RETRACTED: Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data.

The article focuses on a data-driven adaptive traffic signal control algorithm that integrates vehicle perception and traffic flow data to optimize urban traffic management. It combines an improved DV-hop localization algorithm for accurate vehicle positioning with phase space reconstruction of traffic flow time series to predict traffic conditions. Vehicles are categorized by size with assigned weights to influence green light time allocation adaptively, aiming to reduce congestion, delay, and stops at intersections. Experimental results from a real urban intersection demonstrate that this integrated approach outperforms existing methods in prediction accuracy, vehicle arrival rate, delay time, and number of stops. The study acknowledges limitations related to data accuracy, environmental interference, and urban traffic complexity, suggesting future improvements in data collection, modeling, and inclusion of additional factors such as weather and road conditions.