Using high-resolution event-based data for traffic modeling and control: An overview

被引:47
|
作者
Wu, Xinkai [1 ]
Liu, Henry X. [2 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing 100191, Peoples R China
[2] Univ Minnesota, Dept Civil Engn, Minneapolis, MN 55455 USA
关键词
High-resolution data; Event-based data; Traffic modeling; Traffic control; Traffic signal systems; SINGLE-LOOP DETECTORS; REAL-TIME; VEHICLE REIDENTIFICATION; EMPIRICAL-ANALYSIS; KINEMATIC WAVES; CONTROL-SYSTEM; ARCHITECTURE; ALGORITHMS; OPTIMIZATION; MANAGEMENT;
D O I
10.1016/j.trc.2014.02.001
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle's on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:28 / 43
页数:16
相关论文
共 50 条
  • [1] Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data
    Wang, Song
    Xie, Xu
    Huang, Kedi
    Zeng, Junjie
    Cai, Zimin
    [J]. ENTROPY, 2019, 21 (08)
  • [2] Sequence Optimization at Signalized Diamond Interchanges Using High-Resolution Event-Based Data
    Hainen, Alexander M.
    Li, Howell
    Stevens, Amanda L.
    Day, Christopher M.
    Sturdevant, James R.
    Bullock, Darcy M.
    [J]. TRANSPORTATION RESEARCH RECORD, 2015, (2487) : 15 - 30
  • [3] Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach
    Karimpour, Abolfazl
    Khalilabadi, Pouya Jalali
    Homan, Bailey
    Wu, Yao-Jan
    Swartz, Diahn L.
    [J]. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 28 (05) : 679 - 694
  • [4] Estimating pedestrian delay at signalized intersections using high-resolution event-based data: a finite mixture modeling method
    Karimpour, Abolfazl
    Anderson, Jason C.
    Kothuri, Sirisha
    Wu, Yao-Jan
    [J]. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 26 (05) : 511 - 528
  • [5] Roundabout Critical Headway Measurement Based on High-Resolution Event-Based Data from Wireless Magnetometers
    Hainen, Alexander M.
    Rivera-Hernandez, Erick M.
    Day, Christopher M.
    McBride, Michael T.
    Grimmer, Gannon
    Loehr, Andrew J.
    Bullock, Darcy M.
    [J]. TRANSPORTATION RESEARCH RECORD, 2013, (2389) : 51 - 64
  • [6] High-Resolution Event-Based Data at Diamond Interchanges Performance Measures and Optimization of Ring Displacement
    Hainen, Alexander M.
    Stevens, Amanda L.
    Freije, Richard S.
    Day, Christopher M.
    Sturdevant, James R.
    Bullock, Darcy M.
    [J]. TRANSPORTATION RESEARCH RECORD, 2014, (2439) : 12 - 26
  • [7] Modeling Real-Time Cycle-Level Crash Risk at Signalized Intersections Based on High-Resolution Event-Based Data
    Yuan, Jinghui
    Abdel-Aty, Mohamed A.
    Yue, Lishengsa
    Cai, Qing
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (11) : 6700 - 6715
  • [8] Overview and Modeling Capabilities of an Event-Based Signal Controller
    Ardalan, Taraneh
    Sarazhinsky, Denis
    Dobrota, Nemanja
    Gavric, Slavica
    Stevanovic, Aleksandar
    [J]. SYMMETRY-BASEL, 2024, 16 (02):
  • [9] Advances in an Event-Based Spatiotemporal Data Modeling
    Zhu, Xinming
    Liu, Haiyan
    Xu, Qing
    Liu, Jun'nan
    Lihua, Xiaoyang
    [J]. SCIENTIFIC PROGRAMMING, 2021, 2021
  • [10] Short-Term Traffic Forecasting Using High-Resolution Traffic Data
    Li, Wenqing
    Yang, Chuhan
    Jabari, Saif Eddin
    [J]. 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,