北华大学学报（自然科学版）Issue(6)：814-817,4.DOI:10.11713/j.issn.1009-4822.2015.06.028
基于二次指数平滑法和优化Kalman滤波的短时交通组合预测
Short-term Traffic Flow Forecasting Based on Exponential Smoothing and Kalman Filter
摘要
Abstract
Optimize and improve the traditional Kalman filter model to solve the low prediction accuracy and lag. Forecast the road section by using the second exponential smoothing method and Kalman filter model method,and then analyze the forecast result with MAE and MAPE. The results show that the predicted results are relatively accurate when modified factor α= 0. 5,at the same time,the optimization of the improved Kalman filter model also can improve measurement accuracy. Combination forecast of the two methods can effectively reduce the prediction error,but the forecast effect will be more apparent by using the weight combination.关键词
指数平滑法/Kalman滤波/交通量/预测结果/预测误差Key words
exponential smoothing/Kalman filter/traffic/predicted results/prediction error分类
交通工程引用本文复制引用
秦鸣,杨高飞,邓明君,张文强,冯博..基于二次指数平滑法和优化Kalman滤波的短时交通组合预测[J].北华大学学报（自然科学版）,2015,(6):814-817,4.基金项目
江西省自然科学基金项目(20142BAB201015) （20142BAB201015）
江西省科技厅科技计划项目(20123BBE50094) （20123BBE50094）
