Break-taking behaviour pattern of long-distance freight vehicles based on GPS trajectory data

This paper focuses on the break-taking behaviour pattern of long-distance freight vehicles, providing a new perspective on the study of behaviour patterns and simultaneously providing a reference for transport management departments and related enterprises. Based on Global Positioning System (GPS) trajectory data, we select stopping points as break-taking sites of long-distance freight vehicles and then classify the stopping points into three different classes based on the break-taking duration. We then explore the relationship of the distribution of the break-taking frequency between the three single classifications and their combinations, on the basis of the break-taking duration distribution. We find that the combination is a Gaussian distribution when each of the three individual classes is a Gaussian distribution, contrasting with the power-law distribution of the break-taking duration. Then we experimental analysis the distribution of the break-taking durations and frequencies, and find that, for the durations, the three single classifications can be fitted individually by an Exponential distribution and together by a Power-law distribution, for the frequencies, both the three single classifications and together can be fitted by a Gaussian distribution,so that can validate the above theoretical analysis. Key words: break-taking behaviour, long-distance freight vehicle, statistical analysi

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans.This research was supported by Universitat Politecnica de Valencia (Vice-Rectorate for Research, Innovation and Transfer) under Grant SP20120646; Generalitat Valenciana under Grants PROMETEOII/2014/032, GV/2014/034 (Emergent Research Groups), and ISIC/2012/006; and the Spanish Administration and European Union FEDER Programme under Grant TEC2011-23403.Safont Armero, G.; Salazar Afanador, A.; Rodriguez, A.; Vergara Domínguez, L. (2014). On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods. Remote Sensing. 6(8):7546-7565. https://doi.org/10.3390/rs6087546S7546756568Le Bastard, C., Baltazart, V., Yide Wang, & Saillard, J. (2007). Thin-Pavement Thickness Estimation Using GPR With High-Resolution and Superresolution Methods. IEEE Transactions on Geoscience and Remote Sensing, 45(8), 2511-2519. doi:10.1109/tgrs.2007.900982Schafer, R. W., & Rabiner, L. R. (1973). A digital signal processing approach to interpolation. Proceedings of the IEEE, 61(6), 692-702. doi:10.1109/proc.1973.9150Salazar, A., Vergara, L., Serrano, A., & Igual, J. (2010). A general procedure for learning mixtures of independent component analyzers. Pattern Recognition, 43(1), 69-85. doi:10.1016/j.patcog.2009.05.013Vincent, E., Gribonval, R., & Fevotte, C. (2006). Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech and Language Processing, 14(4), 1462-1469. doi:10.1109/tsa.2005.858005Kullback, S., & Leibler, R. A. (1951). On Information and Sufficiency. The Annals of Mathematical Statistics, 22(1), 79-86. doi:10.1214/aoms/1177729694Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.819861Raghavan, R. S. (1991). A model for spatially correlated radar clutter. IEEE Transactions on Aerospace and Electronic Systems, 27(2), 268-275. doi:10.1109/7.78302Hyvärinen, A., Hoyer, P. O., & Inki, M. (2001). Topographic Independent Component Analysis. Neural Computation, 13(7), 1527-1558. doi:10.1162/089976601750264992Salazar, A., Safont, G., & Vergara, L. (2011). Application of Independent Component Analysis for Evaluation of Ashlar Masonry Walls. Lecture Notes in Computer Science, 469-476. doi:10.1007/978-3-642-21498-1_5