哈尔滨工程大学学报
哈爾濱工程大學學報
합이빈공정대학학보
JOURNAL OF HARBIN ENGINEERING UNIVERSITY
2013年
9期
1193-1198,1208
,共7页
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
交通運輸繫統工程%交通事件自動檢測%收費數據%標準偏差法
교통운수계통공정%교통사건자동검측%수비수거%표준편차법
traffic and transportation engineering%automatic incident detection%toll collection data%standard devia-tion algorithm
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。
為解決目前我國高速公路交通檢測器佈設數量嚴重不足所導緻的交通事件檢測效果不佳的問題，在分析瞭收費數據特徵的基礎上，設計瞭一種基于收費數據的交通事件自動檢測算法。該算法以標準偏差法為基礎，首先為瞭減少因交通波動引髮的誤警，提齣瞭一種基于滾動時間序列的交通參數閤成方法；在此基礎上，為瞭減少因常髮性交通擁擠引髮的誤警，提齣瞭一種綜閤攷慮交通參數數據橫嚮時間序列和交通參數數據縱嚮時間序列的改進方案；進而，為瞭減少因算法自身的檢測邏輯引髮的誤警，提齣瞭一種基于數據分析時間窗口內的交通參數標準差以及噹前採樣間隔交通參數相對于其以前平均值改變程度的改進方案。採用我國浙江省滬杭甬高速公路的實測收費數據進行驗證和對比分析的結果錶明，在相同的誤警水平下，本文算法的檢測率明顯優于標準偏差法，平均檢測時間與標準偏差法基本持平，且本文算法具有良好的魯棒性。
위해결목전아국고속공로교통검측기포설수량엄중불족소도치적교통사건검측효과불가적문제，재분석료수비수거특정적기출상，설계료일충기우수비수거적교통사건자동검측산법。해산법이표준편차법위기출，수선위료감소인교통파동인발적오경，제출료일충기우곤동시간서렬적교통삼수합성방법；재차기출상，위료감소인상발성교통옹제인발적오경，제출료일충종합고필교통삼수수거횡향시간서렬화교통삼수수거종향시간서렬적개진방안；진이，위료감소인산법자신적검측라집인발적오경，제출료일충기우수거분석시간창구내적교통삼수표준차이급당전채양간격교통삼수상대우기이전평균치개변정도적개진방안。채용아국절강성호항용고속공로적실측수비수거진행험증화대비분석적결과표명，재상동적오경수평하，본문산법적검측솔명현우우표준편차법，평균검측시간여표준편차법기본지평，차본문산법구유량호적로봉성。
In order to solve the problem of ineffective incident detection due to the severe shortage of traffic sensors for expressways in China , on the basis of analyzing toll data characteristics , an automatic incident detection algo-rithm using toll collection data was designed .The algorithm was based on standard normal deviation algorithm . First, in order to reduce the false alarms caused by traffic fluctuations , this paper proposed a traffic data synthetic method based on rolling time series .On the basis of the first step , in order to reduce the false alarms caused by re-curring congestion , this paper proposed a modification by comprehensively considering the horizontal time series and the longitudinal time series of traffic parameter data .Furthermore , in order to reduce the false alarms caused by detection logic of the algorithm itself , this paper proposed an improved scheme based on the standard deviation val-ue of traffic parameters and the current traffic flow minus the mean in the data analyzing time window .The proposed algorithm was tested with field data collected from the Hu-Hang-Yong Expressway in China .The test and compari-son analysis results indicate that at the same false alarm rate level , the detection rate of the proposed algorithm is significantly better than the standard normal deviation algorithm , the mean time of detection is basically equivalent to that of the standard normal deviation algorithm .Moreover , the proposed algorithm has very strong robustness .
