CAJ | 학술논문

采用K近邻的非参数回归方法对短时交通流量进行了预测,考察了模型中关键因素对预测效果的影响.在4种不同状态向量和预测算法组合下的实验方法比较中,以相邻四个时间间隔的流量和占有率数据作为状态向量,并采用带权重的预测算法取得了良好的效果.将利用K值构造的预测区间用于特殊路况的预测中,得到了明显的改进效果.最后,对非参数回归和神经网络的方法进行了比较,结果表明了非参数回归预测方法的高精度和强移植性.
채용K근린적비삼수회귀방법대단시교통류량진행료예측,고찰료모형중관건인소대예측효과적영향.재4충불동상태향량화예측산법조합하적실험방법비교중,이상린사개시간간격적류량화점유솔수거작위상태향량,병채용대권중적예측산법취득료량호적효과.장이용K치구조적예측구간용우특수로황적예측중,득도료명현적개진효과.최후,대비삼수회귀화신경망락적방법진행료비교,결과표명료비삼수회귀예측방법적고정도화강이식성.
This paper used the K-NN based nonparametric regression to forecast the short term traffic flow, and analyzed the effect caused by key factors' settings in the model. Within four methods of different setting in traffic state vector and forecast technique, the one which defines traffic flow and occupancy rate in 4 time lags as state vector, and uses weight-added forecast technique has the better simulation results. This paper applied the prediction interval calculated by K to forecast during unconventional road condition, and improved the forecasting results. Finally, nonparametric regression's advantages of high accuracy and strong transplant ability are showed while being compared with neural network.