关键词: 关键词：交通流预测, 神经网络, 萤火虫算法, Levenberg-Marquardt算法

Abstract: Abstract: Traffic flow prediction is one of the important technical means to improve efficiency and reduce congestion in intelligent transportation systems. A BP neural network traffic flow prediction method based on improved Firefly Algorithm （FA） and Levenberg Marquardt （LM） algorithm is proposed to address the problems of slow convergence speed and low prediction accuracy in existing traffic flow prediction algorithms. This method utilizes an improved chaotic Firefly Algorithm to optimize the initial weights and thresholds of the BP neural network， and uses the LM algorithm instead of the traditional gradient descent method in the weight update stage to accelerate the convergence process and improve model accuracy. Finally， the LM-FA-BP algorithm is used to predict traffic flow. Based on real complex urban traffic data， multiple fusion models were compared through experiments. The prediction error of our model was significantly reduced compared to other models， with a 33.84% improvement in Mean Absolute Error （MAE） compared to the BP model and a 29.82% improvement compared to the FA-BP model. The model has been tested and implemented on actual roads， with a maximum accuracy of 98% （average absolute percentage error<2.0%）， reaching a high level. The improved LM-FA-BP model has higher accuracy and faster convergence speed in traffic flow prediction. The research results indicate that the model has broad application prospects， especially in intelligent transportation systems where it can effectively improve prediction accuracy.

Key words: Key words: traffic flow prediction, neural networks, firefly algorithm, Levenberg-Marquardt algorithm