ln order to make traffic prediction more accurately, aiming at the problems of random assignment and slow convergence of the traditional BP neural network, an improved sparrow search algorithm(SSA) is proposed to optimize the BP neural network prediction model. The model combines the SSA position update principle and the rooster position update method in the chicken optimization algorithm to improve the sparrow search algorithm, which avoids the algorithm from falling into the local optimum and the ineffectiveness of the position update, and at the same time effectively improves the convergence speed of the algorithm. The improved sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network, and the improved SSA-BP neural network prediction model is obtained. The four prediction models, namely, LSTM neural network, BP neural network, SSA-BP neural network and improved SSA-BP neural network, were trained and tested with traffic data, and the prediction results were compared and analyzed in terms of MAE, MAPE, MSE, RMSE and EC. The results show that the BP neural network is better than the LSTM neural network, and the optimized BP neural network prediction model with sparrow search algorithm reduced MAE by 0.28 veh/(3 min), MAPE by 1%, MSE by 2.72 veh/(3 min), and RMSE by 0.04 compared with the BP neural network prediction model; the optimized BP neural network prediction model with improved sparrow search algorithm reduced MAE by 1.31 veh/(3 min), MAPE by 4%, MSE by 9.2 veh/(3 min), RMSE by 0.18, and the goodness-of-fit was closer to 1. The improved SSA-BP prediction model outperforms the SSA-BP neural network prediction model and effectively improves the prediction accuracy of the BP neural network with a goodness-of-fit of 0.98, which is suitable for traffic volume prediction and can provide reliable prediction values for intelligent transportation systems.