【摘要】：隨著交通事業(yè)的發(fā)展和路網(wǎng)的延伸,高速公路發(fā)展水平與公眾交通需求呈現(xiàn)雙增長(zhǎng)趨勢(shì),交通信息服務(wù)日趨個(gè)性化、差別化、精細(xì)化。如何對(duì)不同車型用戶,不同氣象場(chǎng)景,不同時(shí)段等因素影響下的多維交通信息進(jìn)行挖掘與信息發(fā)布,成為新的研究課題�，F(xiàn)代快節(jié)奏的生活中,時(shí)間價(jià)值日益受到重視,出行者越來(lái)越關(guān)注行程時(shí)間的延誤和可靠性。及時(shí)發(fā)布行程時(shí)間預(yù)測(cè)值及其可靠度,能為駕駛者的路徑選擇提供支持�；诖�,本文利用收費(fèi)數(shù)據(jù)與氣象監(jiān)測(cè)數(shù)據(jù),以遼寧省高速公路為試驗(yàn)路段,開展行程時(shí)間預(yù)測(cè)及其可靠性研究,為公眾出行及相關(guān)部門管理運(yùn)營(yíng)提供決策依據(jù)。主要完成的工作及取得的成果包括:(1)研究分析車型、時(shí)間、氣象因素對(duì)行程時(shí)間的影響,設(shè)計(jì)了以行程時(shí)間為主題的多維數(shù)據(jù)倉(cāng)庫(kù)邏輯模型,搭建數(shù)據(jù)倉(cāng)庫(kù)結(jié)構(gòu)框架。針對(duì)非同源數(shù)據(jù)的集成問(wèn)題,提出了一種時(shí)空匹配法,實(shí)現(xiàn)聯(lián)網(wǎng)收費(fèi)數(shù)據(jù)與氣象監(jiān)測(cè)數(shù)據(jù)的集成。提出數(shù)據(jù)清洗、數(shù)據(jù)轉(zhuǎn)換的方法,清除了異常數(shù)據(jù),實(shí)現(xiàn)了數(shù)據(jù)格式統(tǒng)一,完善了收費(fèi)數(shù)據(jù)倉(cāng)庫(kù)。(2)研究行程時(shí)間稀疏數(shù)據(jù)和異常數(shù)據(jù)的處理方法。創(chuàng)新性地提出了"上、下游數(shù)據(jù)構(gòu)造法",解決了收費(fèi)數(shù)據(jù)的稀疏問(wèn)題。在相關(guān)研究的基礎(chǔ)上提出了"改進(jìn)四分法"的數(shù)據(jù)篩選方法,有效剔除了數(shù)據(jù)中的離群值。處理后,數(shù)據(jù)信息更加完整,貼近真實(shí)情況。設(shè)計(jì)了行程時(shí)間序列的提取方法,為行程時(shí)間預(yù)測(cè)研究做準(zhǔn)備。采用OLAP聯(lián)機(jī)分析處理技術(shù),提取多維行程時(shí)間信息,定量地分析了時(shí)間、車型、氣象等因素對(duì)行程時(shí)間的影響,驗(yàn)證了分維度研究的合理性。(3)研究行程時(shí)間序列的自相關(guān)與偏自相關(guān)特性,利用BIC準(zhǔn)則實(shí)現(xiàn)模型定階,利用最小二乘法進(jìn)行參數(shù)估計(jì),建立ARMA行程時(shí)間預(yù)測(cè)模型。增設(shè)車流量序列作為回歸變量,建立ARMAX預(yù)測(cè)模型,改善了傳統(tǒng)ARMA模型的預(yù)測(cè)效果。案例表明,ARMAX的行程時(shí)間預(yù)測(cè)效果良好,能夠滿足實(shí)際需要。改善了傳統(tǒng)ARMA模型的預(yù)測(cè)滯后問(wèn)題,最大百分比誤差較傳統(tǒng)ARMA模型的降低約5%。(4)研究歷史行程時(shí)間的分布特征,利用多種概率模型進(jìn)行擬合,經(jīng)過(guò)K-S假設(shè)檢驗(yàn)與擬合優(yōu)度的對(duì)比,證明對(duì)數(shù)正態(tài)分布是行程時(shí)間可靠性的最優(yōu)表達(dá)模型�；谠撃Ｐ�,確立了行程時(shí)間可靠性測(cè)度指標(biāo)的計(jì)算方法。選擇變異系數(shù)、緩沖指數(shù)、計(jì)劃時(shí)間指數(shù)、擁擠頻率作表征歷史行程時(shí)間可靠度的指標(biāo),運(yùn)用實(shí)例研究了車型、時(shí)間、氣象等多維因素對(duì)可靠度的影響。提出了預(yù)測(cè)行程時(shí)間可靠概率及預(yù)測(cè)行程時(shí)間緩沖指數(shù),所提出的指標(biāo)將未來(lái)行程時(shí)間與歷史統(tǒng)計(jì)行程時(shí)間相結(jié)合,實(shí)現(xiàn)對(duì)未來(lái)行程時(shí)間可靠程度的評(píng)價(jià),補(bǔ)充了行程時(shí)間可靠性指標(biāo)體系。案例表明預(yù)測(cè)行程時(shí)間可靠性指標(biāo)對(duì)指導(dǎo)路徑?jīng)Q策,引導(dǎo)公眾出行具有實(shí)際意義和重要作用。
[Abstract]:With the development of traffic and the extension of road network, the development level of expressway and the demand of public transportation are increasing, and the traffic information service is becoming individualized, differentiated and refined. How to mine and publish multi-dimensional traffic information under the influence of different vehicle users, different weather scenes and different time periods has become a new research topic. In modern fast-paced life, the value of time is paid more and more attention, and travelers pay more and more attention to the delay and reliability of travel time. Timely release of travel time prediction value and its reliability can provide support for driver's path selection. Based on this, this paper makes use of toll data and meteorological monitoring data, taking Liaoning Expressway as the experimental section, carries out travel time prediction and reliability research, and provides the decision basis for public travel and the management and operation of relevant departments. The main work and achievements are as follows: (1) the effects of vehicle, time and meteorological