Analysis of Anomalies' Impact on Traffic Flow and Development of Heat Map Visualization for Optimizing Urban Congestion Management

Effective traffic flow management is a critical task in the context of the intensive development of urban transportation networks, which face substantial pressure from a diverse array of road users. The emergence of traffic congestion often exhibits a stochastic nature; rapid detection of movement anomalies and timely intervention to regulate traffic in these areas can minimize time losses and enhance road safety. Statistical methods have been employed to identify the relationships between accidents (or anomalies in traffic flow) and traffic parameters on adjacent road segments. An algorithm for heat map visualization has been developed based on the analysis of traffic data using object recognition technologies and data processing via artificial intelligence. The results of this work can significantly enhance the efficiency of urban infrastructure management and improve traffic safety.