Modern Approaches to Traffic Management in Emergency Situations

The article discusses modern approaches to the organization of traffic in emergency situations, which is an urgent issue for improving the safety and efficiency of transport infrastructure. The main focus is on the integration of innovative technologies, intelligent control systems and information and analytical tools to optimize traffic in crisis conditions. The study analyzes the existing methods of traffic management during emergencies, such as natural disasters, man-made accidents, and military threats. It is proposed to use three main mathematical models to evaluate the effectiveness of traffic management: the Lighthill-Whizam-Richards (LWR) model, the queuing system model (QSM), and adaptive traffic management models. The LWR model allows predicting changes in traffic density depending on traffic volume and road conditions. The CMO model is used to analyze the operation of critical transportation hubs and assess their congestion levels, which is important for determining road capacity. Adaptive traffic management models provide flexible real-time traffic control through the use of machine learning and artificial intelligence algorithms. The modeling results, presented in the form of graphs and histograms, demonstrated the impact of various factors on traffic efficiency. The analysis of the data obtained has confirmed that the use of intelligent control systems can reduce congestion time, increase road capacity, and ensure prompt response to emergencies. The article identifies the main advantages of integrating modern technologies into the traffic management system: increasing the efficiency of traffic management, reducing the risk of accidents and improving the overall mobility of the population in emergency situations. It is recommended to introduce adaptive traffic management systems based on the analysis of real traffic to ensure the resilience of transport infrastructure to crisis impacts.