Predicting the future spatiotemporal impacts and underlying cascading patterns of the traffic incidents that occur within the transportation systems could offer considerable benefits to society. As traffic incidents are responsible for approximately 25% of all traffic delays, costing the United States economy an estimated $87 billion annually, an accurate and explainable predictive solution can help mitigate these devastating spatiotemporal impacts by enabling quicker response times from emergency services, more effective traffic management, and better public advisories. Advanced analytics and machine learning algorithms can analyze real-world data to forecast the severity and duration of spatiotemporal events that occurred within the Cyber-Physical-Social Systems, thereby aiding in the allocation of resources and reducing both human and economic losses. The primary innovation of this project will be its ability to learn the complex relationship between traffic incidents and how their impact will cascade among the geometric structure of transportation systems and extract understandable rules and patterns for decision-makers. The objective of this project is to gather and analyze extensive traffic data from various heterogeneous platforms to learn more representative embeddings of traffic incidents. The goal is to create a range of interpretable graph mining and machine learning techniques that can enhance understanding of the cascading impacts not only in transportation s