Application of Reinforcement Learning in Traffic Flow Prediction
Traffic flow prediction is one of the core links of intelligent traffic management,which plays a vital role in realizing accurate scheduling and optimizing traffic flow.However,due to the complexity and uncertainty of the traffic system,the traditional traffic flow forecasting methods are often difficult to meet the requirements of accuracy and real-time.Based on this,this paper proposes a traffic flow prediction method based on reinforcement learning,and uses METR-LA data set to verify this method.The results show that this method shows good prediction performance in different scenarios,and effectively adapts to the spatio-temporal dynamic changes of the urban transportation system.
NETL
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