Abstract：Short-term traffic flow forecasting plays an important role in the modern urban traffic,it can help intelligent transportation systems provide decision-making and improve people′s travel efficiency thus raising the accuracy of the short-term traffic flow prediction becomes the hot spot problems of current research,in this paper,by introducing the adaptive weighting strategies and longicorn must search strategy to improve the whale optimization algorithm,through the function simulation results prove the validity of the improved strategies,and then use the improved algorithm to optimize the least squares support vector machine(LSSVM)punishment factor and kernel function parameters and build forecasting model to forecast short-term traffic flow.The simulation results show that the aboa-LSSVM model constructed in this paper has advantages in the accuracy of short-term traffic flow prediction compared with other models,and is very suitable for traffic flow prediction.
胡松,成卫,李艾. 一种改进鲸鱼算法及其在短时交通流预测中的应用研究[J]. 小型微型计算机系统, 2021, 42(8): 1627-1632.
HU Song,CHENG Wei,LI Ai. Improved Whale Algorithm and Its Application in Short-term Traffic Flow Prediction. Journal of Chinese Computer Systems, 2021, 42(8): 1627-1632.