Short-term traffic flow forecasting using adaptive echo state networks

In recent years, with the increase in vehicle volume, the traffic system has been inconvenienced. Therefore, the timeliness and accuracy of traffic flow prediction are crucial to the overall efficiency of the traffic system. Echo state network (ESN) has attracted extensive attention in the field of time series and traffic flow prediction due to its simple concept, low computational cost and simplicity. However, the traditional ESN model has certain limitations in terms of prediction accuracy and stability when facing complex and changeable traffic flow data. In order to solve this problem, this paper proposes an ESN prediction model combined with adaptive optimization. The simulated annealing algorithm is used to optimize the configuration of several important parameters of ESN, thereby improving the prediction performance and stability of the model. The experimental results show that the optimized ESN model is significantly better than the traditional ESN method in terms of prediction accuracy and stability. The monthly mean absolute percentage error (MAPE) is reduced from 10.05% to 6.6%, and the average error of each detector is reduced from 9.5% to 6.61%. This study proposes a more efficient and reliable method for short-term traffic flow prediction, which shows potential value in practical applications.