Research on real-time traffic safety warning application based on expressway gantry big data

With the continuous development of expressways, traffic safety has become a critical focus, revealing limitations in traditional monitoring methods, which often lack real-time capabilities and precision. This research constructs a real-time traffic safety warning model leveraging expressway gantry big data to provide accurate and timely safety alerts. By collecting, analyzing, and modeling gantry data—including analyses of abnormal interval speeds, identification of low-speed driving behavior, speeding behavior, and abnormal parking events—the study reveals key traffic operation patterns and vehicle characteristics. Various data models, such as gantry interval analysis and slow vehicle analysis models, support precise abnormal event predictions and enable timely dissemination of warnings. Results demonstrate that this system effectively identifies and quantitatively assesses multiple types of abnormal behaviors, offering critical technical support for expressway event warnings and traffic management. This approach ultimately enhances the efficiency of road traffic safety management.