A Federated Learning-Based Traffic Congestion and Fuel Monitoring

Abstract The transport system manages traffic between cities worldwide. In addition to cities, highways and other locations can also experience traffic congestion. The area’s existing transportation system is unsatisfactory without supervision. The constraints of the present traffic monitoring system in collecting road data and expanding its visual range are improved by using remote sensing data to identify congestion. Since some remote sensing data must be kept private, this issue must be resolved to safeguard the security of remote sensing data while deep learning training is underway. In contrast to the traditional deep learning training method, this work provides a federated learning methodology to detect automobile objects in remote sensing pictures, addressing the data privacy problem in the training stage of remote sensing data. This study uses a finetuned YOLOv6 model to detect different vehicles along with federated learning. This experiment uses real-time remote sensing data as training samples. The training results reach an accuracy of roughly 95.89%, and the estimated processing time is as short as 0.047 s. A method that effectively controls traffic and improves the passing-vehicle ratio at the intersection is also introduced. Using a mathematical process, the dependence of fuel use on trip time and fuel consumption is also determined, which helps reduce vehicle idle time and fuel consumption. To detect congestion, the system will automatically recognize automobiles as objects in a traffic scenario based on the final experimental findings.