Abstract <p>Efficient traffic signal control plays a critical role in reducing urban congestion and improving transportation efficiency. This paper presents an adaptive traffic signal control approach based on reinforcement learning that employs the Soft Actor-Critic (SAC) algorithm to optimize traffic signal phase durations. By focusing on phase duration optimization rather than discrete phase selection, the proposed method ensures a more predictable and adaptive traffic management system. Unlike traditional methods that select discrete signal phases, our approach continuously adjusts phase duration based on real-time traffic conditions, providing a more flexible and responsive control strategy. The proposed framework uses connected vehicle data, including position, speed, and acceleration, to predict vehicle arrival times at intersections. These predictions, combined with aggregated traffic characteristics such as queue length and waiting time, form the state representation for the reinforcement learning model. The SAC algorithm is then used to determine optimal phase durations. We evaluated the proposed approach using the SUMO traffic simulator in three different urban scenarios: a single intersection, a three-intersection arterial road, and a small road network. Experimental results demonstrate that the proposed method outperforms baseline approaches, including Deep Q-Network and a heuristic-based method, in terms of average travel time, time loss, and waiting time. Specifically, the SAC-based algorithm achieves reductions of up to 1.5\% in average travel time and up to 13\% in average waiting time across various simulation scenarios compared to the baseline methods. Furthermore, training convergence analysis and visualizations confirm the stability and effectiveness of the learned policy.</p>