Deep Reinforcement Learning Based Traffic Signal Control in Multi-Intersection Environment: A Comparative Study of DQN Variants

This dataset contains simulation data and trained models for the study "Deep Reinforcement Learning Based Traffic Signal Control in Multi-Intersection Environment: A Comparative Study of DQN Variants".

The dataset includes:- SUMO simulation environment files for two scenarios (regulated left-turn and free left-turn)- Training and evaluation results for DQN, DDQN, Dueling DQN, and Dueling DDQN- Performance metrics: reward, average waiting time, and average speed- Scripts used for training and testing

The experiments are conducted in a multi-intersection environment with heterogeneous vehicle types (ambulance, fire truck, police, bus, truck, car, and motorcycle) with priority weights.

This dataset supports reproducibility of the results reported in the associated publication.