Improved Twin Actor Twin Delayed Deep Deterministic Policy Gradient With Spatial Occupancy for Traffic Signal Control System

ABSTRACT The increasing number of automobiles on the road has worsened traffic, raised questions about safety, and had an adverse effect on the environment. Traffic Signal Control (TSC) has become a key strategy to address these issues. While conventional TSC systems help manage urban traffic, their reliance on fixed schedules and simplified models limits their effectiveness in complex, real‐world conditions. In order to overcome the above shortcomings, this paper introduces an Improved Twin‐Actor Twin‐Delayed Deep Deterministic Policy Gradient (ITATD3) algorithm designed for traffic signal control at single intersections. The main innovation of the ITATD3 proposed is in two aspects: (1) the incorporation of a spatial occupancy‐based reward which in combination with waiting time, better measures road‐space utilization and congestion than traditional metrics, and (2) the application of the Cheetah Optimization Algorithm (COA) for fine‐tuning of key hyper parameters of the ITATD3 model to improve convergence speed and stability. The performance of the proposed model is compared against four benchmark models, like TATD3, TD3, D3QN, and DQN. Results show that ITATD3 performs consistently better than other models on various performance metrics, such as travel delay, average waiting time, traffic conflicts, spatial occupancy, throughput, and average travel time. The proposed ITATD3 reduced travel delay by a maximum of 68% and improved traffic throughput significantly compared to conventional models. These results reveal the benefits of ITATD3 as a viable solution for real‐time data‐driven traffic signal control in intelligent transportation systems.