AI-Enabled Traffic System
The dataset we have used, provides hourly traffic counts from four distinct junctions, comprising a total of 48,120 observations. Each entry includes a timestamp (DateTime), a junction identifier (Junction), the observed vehicle count (Vehicles), and a unique identifier (ID). The data highlights real-world complexities, as the sensors at these junctions operated over varying durations. While some junctions offer consistently recorded data, others have sparse or irregular observations.
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We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.
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