Solving traffic congestion has significant impacts on society, the environment, and the economy. This system combines LSTM prediction models with AI technology, utilizing traffic data and various features to effectively forecast traffic conditions for the next 30 minutes. This capability not only enhances traffic efficiency but also promotes economic growth, improves environmental quality, enhances social welfare, and contributes to the sustainable development of cities.

Traffic congestion hinders the development and planning of cities, and addressing it has significant impacts on society, the environment, and the economy. This system utilizes LSTM technology to more accurately forecast traffic flow and congestion trends for the next 30 minutes. By adjusting prediction models based on new data, it effectively supports police deployment, enhances traffic management efficiency, and improves the ability to respond to emergencies.

This system is applied in urban traffic management, where it enhances road use and signal control by accurately predicting traffic flow and congestion. It supports public safety and emergency responses by identifying potential accident sites in advance, facilitating quicker interventions, and reducing carbon emissions.