Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning

doi:10.1109/TITS.2022.3145382

	Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning
	Wang, Chenxing 1; Zhao, Fang 1; Zhang, Haichao 1; Luo, Haiyong 2; Qin, Yanjun 1; Fang, Yuchen 1
	2022-01-31
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
页码	13
摘要	Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately estimate travel time of the given trajectory for multiple city scenarios. However, it faces great challenges due to complex factors including dynamic temporal dependencies and fine-grained spatial dependencies. To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module. By introducing meta learning techniques, the generalization ability of MetaTTE is enhanced using small amount of examples, which opens up new opportunities to increase the potential of achieving consistent performance on TTTE when traffic conditions and road networks change over time in the future. The DED model adopts an encoder-decoder network to capture fine-grained spatial and temporal representations. Extensive experiments on two real-world datasets are conducted to confirm that our MetaTTE outperforms nine state-of-art baselines, and improve 29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto datasets, respectively.
关键词	Estimation Trajectory Task analysis Urban areas Roads Data models Global Positioning System Spatial-temporal data mining travel time estimation meta learning deep learning
DOI	10.1109/TITS.2022.3145382
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2018YFB0505200] ; Action Plan Project of the Beijing University of Posts and Telecommunications - Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[62002026] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Beijing Natural Science Foundation[4212024] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[21310102D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Regio[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000751475600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[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/2XEOYT63/19019
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Fang; Luo, Haiyong
作者单位	1.Beijing Univ Posts & Telecommun, Natl Pilot Software Engn Sch, Sch Comp Sci, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Chenxing,Zhao, Fang,Zhang, Haichao,et al. Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:13.
APA	Wang, Chenxing,Zhao, Fang,Zhang, Haichao,Luo, Haiyong,Qin, Yanjun,&Fang, Yuchen.(2022).Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA	Wang, Chenxing,et al."Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):13.