|Multi-class traffic models describe traffic network based on different classes of vehicles, such as trucks, vans, and cars. In comparison with single-class models, this leads to representations that are closer to real traffic networks . Applying multi-class traffic models in traffic management therefore offers promising prospects of improving the control efficiency.
In this project, we develop multi-class traffic flow models and multi-class emission models. These multi-class traffic models are used in on-line model-based control approach (Model Predictive Control). We integrate multi-class traffic flow models with multi-class emission models and use them to find a balanced trade-off between total time spent and total emissions. We investigate the performance of both fast, first-order models (FASTLANE) and slower, more accurate models (METANET).
As a next step, Distributed Model Predictive Control will be applied in the multi-class traffic control approach, to improve the computation speed and refine the control performance. In addition, we will also consider robust control.