Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations

被引:31
|
作者
Tahmasbi, Rasool [1 ]
Hashemi, S. Mehdi [1 ]
机构
[1] Amirkabir Univ Technol, Intelligent Transportat Syst Res Inst, Tehran 158754413, Iran
关键词
Hull-White model; Ito integral; stochastic differential equation (SDE); traffic flow; TRAFFIC FLOW PREDICTION; NEURAL-NETWORKS; MULTIVARIATE;
D O I
10.1109/TITS.2013.2278614
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Traffic flow prediction can be used for the management of traffic control systems and can be applied toward improving traffic light split times at intersections. In this paper, we developed a methodology for the short-term prediction of traffic flow using the stochastic differential equation (SDE). Since the current volume depends on the previous short-term volume and time, we used the Hull-White model. With the proposed method, a flexible short-term prediction of volume is suggested. It is possible to simulate traffic conditions easily and also detect incidents precisely. This method is applied in Tehran's highways, and the obtained results are compared with previous artworks. Our results offered a better fit to the traffic volume.
引用
收藏
页码:250 / 259
页数:10
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