Development of an Adaptive Traffic Light Signals Control Model Based on Long Short-Term Memory (LSTM) Network and Deep Q-Network: A Case of Mwenge Intersection, Dar es Salaam City, Tanzania

Urban traffic congestion is a major challenge in cities like Dar es Salaam. Traditional fixed-time traffic light systems fail to respond to real-time traffic changes, causing long waits, fuel wastage, and pollution. This study proposes a hybrid traffic light control model for Mwenge intersection using Long Short-Term Memory (LSTM) and Deep Q-Network (DQN) models. The LSTM predicts short-term traffic volumes, which regulate vehicle flow in the SIMULATION OF URBAN MOBILITY (SUMO) simulation environment. At the same time, the Deep Q-Network (DQN) adaptively adjusts signal phases based on real-time traffic states. Traffic data collected over 240 hours was used for model training and simulation. The LSTM achieved an average Test Mean Absolute Error (MAE) of 3 vehicles/min across all directions, indicating accurate traffic prediction. The Deep Q-Network (DQN) improved intersection performance with Average Wait Time (21.6s), Queue Length (9.2 vehicles), and Throughput (888 vehicles), outperforming the traditional model (38.4s, 17.1 vehicles, and 712 vehicles, respectively). Overall, the hybrid LSTM-DQN model reduced wait times by 43.8%, queue lengths by 46.2%, and increased throughput by 25%. The proposed approach offers an adaptive, cost-effective solution for optimising traffic signals in resource-constrained cities like Dar es Salaam, supporting smarter, more sustainable urban mobility. Policymakers are encouraged to adopt this model for busy intersections in Tanzania.