Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With   Multitask Learning

doi:10.1109/TITS.2014.2311123

CORC > 北京大学 > 信息科学技术学院

	Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
	Huang, Wenhao ; Song, Guojie ; Hong, Haikun ; Xie, Kunqing
刊名	ieee transactions on intelligent transportation systems
	2014
关键词	Deep learning multitask learning (MTL) task grouping traffic flow prediction INTELLIGENT TRANSPORTATION SYSTEMS NEURAL-NETWORKS
DOI	10.1109/TITS.2014.2311123
英文摘要	Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000343002400029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology; SCI(E); EI; 59; ARTICLE; gjsong@pku.edu.cn; 5; 2191-2201; 15
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/151990]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Huang, Wenhao,Song, Guojie,Hong, Haikun,et al. Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning[J]. ieee transactions on intelligent transportation systems,2014.
APA	Huang, Wenhao,Song, Guojie,Hong, Haikun,&Xie, Kunqing.(2014).Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning.ieee transactions on intelligent transportation systems.
MLA	Huang, Wenhao,et al."Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning".ieee transactions on intelligent transportation systems (2014).