Real-time traffic signal optimization for urban mobility: a reinforcement learning-enhanced framework with...

This study develops an intelligent, adaptable traffic control strategy using advanced management algorithms to enhance urban mobility in smart cities. The proposed method aims to minimize wait times, reduce congestion, and improve environmental health through better traffic management.

The approach thoroughly investigates and evaluates rule-based (Fixed-Time), optimization-based (Max-Pressure and Delay-Based), and machine-learning-driven (Reinforcement Learning) algorithms under various traffic conditions. This enables the system to automatically select the algorithm that most effectively minimizes wait times and reduces traffic congestion. Microscopic traffic simulations are employed to test the system, and various statistical analyses are conducted to evaluate performance. A Reinforcement Learning (RL) variant is further utilized to validate the method's effectiveness against alternative approaches.

The selected algorithms are executed on high-performance Field Programmable Gate Array (FPGA) platforms, which are suitable for embedded, energy-constrained smart city environments due to their lower latency and power consumption compared to general-purpose GPUs. The proposed system achieves a speedup of over 7× compared to modern high-speed general-purpose processing units (GPPUs), demonstrating the efficiency of the custom FPGA-based pipelined architecture in real-time traffic management applications.

The method not only improves traffic flow but also significantly reduces fuel consumption and carbon dioxide emissions. This study further explores how the proposed solution can be leveraged to address Kuwait's significant traffic challenges and contribute to improving air quality in the region.