本文關(guān)鍵詞：集裝箱碼頭閘口交通需求智能預(yù)測研究　出處：《河北工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 智能運(yùn)輸系統(tǒng) 集裝箱碼頭 閘口交通需求 船期表 季節(jié)性時(shí)間序列 人工神經(jīng)網(wǎng)絡(luò)

【摘要】：集裝箱碼頭閘口是集卡進(jìn)出碼頭的必經(jīng)關(guān)口,是港區(qū)集疏運(yùn)作業(yè)的關(guān)鍵節(jié)點(diǎn)和瓶頸。其開設(shè)的不合理,不僅使客戶長時(shí)間排隊(duì)等待,也造成由于排隊(duì)長度過長交通量過大而使港口道路擁堵嚴(yán)重,同時(shí)給碼頭企業(yè)的運(yùn)輸作業(yè)形成巨大的壓力,使得集裝箱碼頭企業(yè)由于這種需求的不確定性而使運(yùn)輸組織無法合理安排,不均衡性更加嚴(yán)重,企業(yè)經(jīng)濟(jì)損失嚴(yán)重,因此對(duì)集裝箱碼頭閘口交通需求進(jìn)行預(yù)測具有重要的理論和工程應(yīng)用價(jià)值。同時(shí),雖然集裝箱碼頭閘口交通需求具有非線性特點(diǎn),但集裝箱碼頭企業(yè)運(yùn)輸作業(yè)隨船期表的特性,又使其運(yùn)輸組織特別是閘口交通需求呈現(xiàn)出一定的規(guī)律性、內(nèi)隨機(jī)性。如何利用現(xiàn)代信息智能技術(shù)和其高度非線性特點(diǎn),對(duì)閘口交通需求進(jìn)行預(yù)測研究,成為本研究的出發(fā)點(diǎn)。本論文主要工作可概括為以下幾方面:(1)港區(qū)集疏運(yùn)交通系統(tǒng)分析。在討論系統(tǒng)基本要素、系統(tǒng)環(huán)境、交通特點(diǎn)等基礎(chǔ)上,設(shè)計(jì)了包括信息子系統(tǒng)、歷史信息數(shù)據(jù)庫、知識(shí)庫、在線預(yù)測子系統(tǒng)、離線預(yù)測子系統(tǒng)等構(gòu)成的集裝箱碼頭閘口交通智能預(yù)判系統(tǒng),并對(duì)各子系統(tǒng)的功能、原理以及智能預(yù)判系統(tǒng)的功能、原理予以說明,為集裝箱碼頭閘口交通需求的智能預(yù)測提供基礎(chǔ)平臺(tái)和工程應(yīng)用可能。(2)基于曲線擬合和SVM的碼頭閘口交通需求預(yù)測研究。運(yùn)用概率分布擬合方法,建立基于歷史信息的集裝箱碼頭閘口的概率分布模型,并采用SVM方法對(duì)每一班船集卡數(shù)量進(jìn)行預(yù)測,從而對(duì)各個(gè)時(shí)間段的集卡車數(shù)量進(jìn)行預(yù)測,并通過具體實(shí)例予以驗(yàn)證。(3)基于實(shí)時(shí)信息的碼頭閘口交通需求預(yù)測方法。在PDFM方法確定概率分布的基礎(chǔ)上,建立了一種基于實(shí)時(shí)信息的概率修正預(yù)測模型,通過實(shí)例進(jìn)行驗(yàn)證,顯示出該預(yù)測方法的高精度性。(4)基于季節(jié)性ANN的碼頭閘口交通需求預(yù)測方法。在集裝箱碼頭閘口交通需求源于船期表具有季節(jié)性和非線性特點(diǎn)系統(tǒng)分析的基礎(chǔ)上,提出基于每條班線船期表來預(yù)測其對(duì)碼頭閘口產(chǎn)生交通需求的思想,采用季節(jié)性時(shí)間序列方法處理集港車輛到達(dá)碼頭閘口隨時(shí)間的數(shù)量分布,建立處理后的時(shí)間序列數(shù)據(jù)與預(yù)測交通量之間非線性關(guān)系的人工神經(jīng)網(wǎng)絡(luò)模型。在天津港集裝箱碼頭閘口進(jìn)行具體例子應(yīng)用,證明了該方法優(yōu)于概率分布擬合方法和基于實(shí)時(shí)信息的概率修正預(yù)測方法,顯示其可行性。
[Abstract]:The gate of container terminal is the key node and bottleneck of collecting card entering and leaving wharf, and it is not only the unreasonable opening of container terminal, but also makes customers wait in line for a long time. It also causes the port roads to be congested seriously because of the excessive traffic volume of the long queue, and at the same time, it forms a huge pressure on the transport operations of the wharf enterprises. Because of the uncertainty of the demand, the container terminal enterprise can not arrange the transportation organization reasonably, the imbalance is more serious, and the economic loss of the enterprise is serious. Therefore, the prediction of container terminal gate traffic demand has important theoretical and engineering application value, at the same time, although container terminal gate traffic demand has nonlinear characteristics. However, the characteristics of the shipping schedule of container terminal enterprises make the transportation organization, especially the traffic demand of gate, show certain regularity. Internal randomness. How to use modern information intelligence technology and its highly nonlinear characteristics to predict the traffic demand of sluice gates. The main work of this paper can be summarized as follows: 1) Port area transportation system analysis. On the basis of discussing the basic elements of the system, system environment, traffic characteristics and so on. The intelligent prejudgment system of container terminal gate traffic is designed, which includes information subsystem, historical information database, knowledge base, on-line prediction subsystem and off-line prediction subsystem. The functions of each subsystem are also discussed. The principle and function of intelligent prejudgment system are explained. This paper provides a basic platform and engineering application for intelligent prediction of gate traffic demand of container terminal. (2) based on curve fitting and SVM, the traffic demand prediction of terminal gate is studied. The probability distribution fitting method is used. The probability distribution model of container terminal gate based on historical information is established, and the SVM method is used to predict the number of container trucks in each time period. The method of traffic demand prediction based on real-time information is verified by an example. Based on the PDFM method, the probability distribution is determined. A probabilistic modified prediction model based on real-time information is established and verified by an example. It shows the high accuracy of the prediction method. Based on seasonal ANN, the traffic demand forecast method of terminal gate is based on the systematic analysis of the seasonal and nonlinear characteristics of the container terminal gate traffic demand derived from the seasonality and nonlinear characteristics of the ship schedule. This paper puts forward the idea of forecasting the traffic demand for the gate of the wharf based on the schedule of each shift line, and adopts the seasonal time series method to deal with the distribution of the number of vehicles arriving at the terminal with time. An artificial neural network model of the nonlinear relationship between the time series data and the traffic volume is established. The model is applied to the gate of Tianjin Port Container Terminal. It is proved that this method is superior to the probability distribution fitting method and the probability correction prediction method based on real time information.
【學(xué)位授予單位】：河北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491;U691

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本文編號(hào)：1397773