Speed Detection Based on the Adaptive Weighted Average Method

Abstract

Abstract:

With the development of intelligent transportation systems (ITS), the magnanimity realtime traffic data become a major study objectivities, and the key is how to get high quality realtime traffic information with low loss. An adaptive weighted average method for detecting speed is introduced in this paper. Compared with traditional approaches, this method is easier to implement, and the error is slightly larger than the minimum mean square error weighted average. This method does not need large storage space for traffic information, which can resolve the problem of realtime in the course of measuring the traffic parameters. The result of the MATLAB simulation shows that this method saves not only hardware but time. it matches well with the actual demand.

Key words: intelligent transportation, realtime, adaptive weight average, speed detection, weighting value

摘要：

随着智能交通的发展，海量的交通状态实时数据成为智能交通的研究对象，利用低损耗获取高实时性的交通流状态成为实际应用研究中的重点.本文引入一种自适应加权平均的车速检测方法，与以往的方法相比，自适应加权平均方法更加简单，并且容易实现；虽然其误差比最小均方差加权平均算法略大，但是这种方法不必以存储海量的交通信息数据为代价，解决了交通信息采集过程中的关键——实时性不高、存储容量大等实际问题.通过MATLAB仿真结果可以看到，此方法大大节省了数据存储空间和数据处理的时间，适合在实际工程应用中广泛推广.

关键词: 智能交通, 实时性, 自适应加权平均, 车速检测, 权值

CLC Number:

U491

HOU Liang, XU Gui-hua, WANG Jin-hua, MA Wei,CHEN Zuo-han. Speed Detection Based on the Adaptive Weighted Average Method[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(6): 48-51.

侯亮，许桂华，王进花，马威，陈作汉. 基于自适应加权平均的车速检测研究[J]. 交通运输系统工程与信息, 2012, 12(6): 48-51.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/abstract/abstract18492.shtml

http://www.tseit.org.cn/EN/Y2012/V12/I6/48

References

[1]王力,王川久,沈晓蓉.智能交通系统中实时交通信息采集处理的新方法[J].系统工程, 2005,23(2):86-89.[WANG L,WANG C J,SHEN X R. A New method of realtime information collection in intelligent transportation system [J]. Systems Engineering, 2005,23(2):86-89.]

[2]钱哨，张云鹏,黄少波.智能交通云: 基于云计算的智能交通系统[J].计算机与现代化，2010,(11):183-186. [QIAN S,ZHANG Y P,HUANG S B. Intelligent transport cloud: ITS based on cloud computing [J].Computer and Modernization, 2010,(11):183-186. ]

[3]郭军杰,闫茂德,陈荫三.高速公路网异构GIST信息共享的本体模型[J].交通运输工程学报, 2011,11(1):107-113.[GUO J J,YAN M D,CHEN Y S. Ontology model of information sharing for heterogeneous GIST in freeway network [J].Journal of Traffic and Transportation Engineering, 2011,11 (1):107-113.]

[4]童小华,程效军,刘世杰,等.基于J2EE高速公路网络地理信息系统平台研究[J].同济大学学报, 2006,34(12):1627-1631.[TONG X H,CHENG X J,LIU S J,et al. Development of J2EE and WebGIS based highway management system [J]. Journal of Tongji University(Natural Science), 2006,34(12):1627-1631.]

[5]曾滢,杨晓光,马莹莹.交叉口动态车道功能与信号控制协同问题研究[J]. 同济大学学报, 2009,37(7):903-908.[ZENG Y,YANG X G,MA Y Y. Study on synergy problem of dynamic laneuse assignment and signal control at intersections [J]. Journal of Tongji University (Natural Science), 2009,37(7):903-908.]

[6]杨兆升,杨庆芳,冯金巧.路段平均速度组合融合算法及其应用[J].吉林大学学报, 2004,34(4):675-678. [YANG Z S,YANG Q F,FENG J Q. Combined timespace average speed information fusion algorithm and its application [J]. Journal of Jilin University, 2004,34(4):675-678.]

[7]杨兆升,王爽,马道松.基础交通信息融合方法综述[J].公路交通科技, 2006,23(3): 111-116. [YANG Z S,WANG S,MA D S. Review of basic traffic data fusion methods[J]. Journal of Highway and Transportation Research and Development, 2006,23(3): 111-116.]

[8]瞿翌立,戴逸松.多传感器数据自适应加权融合估计算法的研究[J].计量学报, 1998,19(1):69-75.[ZHUO L L,DAI,Y S. Study of adaptive weighted fusion estimated algorithm of multisensor data[J]. ACTA Metrologica Sinica, 1998,19(1):69-75.]