factors on travel time are analyzed. A multi-dimensional data warehouse logical model with travel time as the theme is designed, and the data warehouse structure framework is built. To solve the problem of integration of non-homologous data, a spatio-temporal matching method is proposed to realize the integration of network toll data and meteorological monitoring data. The methods of data cleaning and data conversion are put forward, the abnormal data is eliminated, the data format is unified, and the data warehouse is improved. (2) the processing methods of travel time sparse data and abnormal data are studied. The upstream and downstream data construction method is innovatively proposed, which solves the sparse problem of charge data. On the basis of related research, an improved quadrilateral data screening method is proposed, which can effectively eliminate outliers in the data. After processing, the data information is more complete, close to the real situation. The extraction method of travel time series is designed to prepare for the study of travel time prediction. OLAP OLAP OLAP technology is used to extract multidimensional travel time information and quantitatively analyze the influence of time, vehicle type, weather and other factors on travel time. (3) the autocorrelation and partial autocorrelation characteristics of travel time series are studied. BIC criterion is used to determine the order of the model, the least square method is used to estimate the parameters, and the ARMA travel time prediction model is established. The ARMAX prediction model is established by adding the traffic flow sequence as a regression variable, which improves the prediction effect of the traditional ARMA model. The case shows that the travel time prediction of ARMAX is effective and can meet the actual needs. The prediction lag problem of the traditional ARMA model is improved, and the maximum percentage error is reduced by about 5 times compared with the traditional ARMA model. (4) the distribution characteristics of the historical travel time are studied and fitted by various probability models, and the K-S hypothesis test is compared with the goodness of fit. It is proved that the lognormal distribution is the optimal representation model of travel time reliability. Based on this model, the calculation method of travel time reliability measure index is established. The variation coefficient, buffer index, planning time index and congestion frequency are selected as indicators to characterize the reliability of historical travel time. The effects of multi-dimensional factors such as vehicle type, time and meteorology on reliability are studied with examples. The reliability probability of predicting travel time and the buffer index of predicted travel time are proposed. The proposed index combines the future travel time with the historical statistic travel time to realize the evaluation of the reliability of the future travel time. The reliability index system of travel time is supplemented. The case shows that the reliability index of predicting travel time has practical significance and important role in guiding path decision and guiding public travel.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491

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