliver satisfactory control results under conditions of high traffic demand.

The effectiveness of traffic signal control heavily depends on the design technique of signal plans, and one such approach is the ACO widely applied to addressing combinatorial problems [8, 9]. This algorithm functions even as collaboration system, where each individual ant simulates intelligent behavior. The Ant Colony Algorithm excels in finding best solutions to a variety problem. The ants exhibit capability to discover the shortest paths by depositing a chemical substance, pheromone, on their trails towards food sources. This pheromone serves as a communication link among ants, and paths with higher pheromone concentrations become more attractive and are predominantly used by the majority of ants [10].

In the study [11], The authors suggested employing the ant colony method in conjunction with the concept of colored connected vehicles to address the dynamic traffic routing problem, which encompasses multi-source and multi-destination traffic flows. Their approach aims to optimize the routing of traffic in real-time.

Meanwhile, a newly developed optimization algorithm called the DSOS algorithm has demonstrated remarkable effectiveness and robustness in solving numerical optimization and engineering design problems [12]. Additionally, the same study [12] introduces the DSOS algorithm to address the capacitated vehicle routing problem, aiming to determine optimal routes for a fleet of vehicles to serve a specified set of demand points while minimizing total routing costs.

All of the works in this section and in the body of literature for this area don’t use intricate road networks and several intersections. To overcome the challenges work in current section there is a great need for developing novel approaches.

The objective of this study was to explore traffic lights control improved by ACO-DSOS algorithm to reduce traffic flow of road; an enhanced approach tailored for the optimization of traffic signal timings. The central goal of this algorithm is to elevate overall system efficiency by strategically minimizing the number of vehicles in waiting, thus fostering a marked improvement in traffic flow at numerous intersections. Our comprehensive simulations demonstrate that the ACO-DSOS algorithm not only effectively achieves its primary objective but also yields superior performance compared to static approach. The outcomes of the simulation underscore the algorithm's potential to significantly enhance the operational efficiency of traffic networks, promising tangible benefits for urban mobility and transportation systems.

In real traffic networks, the system utilizes strategically placed detectors that continuously capture diverse real-time data, including the number of delayed vehicles, arrival vehicles, number vehicles released, red light, green light. By processing this live information, the system dynamically adapts, optimizing the number of waiting vehicles and enhancing accuracy and responsiveness for effective traffic flow solutions.

As further research, we will implement multi-objective optimization solution, considering factors such as delayed vehicle time, emergency vehicles, and pedestrian cases. We aim to integrate this solution with diverse traffic data sources and test it on larger, complex networks for scalability and effectiveness in varied urban contexts.