东南大学学报（自然科学版）Issue(2)：413-419,7.DOI:10.3969/j.issn.1001-0505.2014.02.033
基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法
Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation
摘要
Abstract
A Kalman filter model considering the correlation property of traffic flow parameters is proposed to realize network short-term traffic flow prediction under jam traffic.The proposed state-space model of short-term traffic flow prediction is presented by solving the conservation equation u-sing Lax-Wendroff scheme.In addition,the spatial-temporal characteristics of the traffic flow on ur-ban expressway networks and the influence factors of on and off ramp are taken into account for flow rate prediction.The estimation algorithm of the proposed state-space model is designed based on the Kalman filter method.A region expressway network in Beijing is taken as an example to evaluate the performance of the proposed method.The results show that the maximum prediction mean absolute percentage error(MAPE)of the proposed Kalman filter model is less than 10%since the input of the Kalman filter model considers the impacts of spatial-temporal characteristics,and the mean of predic-tion MAPE is 7.96%.For the same predicted conditions,the mean prediction MAPEs of ARIMA and Elman model are 19.88% and 10.5 1%,respectively.关键词
交通流短时预测/阻塞流状态/状态空间模型/卡尔曼滤波Key words
short-term traffic flow prediction/jam traffic/state-space model/Kalman filter分类
交通工程引用本文复制引用
董春娇,邵春福,周雪梅,孟梦,诸葛承祥..基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法[J].东南大学学报（自然科学版）,2014,(2):413-419,7.基金项目
国家自然科学基金资助项目（51178032）． （）
