The optimization effect of traffic speed prediction on travel path based on improved LSTM

The high-speed growth of big data and various cloud computing technologies has accelerated the process of various intelligent transportations. How to more comprehensively alleviate traffic congestion has become a key issue in urban construction. In view of this, this study proposes a traffic speed prediction and travel plan optimization method built on improved long short-term memory. In the process, an improved memory network model is established to predict the driving speed at the future end of the road section, and the improved ant colony optimization algorithms is integrated to optimize and re-plan the route of the traffic congestion travel plan. The results showed that when testing different datasets, the research method had a maximum fitness value of 97.21 when the number of training sessions was 82. When the number of iterations of the system is 85, the average absolute percentage error of the research method on the test set begins to approach 0. At this time, the error values of the three algorithms in the literature, the improved graph convolutional neural network, and the improved long short-term memory model are 0.018%, 0.034%, and 0.035%, respectively, which are significantly greater than the research methods. From the perspective of overall traffic speed prediction, the research method can achieve a fit of 95.42% when predicting the next 10 minutes speed time. All the above results show that the research method has a high adaptability to traffic speed prediction, can achieve dynamic programming of travel plan congestion paths, and provides reliable technical support for urban congestion mitigation methods.