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.
Highlights We suggest an approach for generating virtual trajectories given sparse traffic data. The generated trajectories can be used for vehicle-kinematics-sensitive applications. Our estimation framework includes kernel smoothing, shear transformation and bi-cubic interpolation. The suggested approach is empirically evaluated using the NGSIM trajectory data set. The results show that our approach can improve the accuracy of emissions estimation.
Generating virtual vehicle trajectories for the estimation of emissions and fuel consumption
2022-02-16
Article (Journal)
Electronic Resource
English
Generating virtual vehicle trajectories for the estimation of emissions and fuel consumption
BASE | 2022
|Estimating fuel consumption and emissions based on reconstructed vehicle trajectories
Online Contents | 2014
|Motor vehicle emissions and fuel consumption modelling
Elsevier | 1979
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