CAJ | 학술논문

优化改进传统的Kalman滤波模型，解决预测精度不高且预测滞后的问题.用二次平滑指数法和优化后的Kalman滤波模型两种方法组合对道路断面通过交通量进行短时交通预测，通过平均绝对误差( MAE)和平均绝对百分比误差(MAPE)分析预测结果.实例预测结果表明：当修正因子α=0.5时预测结果较为精确，同时，优化改进后的Kalman滤波模型的预测准确性有所提高.组合预测的两种方法都能够有效地降低预测误差，但按权重组合预测的效果更为明显.
우화개진전통적Kalman려파모형，해결예측정도불고차예측체후적문제.용이차평활지수법화우화후적Kalman려파모형량충방법조합대도로단면통과교통량진행단시교통예측，통과평균절대오차( MAE)화평균절대백분비오차(MAPE)분석예측결과.실례예측결과표명：당수정인자α=0.5시예측결과교위정학，동시，우화개진후적Kalman려파모형적예측준학성유소제고.조합예측적량충방법도능구유효지강저예측오차，단안권중조합예측적효과경위명현.
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.