Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network

doi:10.1109/TITS.2018.2873747

	Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network
	Pang, Junbiao 1; Huang, Jing 2; Du, Yong 3; Yu, Haitao 3; Huang, Qingming 4,5; Yin, Baocai 6,7
	2019-09-01
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
卷号	20 期号:9 页码:3283-3293
摘要	Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper proposes to exploit the long-range dependencies among the multiple time steps for bus arrival prediction via recurrent neural network (RNN). Concretely, RNN with long short-term memory block is used to "correct" the prediction for a station by the correlated multiple passed stations. During the correlation among multiple stations, one-hot coding is introduced to fuse heterogeneous information into a unified vector space. Therefore, the proposed framework leverages the dynamic measurements (i.e., historical trajectory data) and the static observations (i.e., statistics of the infrastructure) for bus arrival time prediction. In order to fairly compare with the state-of-the-art methods, to the best of our knowledge, we have released the largest data set for this task. The experimental results demonstrate the superior performances of our approach on this data set.
关键词	Bus arriving time prediction recurrent neural network heterogenous measurement long-range dependencies multi-step-ahead prediction
DOI	10.1109/TITS.2018.2873747
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[61672069] ; Natural Science Foundation of China[61872333] ; Natural Science Foundation of China[61620106009] ; China Post-Doctoral Research Foundation ; Beijing Municipal Commission of Education[KM201610005034] ; Beijing Municipal Commission of Transport Science and Technology Project
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000484207200008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：48[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4763
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Pang, Junbiao; Yu, Haitao
作者单位	1.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 2.IBM China Investment Co Ltd, Beijing 10085, Peoples R China 3.Beijing Transportat Informat Ctr, Beijing 100161, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 6.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China 7.Beijing Univ Technol, Beijing 100124, Peoples R China
推荐引用方式 GB/T 7714	Pang, Junbiao,Huang, Jing,Du, Yong,et al. Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2019,20(9):3283-3293.
APA	Pang, Junbiao,Huang, Jing,Du, Yong,Yu, Haitao,Huang, Qingming,&Yin, Baocai.(2019).Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20(9),3283-3293.
MLA	Pang, Junbiao,et al."Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20.9(2019):3283-3293.