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Recently, with increasing urban traffic congestion, there has been an upsurge in studies on reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic management. However, most existing RL-TSC research relies on simulation, and instances of real-world application are relatively scarce. Furthermore, existing RL-TSC methods employ a step-based approach, controlling traffic signals every short step. This approach can be inefficient in oversaturated traffic conditions, where large deviations in traffic demand between movements prevent traffic signals from being effectively managed based on the overall traffic situation. In this study, we aim to transform simulation-based RL-TSC into a more practical and applicable model. We have developed a RL-TSC method capable of responding to oversaturated traffic conditions, designing an action space that enables the agent to derive an optimal signal set for every cycle length, thereby understanding the traffic situation across all movements. During each cycle length, our proposed model conducts traffic signal optimization and identifies the optimal signal through an iterative exploration. To facilitate a fast and accurate strategy search process, we developed a kinematic wave-based mesoscopic model. This model estimates the density across the entire link, based on collected traffic data and derives the state and reward values. We have validated the field applicability of the proposed RL-TSC method through a real-world demonstration at a congested intersection in Seoul, Korea. The results indicated a significant improvement in traffic congestion, with the average queue length at the intersection reduced by up to 11.4%. [ABSTRACT FROM AUTHOR]

The majority of fuel consumed in traffic on urban arterials is related to driving in congested traffic, characterized by frequent speed fluctuations and stops at signalized intersections. A range of traffic control strategies was suggested in the past to decrease fuel consumption and emissions in urban networks. The research presented in this study aims to fill the gap in the existing knowledge by proposing a novel combination of an ecology-based performance index and an evolutionary method for optimization of traffic signal settings. An optimization problem is defined to find the best values for traffic light control parameters on the street network. The ecology-based performance index is used as the criteria for optimization. The defined problem is solved by the Bee Colony Optimization (BCO) technique. The geometry of the subject network has been inserted into a Vissim model calibrated by using relevant traffic demand and field-measured travel times. The Vissim has been used as the tool for the evaluation of the proposed BCO, TRANSYT-7F (T7F) and field signal timing plans. The methodology is tested on the field-real-like network of the City of Kragujevac, Serbia. Results show that in the case of the overall network ecology performance index, BCO makes a decrease of 11.95 % and 8.47% compared to the signal timings from field and T7F, respectively. • Proposed an ecology-based fixed-time control logic for street networks. • The model uses a BCO algorithm and derives control parameters. • A VISSIM simulation estimates the quality of the proposed control algorithm. • TRANSYT-7F and field signal plans are used for comparison with the BCO algorithm. • The model is tested on the field-real-like network of the City of Kragujevac. [ABSTRACT FROM AUTHOR]

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI -based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL -based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO 2 (AEC). In addition, the cooperation between the different trained DRL -based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested. [ABSTRACT FROM AUTHOR]