@article{accb736b51224b5ba58e07469aca2179,
title = "Trajectory data-based traffic flow studies: A revisit",
abstract = "In this paper, we review trajectory data-based traffic flow studies that have been conducted over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory that have been stimulated by the availability of trajectory data. We first highlight the critical role of trajectory data (especially the next generation simulation (NGSIM) trajectory dataset) in the recent history of traffic flow studies. Then, we summarize new traffic phenomena/models at the microscopic/mesoscopic/macroscopic levels and provide a unified view of these achievements perceived from different directions of traffic flow studies. Finally, we discuss some future research directions.",
keywords = "Big data, Data collection, Traffic flow, Trajectory data",
author = "Li Li and Rui Jiang and Zhengbing He and Chen, {Xiqun (Michael)} and Xuesong Zhou",
note = "Funding Information: This work was supported in part by the National Key Research and Development Program of China (2018YFB1600900), National Natural Science Foundation of China (61790565, 71871010, 71922019, 71771198), joint project of National Natural Science Foundation of China and Joint Programming Initiative Urban Europe (NSFC-JPI UE) (?U-PASS?, 71961137005), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV), Science and Technology Innovation Committee of Shenzhen (JCYJ20170818092931604), Zhejiang Provincial Natural Science Foundation of China (LR17E080002), and Young Elite Scientists Sponsorship Program by CAST (2018QNRC001). The authors would like to thank the anonymous reviewers for their helpful suggestions to improve this paper. Funding Information: This work was supported in part by the National Key Research and Development Program of China ( 2018YFB1600900 ), National Natural Science Foundation of China ( 61790565 , 71871010 , 71922019 , 71771198 ), joint project of National Natural Science Foundation of China and Joint Programming Initiative Urban Europe (NSFC-JPI UE) ({\textquoteleft}U-PASS{\textquoteright}, 71961137005 ), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV), Science and Technology Innovation Committee of Shenzhen ( JCYJ20170818092931604 ), Zhejiang Provincial Natural Science Foundation of China ( LR17E080002 ), and Young Elite Scientists Sponsorship Program by CAST ( 2018QNRC001 ). Publisher Copyright: {\textcopyright} 2020 The Authors",
year = "2020",
month = may,
doi = "10.1016/j.trc.2020.02.016",
language = "English (US)",
volume = "114",
pages = "225--240",
journal = "Transportation Research Part C: Emerging Technologies",
issn = "0968-090X",
publisher = "Elsevier Limited",
}