A Federated Machine Learning Approach to Predicting Traffic Flow for Virtual Traffic Lights

In order to anticipate traffic, modern traffic systems now employ non-parametric techniques like machine learning. The traffic management system’s execution depends on sensory data sent to it. A minor infrastructure breakdown, like a power outage, can significantly alter traffic patterns in the area. When this failure happens at a busy crossroads, it increases the time that cars idle, creates a traffic bottleneck, and may even result in a crash. This incident makes it clear that the existing system has weak places. A self-organizing traffic system called Virtual Traffic Light can be used to solve these problems. In this system, the vehicles at the intersection can collaboratively manage the traffic flow using data from every vehicle present. Federated machine learning can be adopted when the vehicles collaborate to predict traffic because data privacy is at the center of its operation. In this paper, we worked with traffic data from Austin Texas and focused on key metrics such as execution time and prediction accuracy of multiple federated prediction models. Among the models used, our results suggest that Stochastic Gradient Descent Regressor and Random Forest Regressor are a good choice for traffic prediction in our proposed Virtual Traffic Light system.