Generating virtual vehicle trajectories for the estimation of emissions and fuel consumption

Abstract

Microscopic emission models estimate second-by-second emissions and fuel consumption for individual vehicles based on vehicle trajectories. A vehicle trajectory describes how the position, speed and acceleration of a vehicle evolves over time. In practice, collecting a complete trajectory data set on a road stretch is not always feasible due to economic and privacy constraints. Therefore, several researchers suggest approaches for generating Virtual Vehicle Trajectories (VVT) given some partially observed traffic data. However, the traditional VVT generation approaches, being originally developed for travel time estimations, usually consider a simplified description of vehicle kinematics, hindering their applicability in emission modelling. In this paper, we suggest a novel approach for generating VVT, which facilitates their use in emission modelling. We empirically evaluate our method by comparing it to the traditional approaches. The results are promising, showing that, under certain experimental settings, our method can enhance the accuracy of emission estimations.