【摘要】：基于低頻、低覆蓋率、數據來源多樣的GPS浮動車數據,在現有數據預處理方法的基礎上,以交叉口影響路段數據點為研究對象,研究出更合理且準確獲得交通參數的技術方案。GPS浮動車數據由于其具有全天候、多覆蓋等特性,能夠實時監(jiān)測交通參數,估計交通狀態(tài)。為克服數據本身缺陷,使數據能有效利用,精確得到交通參數,本研究獲取短時內路段所有數據點代表整體狀態(tài)。首先基于數據的特性和在路段分布的節(jié)律,利用曲線擬合及拉格朗日中值定理確定交叉口的影響范圍;其次在該范圍內利用改進K-Means聚類方法,確定初始聚類中心,并以有效性指數作為優(yōu)化目標確定聚類數;在此基礎上分配權重,結合交叉口影響范圍外的數據點,對整個交叉口影響路段的行程速度進行估計。用杭州市局部路網中GPS數據進行案例分析,驗證技術方案。通過實地調查獲取實驗真實值,分別討論了在主、次干路路段本方案估計差異,并與傳統(tǒng)模型進行了對比分析。分析表明,該方法得到的路段行程速度估計值與真實值較為接近,誤差較小,在城市主干路和次干路中的誤差分別為4.1%和9.5%,比傳統(tǒng)模型誤差更小更穩(wěn)定,能較好地滿足城市智能交通控制系統(tǒng)對于交通參數的精度要求。
[Abstract]:Based on the GPS floating car data with low frequency, low coverage and diverse data sources, based on the existing data preprocessing methods, the data points of intersection affected sections are studied. A more reasonable and accurate technical scheme for obtaining traffic parameters is proposed. GPS floating car data can monitor traffic parameters and estimate traffic status in real-time because of its all-weather and multi-coverage characteristics. In order to overcome the defect of the data, make the data can be used effectively, and get the traffic parameters accurately, all the data points in the short-term road section represent the whole state. Firstly, based on the characteristic of data and the rhythm of road section distribution, the influence range of intersection is determined by curve fitting and Lagrange mean value theorem. Secondly, the improved K-Means clustering method is used to determine the initial clustering center, and the efficiency index is used as the optimization objective to determine the clustering number. On the basis of this, the travel speed of the whole intersection is estimated by assigning weights and combining with the data points outside the influence range of the intersection. Using the GPS data of Hangzhou local road network to carry on the case analysis, validates the technical scheme. The actual value of the experiment was obtained through the field investigation, and the difference of the estimation of this scheme on the main and secondary trunk roads was discussed respectively, and the difference was compared with the traditional model. The analysis shows that the estimated value of road travel velocity obtained by this method is close to the real value and the error is small. The error in urban main road and secondary trunk road is 4.1% and 9.5% respectively, which is smaller and more stable than the traditional model. It can meet the precision requirement of urban intelligent traffic control system.
【作者單位】： 上海理工大學管理學院;
【基金】：教育部人文社會科學研究青年基金項目(17YJCZH225) 上海理工大學人文社會科學基金項目(SK17YB05)
【分類號】：U491

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