Heavy traffic data collection and detection of overloaded HGV

被引:2
|
作者
Stanczyk, Daniel [1 ]
Klein, Eric [1 ]
机构
[1] CETE E, F-57000 Metz, France
来源
关键词
Weigh-in-motion (WIM); pre-selection; video-WIM; sensors; specification; accuracy; heavy vehicles;
D O I
10.1016/j.sbspro.2012.06.994
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The knowledge of heavy traffic on the road network presents a strategic interest for the road operators, in order to : - increase mobility by adapting their policies to the traffic, - optimise the life expectancy of road surface and bridges, - improve safety. This implies the collection of the following data: number, time distribution, type (defined by the number of axles), speed, and weight. The traditional systems are able to count the vehicles and to measure the HGV's speed. However, the control of overloaded HGV was made randomly and the vehicles were stopped and weighed on parking areas. This method caused congestion and its efficiency was limited. As a result, no significant data was available on the HGV's type or weight and the knowledge on heavy traffic was restricted to time distribution and speed. To improve the national database on heavy traffic and to develop a new control method for overloaded HGV, the French Ministry of Transport defined a detection system based on weighting equipments and automatic number plate recognition (ANPR). The detection systems are connected to a national database, which can be give real-time information on the heavy traffic or be used for statistics. Today, around 25 detection systems are installed on the structural road network, fulfilling the following objectives: - building a national database on heavy traffic, - preselecting and identifying overloaded HGV to make controls on parking areas more efficient, - developing specific traffic management measures, - identifying, via ANPR, transporters regularly in breach with the law to make specific controls in the companies. In a first part, the presentation will describe the detection system (material, sensors, hardware and software) and its deployment on the French road network. In a second part, the presentation will be focused on the results, their precision, the first extractions made on the national database, and the global efficiency of the system. (C) 2012 Published by Elsevier Ltd. Selection and/or peer review under responsibility of the Programme Committee of the Transport Research Arena 2012
引用
收藏
页码:133 / 143
页数:11
相关论文
共 50 条
  • [21] DRIVERS' PRIVACY CONCERNS ON AUTOMATIC VEHICLE DETECTION SYSTEM FOR TRAFFIC DATA COLLECTION IN HONG KONG
    Wong, C. K.
    TRANSPORTATION SYSTEMS: ENGINEERING & MANAGEMENT, 2007, : 575 - 582
  • [22] HEAVY TRAFFIC CHARACTERISTICS OF A CIRCULAR DATA NETWORK
    AVIITZHAK, B
    BELL SYSTEM TECHNICAL JOURNAL, 1971, 50 (08): : 2521 - +
  • [23] Finding cardinality heavy-hitters in mussive traffic data and its application to anomaly detection
    Ishibashi, Keisuke
    Mori, Tatsuya
    Kawarara, Ryoichi
    Hrrokawa, Yutaka
    Kobayashi, Atsushi
    Yamamoto, Kimihiro
    Sakamoto, Hitoaki
    Asano, Shoichiro
    IEICE TRANSACTIONS ON COMMUNICATIONS, 2008, E91B (05) : 1331 - 1339
  • [24] Scheduling Sensor Data Collection with Dynamic Traffic Patterns
    Zhao, Wenbo
    Tang, Xueyan
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2013, 24 (04) : 789 - 802
  • [25] DATA-COLLECTION FROM ROAD TRAFFIC ACCIDENTS
    DOVE, AF
    PEARSON, JCG
    WESTON, PAM
    ARCHIVES OF EMERGENCY MEDICINE, 1986, 3 (03): : 193 - 198
  • [26] AN OPERATIONAL REVIEW OF TRAFFIC DATA-COLLECTION SYSTEMS
    LYLES, RW
    WYMAN, JH
    ITE JOURNAL-INSTITUTE OF TRANSPORTATION ENGINEERS, 1983, 53 (12): : 18 - 24
  • [27] TRAFFIC DATA-COLLECTION - WHY, WHEN AND HOW
    TURNER, D
    DAVIES, CA
    STERN, D
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-MUNICIPAL ENGINEER, 1992, 93 (01) : 9 - 18
  • [28] Network Traffic Data Collection for Machine Learning Analysis
    Chao, James
    Rodriguez, Ramiro
    SPIE FUTURE SENSING TECHNOLOGIES 2023, 2023, 12327
  • [29] Key Technology of Traffic Data Collection, Repair and Mining
    Wang, Xiaoxia
    Li, Zhanqiang
    SIXTEENTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS, 2017, : 473 - 481
  • [30] Traffic data collection using image processing technology
    Molnár, P
    Collins, TR
    TRAFFIC AND GRANULAR FLOW'99: SOCIAL, TRAFFIC, AND GRANULAR DYNAMICS, 2000, : 357 - 362