Automotive brake squeal generated during brake applications has become a major concern in automotive industry. Warranty costs for brake noise related complaints have been greatly increasing in recent years. Brake noise and vibration control are also important for the improvement of vehicle quietness and passenger comfort. In this work, the mode coupling instability mechanism is discussed and a method to estimate the critical value of friction coefficient identifying the onset of brake squeal is presented. This is achieved through a sequence of steps. In the first step, a modal expansion method is developed to calculate eigenvalue and eigenvector sensitivities. Different types of mode couplings and their relationships with possible onset of squeal are discussed. Then, a reduced-order characteristic equation method based on the elastically coupled system eigenvalues and their derivatives is presented to estimate the critical value of friction coefficient. The significance of this method is that the critical value of friction coefficient can be predicted accurately without the need for a full complex eigenvalue analysis, making it possible to determine the sensitivity of system stability with respect to design parameters directly.
Traffic violations like illegal parking, illegal turning, and speeding have become one of the greatest challenges in urban transportation systems, bringing potential risks of traffic congestions, vehicle accidents, and parking difficulties. To maximize the utility and effectiveness of the traffic enforcement strategies aiming at reducing traffic violations, it is essential for urban authorities to infer the traffic violation-prone locations in the city. Therefore, we propose a low-cost, comprehensive, and dynamic framework to infer traffic violation-prone locations in cities based on the large-scale vehicle trajectory data and road environment data. Firstly, we normalize the trajectory data by map-matching algorithms and extract turning behaviors, parking behaviors, and average speeds of vehicles. Secondly, we restore spatiotemporal contexts of driving behaviors to get corresponding traffic restrictions such as no parking, no turning, and speed restrictions. After matching the traffic restrictions with driving behaviors, we get the traffic violation distribution. Finally, we extract the spatiotemporal patterns of traffic violations to infer traffic violation-prone locations in cities and build an inference system. To evaluate the proposed framework, we conduct extensive studies on large-scale, real-world vehicle GPS trajectories collected from two cities located in the east and west of China, respectively. Evaluation results confirm that the proposed framework can infer traffic violation-prone locations in cities effectively and efficiently.
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