Management of intersections with multi-modal high-resolution data

被引：16
|
作者：
Muralidharan, Ajith [1 ,3 ]
Coogan, Samuel [1 ,2 ]
Flores, Christopher [1 ]
Varaiya, Pravin [1 ]
机构：
[1] Sensys Networks Inc, Berkeley, CA 94710 USA
[2] UC, Dept EECS, Berkeley, CA 94720 USA
[3] Linkedln, Mountain View, CA USA
来源：
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES | 2016年 / 68卷
基金：
美国国家科学基金会;
关键词：
High-resolution data; Arterial data; Timing plans; Red light violations; Pedestrian occupancy; Intersection safety;
D O I：
10.1016/j.trc.2016.02.017
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
A high-resolution (HR) data system for an intersection collects the location (lane), speed, and turn movement of every vehicle as it enters an intersection, together with the signal phase. Some systems also provide video monitoring; others measure pedestrian and bicycle movements; and some have vehicle to infrastructure (V2I) communication capability. The data are available in real time and archived. Real time data are used to implement signal control. Archived data are used to evaluate intersection, corridor, and network performance. The system operates 24 x 7. Uses of a HR data system for assessing intersection performance and improving mobility and safety are discussed. Mobility applications include evaluation of intersection performance, and the design of better signal control. Safety applications include estimates of dilemma zones, red-light violations, and pedestrian-vehicle conflicts. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：101 / 112
页数：12
相关论文
共 50 条
  • [31] Multi-modal Air Trajectory Traffic Management
    Henderson, Thomas C.
    Marston, Vista
    Sacharny, David
    INTELLIGENT AUTONOMOUS SYSTEMS 18, VOL 2, IAS18-2023, 2024, 794 : 247 - 255
  • [32] Interoperable Multi-Modal Data Analysis Platform for Alzheimer's Disease Management
    Pang, Zhen
    Zhang, Shuhao
    Yang, Yun
    Qi, Jun
    Yang, Po
    2020 IEEE INTL SYMP ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, INTL CONF ON BIG DATA & CLOUD COMPUTING, INTL SYMP SOCIAL COMPUTING & NETWORKING, INTL CONF ON SUSTAINABLE COMPUTING & COMMUNICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2020), 2020, : 1321 - 1327
  • [33] Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation
    Peng, Cheng
    Li, Yangyang
    Jiao, Licheng
    Chen, Yanqiao
    Shang, Ronghua
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (08) : 2612 - 2626
  • [34] Intelligent analysis for medical multi-modal data
    Multimedia Tools and Applications, 2021, 80 : 17333 - 17333
  • [35] An Adaptive Multiplexer for Multi-Modal Data Communication
    Eid, Mohamad
    Cha, Jongeun
    El Saddik, Abdulmotaleb
    2009 IEEE INTERNATIONAL WORKSHOP ON HAPTIC AUDIO VISUAL ENVIRONMENT AND GAMES, 2009, : 111 - 116
  • [36] Scalable analysis of multi-modal biomedical data
    Smith, Jaclyn
    Shi, Yao
    Benedikt, Michael
    Nikolic, Milos
    GIGASCIENCE, 2021, 10 (09):
  • [37] MixGen: A New Multi-Modal Data Augmentation
    Hao, Xiaoshuai
    Zhu, Yi
    Appalaraju, Srikar
    Zhang, Aston
    Zhang, Wanqian
    Li, Bo
    Li, Mu
    2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WORKSHOPS (WACVW), 2023, : 379 - 389
  • [38] Spatial mapping of multi-modal data in neuroscience
    Hawrylycz, Mike
    Sunkin, Susan
    Ng, Lydia
    METHODS, 2015, 73 : 1 - 3
  • [39] Learning to Hash on Partial Multi-Modal Data
    Wang, Qifan
    Si, Luo
    Shen, Bin
    PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 3904 - 3910
  • [40] Intelligent analysis for medical multi-modal data
    Ying, Shihui
    Wu, Yang
    Zhu, Xiaofeng
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (11) : 17333 - 17333
12345