Abstract

Traffic dynamics play a pivotal role in urban planning, transportation management, and public safety. Understanding the intricate relationships between traffic flow, occupancy, and speed across both time and space is crucial for effective decision-making in traffic management systems. In this research paper, we present a comprehensive analysis of traffic data using a data-driven approach. Our study involves the development of an algorithm to systematically organize raw traffic data into a structured format, facilitating temporal and spatial analysis. The algorithm iterates over each timestep and location within the dataset, creating a dictionary for each combination of these parameters. Key traffic metrics such as flow, occupancy, and speed are extracted and incorporated into the generated dictionaries. The resulting dataset allows for detailed investigation and visualization of traffic patterns, aiding in the identification of congestion hotspots, temporal trends, and spatial anomalies. Our research contributes to the advancement of traffic management strategies by providing insights derived from a systematic analysis of real-world traffic data.

Keywords: Traffic Dynamics, Temporal Analysis, Spatial Patterns, Data-Driven Approach, LSTM.

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