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Classification of Road Traffic Anomaly Based on Travel Data Analysis

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Autonomous Vehicles (AVs) collect travel data based on various smart devices and sensors, with the goal of enabling a vehicle to operate under its own power. Fully automation vehicle is expected to have full control over all functions. In order to manage large amounts of data in different formats collected via various types of communication channels, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, a deep learning concept, inspired by the human central nervous system, is proposed. Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from input datasets. In this case, it also refers to a three-layer structure: an input layer, a hidden layer, and an output layer. In this paper, various machine learning schemes have been proposed in order to detect anomalous conditions in urban road traffic. Furthermore, an evaluation of performance analysis based on simulation result of these schemes is performed. A deep learning concept is introduced to manage vehicle speed data, with the goal of detecting anomalous conditions in urban road traffic.
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Deep Leaning; Autonomous Vehicle; Traffic Anomaly; GNSS; Data Analysis

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