Dynamic control of intelligent parking guidance using neural network predictive control

The parking problem is a very important issue in city life because many citizens waste a large amount of energy and time trying to find suitable parking lots. To resolve this problem, various intelligent parking guidance systems have been introduced. However, the method of operating an intelligent parking guidance system remains in the infant stage. For successful operation, it is important to develop an effective method that assesses and selects the best parking lot in a real-time environment. In this vein, this study proposes a neural network-based predictive control approach that finds suitable weights for multiple factors dynamically so that the best performance of the intelligent parking guidance system can be achieved. The proposed method can enhance the performance of an intelligent parking guidance system via dynamic control in selecting the best parking lot. To evaluate the proposed approach, simulation tests and comparison with a traditional model have been conducted. As a result, the proposed approach provides a robust solution in an efficient manner under diverse parking environments. With the proposed approach, from the public interest viewpoint, the car parking problem can be approached more effectively.