The classical BPR impedance function model was improved in order to more accurately calculate the impedance value of road traffic. The long short-term memory (LSTM) neural network was established to predict the positive and negative of the undetermined coefficient value in the improved function. The traffic data collected from the Shangtang Elevated to Zhonghe Elevated sections of Hangzhou City were used to verify the model. The results were compared with the traditional BPR impedance function method, the classic EMME/2 cone delay function, the BP neural network prediction method and the LSTM neural network prediction method. Results show that the improved model has higher accuracy and reliability under the premise that the data accuracy meets the requirements, indicating that the road impedance calculated by using the improved model can more realistically reflect the traffic operation condition of the road.