A new methodology for vehicle trajectory reconstruction based on wavelet analysis

Vehicle trajectories with high spatial and temporal resolution are known as the most ideal source of data for developing innovative microscopic traffic models. Aside from the method applied for collecting the vehicle trajectories, such data are more or less error-infected. The ever-increasing noise amplitude during the process of deriving the data (such as speed and acceleration) required for developing models, might change or even hide the structure of data and lead to useful information being overlooked. This highlights the importance of presenting the efficient methods which are adequate to remove noise and enhance the quality of vehicle trajectory data. Accordingly, in this paper a simple two-step technique based on wavelet analysis has been recommended for filtering errors and reconstructing trajectory data. Primarily, by using wavelet transform a special treatment was employed to identify and modify the outliers. Next, the noise in trajectory data was eliminated by applying the wavelet-based filter. The results of applying the proposed method to the synthetic noise-infected trajectory and the NGSIM dataset reveal how appropriate its performance is compared with other methodologies in terms of quantitative criteria. (C) 2016 Elsevier Ltd. All rights reserved.