At present, vehicle routing optimization has become the key to improving logistics efficiency and reducing costs. This article proposes an improved ant colony algorithm to address the limitations of traditional ant colony algorithms in the optimal path problem for vehicles. The core of this study is to improve the pheromone update model of ant colony algorithm and validate it by constructing an experimental environment. The improved ant colony algorithm proposed in this article has significant performance improvements in solving vehicle path optimization problems, and is feasible and superior in practical applications, especially in terms of search efficiency. This algorithm provides a new perspective for future research directions.
KEYWORDS: Education and training, Data modeling, Neural networks, Statistical analysis, Analytical research, Roads, Data hiding, Transportation, Signal filtering, Tunable filters
On the basis of current research on traffic flow prediction, the article proposes a traffic flow prediction method based on the fusion of K-Means algorithm and GRU. This method first uses K-means for clustering analysis of traffic flow and establishes a traffic flow pattern database, and then predicts traffic flow through GRU training. After simulation experiments, the MAPE and RMSE values of the traffic flow prediction method based on the fusion of K-Means and GRU are lower than those of traditional GRU, LSTM, KNN, SAES, and SVM, and the fitting effect is good. It is a reference traffic prediction method.
Traffic flow prediction has a good guiding effect on traffic control. In response to the current inability of road traffic flow prediction methods to fully reveal the inherent laws of traffic flow, and considering the issue of fully considering spatiotemporal correlation in traffic flow prediction, this paper proposes an LSTM (Long Short-Term Memory) model based on Bayesian optimization. Experimental studies have shown that the LSTM model based on Bayesian optimization has good performance and high prediction accuracy.
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