计算机与现代化Issue(10)：80-88,9.DOI:10.3969/j.issn.1006-2475.2025.10.013
改进的FA-BP神经网络的交通流预测算法
Improved FA-BP Neural Network Traffic Flow Prediction Algorithm
摘要
Abstract
Traffic flow prediction is one of the important technical means to improve efficiency and reduce congestion in intelli-gent 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 accu-racy in existing traffic flow prediction algorithms.This method utilizes an improved chaotic Firefly Algorithm to optimize the ini-tial 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.关键词
交通流预测/神经网络/萤火虫算法/Levenberg-Marquardt算法Key words
traffic flow prediction/neural networks/firefly algorithm/Levenberg-Marquardt algorithm分类
计算机与自动化引用本文复制引用
王远锐,江凌云..改进的FA-BP神经网络的交通流预测算法[J].计算机与现代化,2025,(10):80-88,9.基金项目
江苏省重点研发项目(BE2020084-4) （BE2020084-4）
