Solving the Security Problem of Intelligent Transportation System With Deep Learning

Objective: the objective of this study is to study deep learning to solve the safety problems of intelligent transportation system. Method: the intelligent transportation system is improved by using the deep learning algorithm, and the improved system is simulated, and the data transmission performance, accuracy prediction performance and path change strategy of the system are statistically analyzed. Results: in the analysis of the data transmission performance of the system, the probability of successful propagation is found to be 100%. When the value of lambda is 0.01 similar to 0.05, it is the closest to the actual result and the data delay is the smallest. In the analysis of the accuracy prediction of the system, it is found that the system of this study has the best accuracy prediction performance with the increase of the number of iterations compared with other models in different categories. After further analyzing the path induction strategy of the system, it is found that the route guidance strategy of this study can effectively restrain the spread of congestion and achieve the effect of timely evacuation of traffic congestion in the face of congested road sections. Conclusion: it is found that the improvement of the intelligent transportation system by using deep learning can significantly reduce the data transmission delay of the system, improve the prediction accuracy, and effectively change the path in the face of congestion to suppress the congestion spread. Although there are some shortcomings in the experiment, it still provides experimental reference for the development of the transportation industry in the later stage.

Automated summary

This study aims to improve the intelligent transportation system using deep learning algorithms, leading to enhanced data transmission performance, accurate prediction, and effective path adjustment strategy to suppress congestion spread.