Reinforcement Learning-Driven Latency Minimization for Transportation and Traffic Engineering in Logistic SDN Networks

Transportation systems play a crucial role in the operation of contemporary societies because efficiency is inherently connected to the successful management of time. The latencies in the transportation systems play a crucial role in having an impact on productivity, energy consumption, and higher operational expenditures. These inefficiencies generate cost limitations and extra time for companies and individuals while increasing traffic congestion and deteriorating environments due to higher fuel usage. In this study, we introduce a new reinforcement learning technique to improve transportation networks while considering delays on the routed paths and the waiting time at important stopping points such as intersections and junctions. By concentrating on these parameters in our research, we propose the Reinforcement Learning-based Latency Optimized Traffic Engineering (RLLOTE) algorithm to enhance the overall efficiency of transportation systems, leading to enhanced traffic flow, reduced trip times, and minimized environmental impacts. One of the primary goals of the RL-LOTE algorithm, which is founded on the concept of reinforcement learning, is to reduce the total amount of time spent traveling by making use of previous experiences and making dynamic adjustments to the routes. Our approach considers multiple factors, including traffic congestion, road conditions, and waiting time, to develop a complete and effective strategy for minimizing transportation delays. Our research indicates that the utilization of the RLLOTE algorithm can enhance the efficiency and sustainability of transportation systems by significantly optimizing route planning.