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Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post‐impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3‐stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean‐up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial‐temporal post‐impact variables (e.g., clean‐up time, recovery time, and accumulative …