子查询返回的值不止一个。当子查询跟随在 =、!=、<、<=、>、>= 之后,或子查询用作表达式时,这种情况是不允许的。 <span style="FONT-FAMILY: 宋体; FONT-SIZE: 10pt">高速公路交通事件自动检测算法</span>-《哈尔滨工程大学学报》

[1]李琦,姜桂艳.高速公路交通事件自动检测算法[J].哈尔滨工程大学学报,2013,(09):1193-1198.[doi:10.3969/j.issn.1006-7043.201211058]
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高速公路交通事件自动检测算法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年09期
页码:
1193-1198
栏目:
出版日期:
2012-09-25

文章信息/Info

文章编号:
1006-7043(2013)09-1193-07
作者:
李琦1 姜桂艳2
1.青岛市城市规划设计研究院,山东 青岛 266071; 2.宁波大学 海运学院,浙江 宁波 315211
关键词:
交通运输系统工程交通事件自动检测收费数据标准偏差法
分类号:
U491
DOI:
10.3969/j.issn.1006-7043.201211058
文献标志码:
A
摘要:
为解决目前我国高速公路交通检测器布设数量严重不足所导致的交通事件检测效果不佳的问题,在分析了收费数据特征的基础上,设计了一种基于收费数据的交通事件自动检测算法.该算法以标准偏差法为基础,首先为了减少因交通波动引发的误警,提出了一种基于滚动时间序列的交通参数合成方法;在此基础上,为了减少因常发性交通拥挤引发的误警,提出了一种综合考虑交通参数数据横向时间序列和交通参数数据纵向时间序列的改进方案;进而,为了减少因算法自身的检测逻辑引发的误警,提出了一种基于数据分析时间窗口内的交通参数标准差以及当前采样间隔交通参数相对于其以前平均值改变程度的改进方案.采用我国浙江省沪杭甬高速公路的实测收费数据进行验证和对比分析的结果表明,在相同的误警水平下,本文算法的检测率明显优于标准偏差法,平均检测时间与标准偏差法基本持平,且本文算法具有良好的鲁棒性.

参考文献/References:

[1]姜桂艳.道路交通状态判别技术与应用[M].北京:人民交通出版社, 2004: 160169.

[2]TURNER S, ALBERT L, GAJEWSKI B, et al. Archived intelligent transportation system data quality: preliminary analyses of San Antonio transguide data [J]. Transportation Research Record, 2000(1719): 7784.

[3]MASTERS P H, LAM J K, WONG K. Incident detection algorithms for COMPASS——an advanced traffic management system[C]// Vehicle Navigation and Information Systems Conference. Dearborn, USA, 1991: 295310.

[4]SHLADOVER S, DESOER C, HEDRICK J. Automated vehicle control developments in the PATH program[J]. IEEE Transactions on Vehicular Technology, 1991, 40(1): 111130.

[5]YAMADA S. The strategy and deployment plan for VICS[J]. IEEE Communication, 1996, 34(10): 9497.

[6]ZHOU D S. An integrated traffic incident detection model [D]. Austin:University of Texas at Austin, 2000: 1020.

[7]AHMED F, HAWAS Y E. A thresholdbased realtime incident detection system for urban traffic networks[J]. ProcediaSocial and Behavioral Sciences, 2012, 48:17131722.

[8]DIA H, THOMAS K. Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data[J]. Information Fusion, 2011, 12(1): 2027.

[9]KARIM A, ADELI H. Comparison of the fuzzywavelet RBFNN freeway incident detection model with the California algorithm[J]. Journal of Transportation Engineering, ASCE, 2002, 8(1): 130.

[10]SRINIVASAN D, SHARMA V, TOH K A. Reduced multivariate polynomialbased neural network for automated traffic incident detection[J]. Neural Networks, 2008, 21

(2/3): 484492.

[11]温慧敏,杨兆升.交通事件检测技术的进展研究[J].交通运输系统工程与信息, 2005, 5(1): 2528.

WEN Huimin, YANG Zhaosheng. Recent development of traffic incident detection technologies [J]. Journal of Transportation Systems Engineering and Information Technology, 2005, 5(1): 2528.

[12]THOMAS K, DIA H. Development and evaluation of fractal dimension models for freeway incident detection [J]. Road and Transport Research Journal, 2004, 13 (2): 220.

[13]YUAN F, CHEU R L. Incident detection using support vector machines[J]. Transportation Research Part C: Emerging Technologies, 2003, 11(3): 309328.〖ZK)〗

[14]JIN X, CHEU R L. Development and adaptation of constructive probabilistic neural network in freeway incident detection [J]. Transportation Research Part C: Emerging Technologies, 2002, 10(2): 121147.

[15]MAK C L, FAN H S L. Algorithm fusion for detecting incidents on Singapore’s central expressway [J]. Journal of Transportation Engineering, 2006, 132(4): 321330.

[16]MAK C L, FAN H S L. Development of dualstation automated expressway incident detection algorithms[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(3): 480490.

[17]牛世峰,姜桂艳,李红伟.基于纵向时间序列的快速路交通事件检测算法[J].哈尔滨工业大学学报, 2011, 43(2): 144148.

NIU Shifeng, JIANG Guiyan, LI Hongwei. Automated detection algorithm for traffic incident in urban expressway based on lengthways time series of traffic parameters [J]. Journal of Harbin Institute of Technology, 2011, 43(2):144148.

相似文献/References:

[1]李琦,姜桂艳.高速公路交通事件自动检测算法 [J].哈尔滨工程大学学报,2013,(09):1193.[doi:10.3969/j.issn.10067043.201211058]

备注/Memo

备注/Memo:

基金项目:国家自然科学基金资助项目(51278257);高等学校博士学科点专项科研基金资助项目(20110061110034);浙江省自然科学基金资助项目(LY12F01013.

作者简介:李琦(1985), 男, 博士;

姜桂艳(1964),女,教授,博士生导师.

更新日期/Last Update: 2013-09-26