Utilizing RL and Web-Enhanced Commuting for Traffic Congestion Mitigation and Public Transportation Enhancement

In the face of escalating urban traffic congestion and inadequate public transportation efficiency, this study introduces a pioneering solution by merging Reinforcement Learning (RL) with web-enhanced commuting strategies. The central goal is to create a real-time adaptive system capable of optimizing traffic control and enhancing public transit management. The urgency of the problem lies in the elongated commute times and ecological ramifications caused by congested roads, underscoring the necessity for innovative and sustainable urban mobility solutions. Through the innovative combination of RL techniques and web-enhanced approaches, the study proposes a dynamic framework that can potentially alleviate congestion and augment public transportation efficiency through on-the-fly adjustments. Preliminary experimental results highlight encouraging advancements in both traffic flow management and public transportation efficiency, affirming the efficacy of the RL-augmented system. This integration of cutting-edge technology and commuter-focused strategies presents an avenue for reshaping urban mobility paradigms, mitigating traffic congestion, and optimizing public transportation systems, thereby paving the way for a more sustainable urban future.