The accurate prediction of traffic flow is paramount for the advancement of intelligent transportation systems. Despite this, current prediction models only account for either temporal or spatial features in isolation, without considering their interaction, impeding the model’s ability to express itself. In light of this, we propose the graph differential equations network (GDENet), an approach that can effectively mine spatiotemporal correlation. Specifically, we propose a spatiotemporal feature integrator (STFI), which alleviates the error caused by the deviation of the sampling distribution from the overall distribution. By incorporating temporal information into the model for training and combining it with spatial features, we thoroughly explore the spatiotemporal intrinsic association. When compared to state‐of‐the‐art methods, our proposed algorithm reduces memory consumption and elevates computational efficiency and the practical value. We conduct experiments with real‐world datasets, and our proposed model outperformed advanced prediction models.

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO‐ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO‐ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high‐quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO‐ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO‐ACO algorithm can plan a site energy dispatching route with shorter route, less time‐consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.