Measurement: Sensors (Jun 2024)
The application of CNN in Smart city management and supervision under the background of Smart city
Abstract
Intelligent transportation management, as an important component of intelligent city supervision, has practical value in improving the effectiveness of urban management. To improve the efficiency of intelligent city traffic management, this study proposes to use convolutional neural networks to improve the performance of navigation and intelligent transportation networks in intelligent transportation. This network is used for vehicle recognition in navigation to predict road congestion and determine the optimal driving path. This structure is applied in the intelligent road network to monitor the driving status of vehicles on the road. It is combined with the vehicle networking system to locate abnormal vehicles to achieve intelligent traffic management and supervision. These results confirm that this algorithm achieves a vehicle recognition accuracy of 91.2 % after 96 iterations and completes the recognition process within 163 ms. This algorithm converges to a vehicle positioning accuracy of 82.4 % after 93 iterations and achieves positioning within 277 ms. This algorithm can not only improve the optimal path planning effect of navigation, but also identify abnormal vehicles within 77 ms. Therefore, the proposed algorithm has high practical value in intelligent transportation management.
