A digital management method of urban transportation combining VAE-LSTM and PSO

Digital technology can effectively improve the efficiency of urban traffic management and greatly improve the travel experience of residents, and many intelligent algorithms are gradually applied to it. However, the existing management methods still have certain limitations, so the research proposes the variational self-coder-long and short-term memory network algorithm, and at the same time combines with the particle swarm optimization algorithm to construct a new urban traffic digital management model. The experimental comparison between the research model and the existing model shows that the model has an accuracy of 95.3% in monitoring traffic flow, an efficiency of 90.8% in restoring road access, and the shortest stopping route is 16.8m, which are all better than the comparison model. The above data fully proves the feasibility of the research model, which helps urban transportation to step into intelligent digital management faster.