To address the cooperative optimization challenge of vehicle trajectory regulation and signal timing in intelligent and connected hybrid traffic environments, a dual-module cooperative control framework based on deep reinforcement learning was proposed. A dual-loop coupling mechanism integrating time-space management and signal timing was developed for dynamic optimization. The time-space management module adopted an improved CACC (cooperative adaptive cruise control) car-following model with dynamic signal phase awareness, generating speed guidance strategies for connected and automated vehicles (CAVs) through spatiotemporal constraints to ensure continuous traffic flow. The signal timing module designed a double dueling deep Q-network (D3QN) algorithm with dynamic phase perception capability, constructing a 34-dimensional state space containing phase duration, cumulative queue length, and other parameters. A reward function targeting average vehicle queuing time was established. The algorithm utilized dual-network architecture and prioritized experience replay mechanism, achieving dynamic phase duration decisions through an ε-decay strategy to balance exploration and exploitation. Multi-scenario verification experiments were conducted using the SUMO-Python co-simulation platform under traffic flow conditions at the western intersection of Dongfanghong Square in Lanzhou City. Comparative tests demonstrated that compared with traditional fixed-time control, baseline D3QN algorithm, and conventional vehicle-infrastructure cooperative algorithms, the proposed model improved key metrics including average queue length, average speed, and queuing time. The results validate that deep coupling between vehicle trajectory control and signal timing optimization enhances intersection efficiency. The study provides an valuable research solution for cooperative vehicle trajectory-signal timing control in intelligent and connected hybrid traffic scenarios.