计算机工程2025，Vol.51Issue(2)：111-125,15.DOI:10.19678/j.issn.1000-3428.0068510
改进鲸鱼优化GRU的窄路短时车流量预测
Short-Time Traffic Flow Prediction on Narrow Roads Based on Improved Whale-Optimized GRU
摘要
Abstract
To address the unavoidable bottleneck in traffic scenes,the short-time traffic flow prediction of narrow roads is very important for optimizing path planning and improving traffic conditions.For traffic on narrow roads,an Improved Whale Optimization Algorithm(IWOA)-Gated Recurrent Unit(GRU)short-time narrow road traffic prediction model is proposed based on a good node-set initialization population,nonlinear parameter control,and Cauchy variation perturbation.An empirical study is conducted using the Simulation of Urban Mobility(SUMO)dataset.The experimental results show that IWOA has better global performance,convergence speed,and stability compared with WOA-GRU,Particle Swarm Optimization(PSO)-GRU,and Long Short-Term Memory(LSTM).The Root Mean Square Error(RMSE)of the proposed method decreased by 10.96％,28.71％,and 42.23％,respectively,and Mean Absolute Percentage Error(MAPE)decreased by 13.92％,46.18％,and 52.83％,respectively,indicating significant accuracy and stability.关键词
短时车流量预测/窄路段/鲸鱼优化算法/门控循环单元/SUMO软件Key words
short-time traffic flow prediction/narrow roads/Whale Optimization Algorithm(WOA)/Gated Recurrent Unit(GRU)/Simulation of Urban Mobility(SUMO)software分类
交通工程引用本文复制引用
贾硕,林士飏,杨苗会,孙滕..改进鲸鱼优化GRU的窄路短时车流量预测[J].计算机工程,2025,51(2):111-125,15.基金项目
教育部高等教育司产学合作协同育人汽车项目(202102473012) （202102473012）
山东省重点研发计划(重大科技创新工程)(2023CXGC010111). （重大科技创新工程）
