[1]徐铖铖,刘攀,王炜,等.基于判别分析的高速公路交通安全实时评价指标[J].东南大学学报(自然科学版),2012,42(3):555-559.[doi:10.3969/j.issn.1001-0505.2012.03.032]
 Xu Chengcheng,Liu Pan,Wang Wei,et al.Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety[J].Journal of Southeast University (Natural Science Edition),2012,42(3):555-559.[doi:10.3969/j.issn.1001-0505.2012.03.032]
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基于判别分析的高速公路交通安全实时评价指标()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第3期
页码:
555-559
栏目:
交通运输工程
出版日期:
2012-05-20

文章信息/Info

Title:
Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety
作者:
徐铖铖 刘攀 王炜 蒋璇
东南大学交通学院, 南京 210096
Author(s):
Xu Chengcheng Liu Pan Wang Wei Jiang Xuan
School of Transportation, Southeast University, Nanjing 210096, China
关键词:
高速公路交通安全 Fisher判别分析 实时事故风险 交通事故 条件logistic回归模型
Keywords:
highway safety Fisher discriminant analysis real-time crash risk traffic accident conditional logistic regression model
分类号:
U491.31
DOI:
10.3969/j.issn.1001-0505.2012.03.032
摘要:
为了实时评价交通流变化对高速公路交通事故风险的影响,利用高精度交通流数据建立了高速公路交通安全实时评价指标.提取了美国加州I-880N高速公路上采集间隔为30s的实时交通流数据和事故数据,采用Fisher判别分析方法建立交通流参数的线性组合,以判别危险交通流状态.该线性组合被定义为高速公路交通安全实时评价指标,当该指标小于0时,代表危险交通流状态,有发生交通事故的风险; 当该指标大于0时,代表正常交通流状态,没有发生交通事故的风险.采用条件logistic回归模型进一步研究了该指标与交通事故风险之间的定量关系.研究结果表明,该指标预测事故的精度为65.7%.另外,该指标每减小1个单位,交通事故风险将提高1.8倍.
Abstract:
To evaluate the impacts of changing traffic flow conditions on the risks of crash on freeways, a quantitative indicator based on real-time traffic flow data is developed to predict the occurrences of crash on freeways. Crash data and real-time traffic data with an interval of 30s are extracted from a segment of the I-880 N freeway in the state of California, United States. A linear combination of traffic flow parameters which is defined as real-time crash indicator is developed by Fisher discriminant analysis to identify dangerous traffic conditions. Negative real-time crash indicators represent dangerous traffic conditions potentially leading to crash occurrences while positive crash indicators represent normal traffic conditions which theoretically will not lead to crash occurrences. The conditional logistic regression model is applied to quantify the impacts of real-time crash indicator on the crash risk. The results show that the accuracy of using real-time crash indicator to predict crash occurrences on freeways is 65.7%. Moreover, one unit decrease in real-time crash indicator may increase the crash risk by 1.8 times.

参考文献/References:

[1] Lord D,Manar A,Vizioli A.Modeling crash-flow-density and crash-flow-V/C ratio for rural and urban freeway segments [J].Accident Analysis and Prevention,2005,37(1):185-199.
[2] El-Basyouny K,Sayed T.Comparison of two negative binomial regression techniques in developing accident prediction models [J].Transportation Research Record,2006,1950:9-16.
[3] Hiselius L W.Estimating the relationship between accident frequency and homogeneous and inhomogeneous traffic flows [J].Accident Analysis and Prevention,2004,36(2):149-163.
[4] Greibe P.Accident prediction models for urban roads [J].Accident Analysis and Prevention,2003,35(3):273-285.
[5] Memon A Q.Road accident prediction models and the influence of traffic flow,road length,road class and vehicle class on accidents [C/CD] //Proceedings of the 87th Annual Meeting of the Transportation Research Board. Washington DC,2008.
[6] 钟连德,孙小端,陈永胜,等.高速公路V/C与事故率关系研究[J].北京工业大学学报,2007,33(1):33-40.
  Zhong Liande,Sun Xiaoduan,Chen Yongsheng,et al.Research on the relationship between V/C and crash rate on freeway [J].Journal of Beijing University of Technology,2007,33(1):33-40.(in Chinese)
[7] 崔红军,魏连雨,庞建勋.道路条件与交通安全的研究方法[J].西安公路交通大学学报,2001,21(4):36-39.
  Cui Hongjun,Wei Lianyu,Pang Jianxun.Research method of road condition and traffic accident [J].Journal of Xi’an Highway University,2001,21(4):36-39.(in Chinese)
[8] Abdel-Aty M,Uddin N,Abdalla F,et al.Predicting freeway crashes based on loop detector data using matched case-control logistic regression [J].Transportation Research Record,2004,1897:88-95.
[9] Abdel-aty M,Uddin N,Pande A.Split models for predicting multi-vehicle crashes during high-speed and low-speed operating conditions on freeways [J].Transportation Research Record,2005,1908:51-58.
[10] Abdel-Aty M,Pande A.Identifying crash propensity using specific traffic speed conditions [J].Journal of Safety Research,2005,36(1):97-108.
[11] Oh J,Oh C,Ritchie S,et al.Real-time estimation of accident likelihood for safety enhancement [J].Journal of Transportation Engineering,2005,131(5):358-363.
[12] Oh C,Oh J,Ritchie S.Real-time hazardous traffic condition warning system:framework and evaluation [J].IEEE Transactions on Intelligent Transportation Systems,2005,6(3):265-272.
[13] Lee C,Saccomanno F,Hellinga B.Analysis of crash precursors on instrumented freeways [J].Transportation Research Record,2002,1784:1-8.
[14] Lee C,Hellinga B,Saccomanno F.Real-time crash prediction model for application to crash prevention in freeway traffic [J].Transportation Research Record,2003,1840:67-77.
[15] Hossain M,Muromachi Y.Evaluating location of placement and spacing of detectors for real-time crash prediction on urban expressways [C/CD] //Proceedings of the 89th Annual Meeting of the Transportation Research Board.Washington DC,2010.
[16] Zheng Z,Ahna S,Monsere C.Impact of traffic oscillations on freeway crash occurrences [J].Accident Analysis and Prevention,2010,42(2):626-636.
[17] 张文彤.SPSS11统计分析教程[M].北京:北京希望电子出版社,2002:177-189.

备注/Memo

备注/Memo:
作者简介: 徐铖铖(1987—), 男, 博士生; 王炜(联系人), 男, 博士, 教授, 博士生导师,wangwei@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(50908050)、国家重点基础研究发展计划(973计划)资助项目(2012CB725402)、江苏省研究生科研创新计划资助项目(CXZZ_0164)、教育部博士生学术新人奖资助项目.
引文格式: 徐铖铖,刘攀,王炜,等.基于判别分析的高速公路交通安全实时评价指标[J].东南大学学报:自然科学版,2012,42(3):555-559. [doi:10.3969/j.issn.1001-0505.2012.03.032]
更新日期/Last Update: 2012-05-20