[9]滕召胜.基于自适应加权数据融合的粮食水分快速测定议[J].农业机械学报, 1999,30(6):64-67.[TENG Z S. An intelligent instrument for quick measurement of cereal moisture content based on selfadaptive fusion of weighted data[J]. Transactions of the Chinese Society of Agricultural Machinery, 1999,30(6):64-67.]

[10]刘智勇.智能交通控制理论及其应用[M].科学出版社,2003年.[LIU Z Y. Intelligent traffic control theory and application [M]. Science Publishing Company, 2003.]

[11]Bataillou E, Thierry E, Rix H. Weighted averaging with adaptive weight estimation [J]. Computers in Cardiology,1991. Proceedings. 23-26 Sept. 1991:37-40.

[12]Yager R R, Filev D P. Induced ordered weighted averaging operators [J]. Systems, Man, and Cybernetics, Part B: Cybernetics IEEE Transactions on, 1999, 29(2):141-150.

[13]Christopher A Vaz, N V Thakor. Adaptive Fourier estimation of timevarying evoked potentials [J]. IEEE Transaction on Biomedical Engineering, 1989, 36(4):448-455.

[1]	GUO Lie , XU Lin-li, QIN Zeng-ke, WANG Xu. Analysis and Overview of Influencing Factors on Autonomous Driving Takeover [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 72-90.
[2]	XU Meng , LIU Tao , ZHONG Shao-peng , JIANG Yu. Urban Smart Public Transport Studies: A Review and Prospect [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 91-108.
[3]	CHENG Jun, ZHANG Li-yan, CHEN Qi-hong. A Map Aided Visual-inertial Fusion Localization Method for Autonomous Vehicles [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 117-126.
[4]	WANG Guo-dong , LIU Li , MENG Yu , MA Zhi-ping, ZHENG Hao-qing , GU Qing, BAI Guo-xing. Integrated Control of Trajectory Planning and Tracking for Vehicle Collision Avoidance [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 127-136.
[5]	MA Dong-fang , CHEN Xi , WU Xiao-dong , JIN Sheng. Mixed-coordinated Decision-making Method for Arterial Signals Based on Reinforcement Learning [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 145-153.
[6]	TIAN Hui-juan, LIU Jia-wei, ZHAI Jia-hao, DENG Lin-lin. Video-based Vehicle Speed Measurement Method Using Multiple Intrusion Lines [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 49-56.
[7]	JIA Yan-feng, QU Da-yi, ZHAO Zi-xu, WANG Tao, SONG Hui. Car-following Decision-making and Model for Connected and Autonomous Vehicles Based on Safety Potential Field [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 85-97.
[8]	ZHAO Jian-dong , CHEN Qin , JIAO Yan-li , ZHANG Kai-li , HAN Ming-min. Recognition of Abnormal Driving Behavior of Key Commercial Vehicles [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 282-291.
[9]	XU Dong-wei , PENG Hang , SHANG Xue-tian , WEI Chen-chen , YANG Yan-fang . Road Network Data Repair Based on Graph Autoencoder-generative Adversarial Network [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 33-41.
[10]	YOU Feng , LIANG Jian-zhong , CAO Shui-jin, XIAO Zhi-hao, WU Zhen-jiang , WANG Hai-wei. Dense Pedestrian Crowd Trajectory Extraction and Motion Semantic Information Perception Based on Multi-object Tracking [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 42-54.
[11]	WANG Fu-jian, YU Jia-hao , ZHAO Jin-huan , MEI Zhen-yu. Short-term Public Traffic Passenger Volume Forecasting Method Based on Real-time Relevance of Stations [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 131-144.
[12]	ZHANG Yi , YAO Dan-ya , LI Li , PEI Hua-xin , YAN Song , GE Jing-wei. Technologies and Applications for Intelligent Vehicle-infrastructure Cooperation Systems [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 40-51.
[13]	LONG Jian-cheng, GUO Jia-qi. Review and Prospect of Dynamic Traffic Assignment Research [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 125-138.
[14]	YU Zu-jun, ZHANG Chen-guang , GUO Bao-qing. Vehicle Simultaneous Localization and Mapping Algorithm with Lidar-camera Fusion [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 72-81.
[15]	ZHANG Wei-bin , ZHANG Pu-lin, SU Zi-yi , SUN Feng. Missing Data Repairs for Road Network Traffic Flow with Self-attention Graph Auto-encoder Networks [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 90-98.