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The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable …