Trajectory data-based traffic flow studies: A revisit

被引：149

作者：

Li, Li ^{[1
,2
]}

Jiang, Rui ^{[3
]}

He, Zhengbing ^{[4
]}

Chen, Xiqun ^{[5
]}

Zhou, Xuesong ^{[6
]}

机构：

[1] Tsinghua Univ, Dept Automat, BNRist, Beijing, Peoples R China

[2] Tsinghua Univ, Ctr Intelligent Connected Vehicles & Transportat, Beijing, Peoples R China

[3] Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China

[4] Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

[5] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China

[6] Arizona State Univ, Sch Sustainable Engn & Built Environm, Tempe, AZ 85287 USA

来源：

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES | 2020年 / 114卷

基金：

中国国家自然科学基金;

关键词：

Traffic flow; Trajectory data; Data collection; Big data; CAR-FOLLOWING MODEL; LANE-CHANGING BEHAVIOR; CONCAVE GROWTH-PATTERN; VEHICLE TRAJECTORIES; DRIVING BEHAVIOR; MICROSCOPIC SIMULATION; OSCILLATION PROPAGATION; RELAXATION PHENOMENON; FUNDAMENTAL DIAGRAM; EMISSION ESTIMATION;

D O I：

10.1016/j.trc.2020.02.016

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

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.

引用

页码：225 / 240

页数：16

共 262 条

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