A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation

Examining Crash Location Characteristics in Texas Between 2003 and 2017 to Assess the Effects of the Great Recession on Fatalities

Various studies have developed models to predict the number of crash fatalities based on the statistically most significant parameters in police-reported crash data during the Great Recession period (December 2007- June 2009 as per the National Bureau of Economic Research). However, no proper research has been conducted to study the spatial patterns for the fatal crash data during the same period. This study serves as an extension of the study conducted by Project 17-67 funded by NCHRP and aims to understand how the economic downturn affected the spatial patterns of crash fatalities between 2003 and 2017 in the state of Texas. The study divides the fatal crashes dataset into three time periods 2003-2007 (pre-recession era), 2008-2012 (recession era), and 2013-2017 (post-recession era). The study uses the optimized hotspot analysis tool, which first finds the critical distance (fixed distance band) at which spatial correlation between the crash locations is most significant for each dataset. It then finds the hotspots for all the datasets based on the critical distance. The study conducted a hotspot analysis based on two approaches: death counts per crash and death counts/AADT per crash. The use of AADT was considered necessary to remove the factor of traffic flow, which is one of the reasons for the higher number and severity of fatal crashes. The results obtained from the second approach are much more consistent in terms of the number of statistically significant points and their locations across the three time periods than the first approach and is, therefore, used for further analysis. The analysis showed that the number of hotspots and coldspots roughly doubled during the recession period compared to the pre-recession period, despite a reduction in the number of fatalities. Similarly, although the number of fatalities in the post-recession era increased, the number of hotspots and coldspots remained similar in numbers during the same period

Performance-Based Operations Assessment of Adaptive Control Implementation in Des Moines, Iowa RB09-016, August 2018

Maintaining optimal mobility on high-volume arterial traffic corridors is important to transportation agencies and the public. Corridor performance often can be enhanced by updating traffic signal timing, but most agencies find it necessary to prioritize their retiming efforts based on resource constraints. To facilitate prioritization, a set of arterial corridor performance measures was developed using INRIX probe vehicle data. These commercially available data are derived from in-vehicle global positioning system (GPS) observations transmitted wirelessly, eliminating the need for supplemental traffic observation infrastructure to be installed in the field. The main objective of this study was to present a methodology to compare arterial corridors in terms of mobility-based performance measures. This process can help agencies select the corridors that are in need of signal retiming and can help identify corridors suited for adaptive signal control implementation. The two-step methodology began by identifying the number of days in a year with abnormal traffic patterns and comparing the volume-normalized performance of the remaining segments to identify corridors that are problematic on normal days. The proposed methodology was applied to 12 corridors in Des Moines, Iowa, and 1 in Omaha, Nebraska. Three corridors were found to have a high number of anomalous days. Among the remaining corridors, three were identified as under-performing on normal days. In addition, the impact of implementing an adaptive signal control system on one corridor (University Avenue) was evaluated, where small improvements in travel rate and daily variation were observed, but the overall variability increased

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Robust damage detection in smart structures

This thesis is devoted to present some novel techniques in Structural Health Monitoring (SHM). SHM is a developing field that tries to monitor structures to make sure that they remain in their desired condition to avoid any catastrophe. SHM includes different levels from damage detection area to prognosis field. This work is dedicated to the first level, which might be considered the main and most important level. New techniques presented in this work are based on different statistical and signal processing methods such as Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc. These techniques are applied on the propagated waves that are activated and captured in the structure using appropriate transducers. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. To guarantee the efficiency of the suggested techniques, they are tested on different laboratory and real scale test benchmarks, such as aluminum and composite plates, fuselage, wing skeleton, tube, etc. Because of the variety of tested benchmarks, this thesis is called damage detection in smart structures. This variety may promise the ability and capability of the proposed methods on different fields such as aerospace and gas/oil industry. In addition to the normal laboratory conditions, it is shown in this work that environmental changes can affect the performance of the damage detection and wave propagation significantly. As such, there is a vital need to consider their effect. In this work, temperature change is chosen as it is one of the main environmental fluctuation factors. To scrutinize its effect on damage detection, first, the effect of temperature is considered on wave propagation and then all the proposed methods are tested to check whether they are sensitive to temperature change or not. Finally, a temperature compensation method is applied to ensure that the proposed methods are stable and robust even when structures are subjected to variant environmental conditions.La presente tesis doctoral se dedica a la exploración y presentación de técnicas novedosas para la Monitorización y detección de defectos en estructuras (Structural Health Monitoring -SHM-) SHM es un campo actualmente en desarrollo que pretende asegurarse que las estructuras permanecen en su condición deseada para evitar cualquier catástrofe. En SHM se presentan diferentes niveles de diagnóstico, Este trabajo se concentra en el primer nivel, que se considera el más importante, la detección de los defectos. Las nuevas técnicas presentadas en esta tesis se basan en diferentes métodos estadísticos y de procesamiento de señales tales como el Análisis de Componentes Princpales (PCA) y sus variaciones robustas, Transformada wavelets, lógica difusa, gráficas de Andrew, etc. Estas técnicas de aplican sobre las ondas de vibración que se generan y se miden en la estructura utilizando trasductores apropiados. Dispositivos piezocerámicos (PZT's) se han escogido para este trabajo ya que presentan características especiales tales como: alto rendimiento, bajo consumo de energia y bajo costo. Para garantizar la eficacia de la metodología propuesta,se ha validado en diferentes laboratorios y estructuras a escala real: placas de aluminio y de material compuesto, fuselage de un avión, revestimiento del ala de un avóin, tubería, etc. Debido a la gran variedad de estructuras utilizadas, su aplicación en la industria aeroespacial y/o petrolera es prometedora. Por otra parte, los cambios ambientales pueden afectar al rendimiento de la detección de daños y propagación de la onda significativamente . En este trabajo , se estudia el efecto de las variaciones de temperatura ya que es uno de los principales factores de fluctuación del medio ambiente . Para examinar su efecto en la detección de daños, en primer lugar, todos los métodos propuestos se prueban para comprobar si son sensibles a los cambios de temperatura o no. Finalmente , se aplica un método de compensación de temperatura para garantizar que los métodos propuestos son estables y robustos incluso cuando las estructuras se someten a condiciones ambientales variante