Control method of urban intelligent parking guidance system based on Internet of Things

With the rapid development of the economy, people's living standards continue to improve, which has exacerbated the increase in urban motor vehicles. The increase in the number of cars has facilitated people's travel and promoted economic growth. However, with the continuous increase in the number of motor vehicles in XX, the problem of difficult parking is becoming more and more dangerous. In order to find a more effective, convenient and accurate parking space prediction effect, this article uses IoT technology to model the roads and main parking lots around XX stations, and uses adaptive genetic algorithms to induce drivers and simulate them. The optimal path and the shortest time for the driver to reach each parking lot from the current location are obtained. In this study, a wavelet neural network model is proposed. The data of the B underground parking lot is used to train and predict the model, and it is found that the prediction accuracy is high. The particle swarm optimization algorithm was used to optimize the wavelet neural network model. As a result, the error between the predicted value and the actual value was further reduced, and the accuracy was further improved. This present work proposes the optimal parking lot selection based on the Logit model. The experimental results show that the parking lot induction method based on the Logit model can realize the selection of the best parking lot. Combined with the optimal path selection, the driver is guided to reach the optimal parking lot on the optimal path.