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We introduce a real-time crash safety system capable of predicting crashes 5-10 minutes in advance, using advanced machine learning techniques. The system integrates primary, severity, and secondary crash predictions into a unified framework, employing a multi-layered prediction strategy that enhances proactive traffic safety management by providing robust and actionable alerts. It uses a comprehensive segment-specific approach, utilizing well-established machine learning models suited to the unique characteristics of different road segment types, such as basic, merge, diverge, and ramp. Additionally, route-specific models capture regional variations in traffic patterns and environmental factors, ensuring that the system adapts to the unique characteristics of different routes. Performance evaluations demonstrate that the system achieves high sensitivity (0.798-0.918) and low false alarm rates (0.088-0.208), depending on the segment type. The use of the continuity concept ensures the consistency of high crash likelihood situations, which improves the reliability of predictions. The results show that the system can accurately predict crashes and provide proper warnings. It can make traffic operators to take proactive actions such as adjusting traffic management measures. By predicting both the occurrence and severity of crashes, as well as the likelihood of secondary crashes, the system enhances the overall efficiency and effectiveness of traffic crash management. This comprehensive approach to crash prediction represents a significant advancement in proactive traffic management and road safety enhancement.