Traffic Prediction Model of Fuel Consumption and Carbon Emissions with Integration of Machine Learning and Federated Learning

Federated learning is a distributed learning paradigm that enables learning on multiple clients to solve the data privacy problem. This study proposes a multi-variable input traffic model based on federated learning, which predicts fuel consumption and emissions based on speed, acceleration, temperature, and seasons, and apply real-world vehicle history data for simulation. To avoid affecting predicting effect, auto-encoder is introduced to traffic model to remove outliers. The results show that the MSE after the outlier removal is at least 10% lower than the raw data, and this traffic model compared with the existing non-federal learning methods, the prediction loss value in MAE in winter only increase by about 0.2%-3.8%, the MSE in summer decrease by about 3%-17.8%. It is proved this model still have a favorable prediction performance while protecting privacy.