Examining the potential of floating car data for dynamic traffic management

被引:13
作者
Houbraken, Maarten [1 ,2 ]
Logghe, Steven [2 ]
Audenaert, Pieter [1 ]
Colle, Didier [1 ]
Pickavet, Mario [1 ]
机构
[1] Univ Ghent, Dept Informat Technol, IGent Toren, Technol Pk Zwijnaarde 15, B-9052 Ghent, Belgium
[2] Be Mobile, Kardinaal Mercierlaan 1A, B-9090 Melle, Belgium
关键词
traffic engineering computing; floating car data; dynamic traffic management; road side equipment; RSE infrastructure investment; live country-wide FCD system; dynamic speed management system; MAP-MATCHING ALGORITHM; TRAVEL-TIME ESTIMATION;
D O I
10.1049/iet-its.2016.0230
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6-8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system.
引用
收藏
页码:335 / 344
页数:10
相关论文
共 31 条
  • [1] Data fusion algorithm for macroscopic fundamental diagram estimation
    Ambuhl, Lukas
    Menendez, Monica
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 71 : 184 - 197
  • [2] Ancona S, 2014, 2014 11TH IEEE/IFIP ANNUAL CONFERENCE ON WIRELESS ON-DEMAND NETWORK SYSTEMS AND SERVICES (IEEE/IFIP WONS 2014), P89, DOI 10.1109/WONS.2014.6814727
  • [4] Breitenberger S., 2004, Traffic Engineering and Control, V45, P396
  • [5] Map-matching algorithm for large-scale low-frequency floating car data
    Chen, Bi Yu
    Yuan, Hui
    Li, Qingquan
    Lam, William H. K.
    Shaw, Shih-Lung
    Yan, Ke
    [J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2014, 28 (01) : 22 - 38
  • [6] Local Path Searching Based Map Matching Algorithm for Floating Car Data
    Chen, Feng
    Shen, Mingyu
    Tang, Yongning
    [J]. 2011 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY ESIAT 2011, VOL 10, PT A, 2011, 10 : 576 - 582
  • [7] Deriving a surrogate safety measure for freeway incidents based on predicted end-of-queue properties
    Chou, Chih-Sheng
    Nichols, Andrew P.
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2015, 9 (01) : 22 - 29
  • [8] Coifman B, 1998, TRANSPORT RES REC, P181
  • [10] Traffic Estimation And Prediction Based On Real Time Floating Car Data
    de Fabritiis, Corrado
    Ragona, Roberto
    Valenti, Gaetano
    [J]. PROCEEDINGS OF THE 11TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2008, : 197 - +