The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management.