Data-driven insights: Unravelling traffic dynamics with k-means clustering and vehicle type differentiation

Urban traffic poses persistent challenges, necessitating innovative approaches for effective traffic flow analysis and management. This research adopts a data-driven methodology, employing different algorithms such as K-Means clustering, multiple linear regression to analyse real-world traffic flow. The study utilizes road traffic data collected over seven days, spanning seven hours each day, comprising traffic count, vehicle speed, and categorization by vehicle type. Through rigorous data preprocessing and K-Means clustering, the research identifies distinct traffic clusters, revealing patterns beyond average counts and speeds. Notably, the differentiation of vehicle types within clusters provides nuanced insights into transport mode interactions. The findings contribute to the traffic flow analysis field and offer practical implications for informed urban traffic management strategies. Understanding traffic dynamics aids in developing effective congestion mitigation measures. The study concludes by highlighting potential areas for future research and improvements in optimizing traffic dynamics, emphasizing the importance of data-driven approaches in addressing urban traffic challenges.