Estimating Acceleration, Fuel Consumption, and Emissions from Macroscopic Traffic Flow Data

With increasing public concern about the environment, livability and sustainability have become important issues in dynamic traffic management (DTM). Microscopic fuel consumption and emission models use vehicle speed and acceleration as inputs and are suitable for investigating the environmental effects of DTM measures at the link level. However, the lack of microscopic traffic data limits the application of these models. A method is provided for acquiring microscopic information from macroscopic traffic data. The main approach is to reconstruct the traffic state and vehicle group trajectories with an adaptive smoothing method, derive acceleration from the reconstructed vehicle trajectories, and calculate fuel consumption and emissions with filtered speed and estimated acceleration as inputs. The derived acceleration is compared with vehicle trajectories from simulation. Validation of the method shows that the estimated acceleration reflects the congestion characteristics. A case study investigating the environmental benefits of a freeway control algorithm on a Dutch freeway was conducted to illustrate the application potential of the method.