Recognition and tracking of spatial-temporal congested traffic patterns on freeways

被引:86
|
作者
Kerner, BS [1 ]
Rehborn, H [1 ]
Aleksic, M [1 ]
Haug, A [1 ]
机构
[1] Daimler Chrysler AG, RIC, TS, Telemat Res, D-73734 Esslingen, Germany
关键词
local traffic measurements on freeways; classification of traffic phases; tracking of spatial-temporal congested patterns; freeway bottlenecks; suitability of the freeway infrastructure for congested pattern recognition; traffic control center; field trial evaluation of models ASDA/FOTO; three-phase traffic theory; wide moving jams; synchronized traffic flow;
D O I
10.1016/j.trc.2004.07.015
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The two models FOTO (Forecasting of Traffic Objects) and ASDA (Automatische Staudynamikanalyse: Automatic Tracking of Moving Traffic Jams) for the automatic recognition and tracking of congested spatial-temporal traffic flow patterns on freeways are presented. The models are based on a spatial-temporal traffic phase classification made in the three-phase traffic theory by Kerner. In this traffic theory, in congested traffic two different phases are distinguished: "wide moving jam" and "synchronized flow". The model FOTO is devoted to the identification of traffic phases and to the tracking of synchronized flow. The model ASDA is devoted to the tracking of the propagation of moving jams. The general approach and the different extensions of the models FOTO and ASDA are explained in detail. It is stressed that the models FOTO and ASDA perform without any validation of model parameters in different environmental and traffic conditions. Results of the online application of the models FOTO and ASDA at the TCC (Traffic Control Center) of Hessen near Frankfurt (Germany) are presented and evaluated. (C) 2004 Elsevier Ltd. All rights reserved.
引用
下载
收藏
页码:369 / 400
页数:32
相关论文
共 50 条
  • [31] Analysis of maximum traffic flow and its breakdown on congested freeways
    Bassan, Shy
    Ceder, Avishai
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2008, 387 (16-17) : 4349 - 4366
  • [32] Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies
    Tian, Chenyu
    Chan, Wai Kin
    IET INTELLIGENT TRANSPORT SYSTEMS, 2021, 15 (04) : 549 - 561
  • [33] Tracking vehicles in congested traffic
    Beymer, D
    Malik, J
    TRANSPORTATION SENSORS AND CONTROLS: COLLISION AVOIDANCE, TRAFFIC MANAGEMENT, AND ITS, 1997, 2902 : 8 - 18
  • [34] Tracking vehicles in congested traffic
    Beymer, D
    Malik, J
    PROCEEDINGS OF THE 1996 IEEE INTELLIGENT VEHICLES SYMPOSIUM, 1996, : 130 - 135
  • [35] Joint spatial-temporal attention for action recognition
    Yu, Tingzhao
    Guo, Chaoxu
    Wang, Lingfeng
    Gu, Huxiang
    Xiang, Shiming
    Pan, Chunhong
    PATTERN RECOGNITION LETTERS, 2018, 112 : 226 - 233
  • [36] Spatial-temporal pooling for action recognition in videos
    Wang, Jiaming
    Shao, Zhenfeng
    Huang, Xiao
    Lu, Tao
    Zhang, Ruiqian
    Lv, Xianwei
    NEUROCOMPUTING, 2021, 451 : 265 - 278
  • [37] Spatial-Temporal Neural Networks for Action Recognition
    Jing, Chao
    Wei, Ping
    Sun, Hongbin
    Zheng, Nanning
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2018, 2018, 519 : 619 - 627
  • [38] Spatial-temporal interaction module for action recognition
    Luo, Hui-Lan
    Chen, Han
    Cheung, Yiu-Ming
    Yu, Yawei
    JOURNAL OF ELECTRONIC IMAGING, 2022, 31 (04)
  • [39] CONTRASTING PATTERNS IN THE ACQUISITION OF SPATIAL-TEMPORAL TERMS
    RICHARDS, MM
    HAWPE, LS
    JOURNAL OF EXPERIMENTAL CHILD PSYCHOLOGY, 1981, 32 (03) : 485 - 512
  • [40] Prediction of Traffic Flow by Sequencing Spatial-Temporal Traffic Dependency on Highways
    Ganapathy, Jayanthi
    Paramasivam, Jothilakshmi
    IETE JOURNAL OF RESEARCH, 2024, 70 (06) : 5771 - 5783