發(fā)布時(shí)間：2018-04-16 03:18

  本文選題：短時(shí)預(yù)測 + 交通狀態(tài)　； 參考：《大連海事大學(xué)》2015年碩士論文

【摘要】：近年來,隨著社會(huì)經(jīng)濟(jì)的發(fā)展,機(jī)動(dòng)車數(shù)量的飛速增長,為生活帶來了交通上的便捷,但同時(shí)也加重了道路擁堵程度。由于受土地資源等限制,城市中難以通過擴(kuò)建道路滿足通行需求,因此,智能交通系統(tǒng)成為國內(nèi)外解決擁堵的研究熱點(diǎn)。短時(shí)交通狀態(tài)預(yù)測是ITS中交通誘導(dǎo)和控制的關(guān)鍵,短時(shí)交通狀態(tài)預(yù)測方法的應(yīng)用,取得了一定的效果,但大部分方法是基于固定檢測器進(jìn)行預(yù)測,難以適應(yīng)浮動(dòng)車數(shù)據(jù)的特點(diǎn)�；诟�(dòng)車數(shù)據(jù)的預(yù)測模型,一般選擇忽略缺失數(shù)據(jù),預(yù)測精度較低,不能滿足舒緩交通的需求。為了提高短時(shí)交通狀態(tài)預(yù)測的準(zhǔn)確性,本文在改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型的基礎(chǔ)上,給出基于歷史和實(shí)時(shí)數(shù)據(jù)對訓(xùn)練數(shù)據(jù)進(jìn)行分類補(bǔ)缺的方法,根據(jù)輸入層數(shù)據(jù)的缺失情況,選擇不同的改進(jìn)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。本文選取了行程速度作為表征交通狀態(tài)的參數(shù),針對現(xiàn)有浮動(dòng)車數(shù)據(jù)的特點(diǎn),采用篩選、擬合、補(bǔ)缺以及降噪的預(yù)處理過程,針對補(bǔ)缺處理,給出了基于歷史和實(shí)時(shí)數(shù)據(jù)的K-means分類補(bǔ)缺方法,并對預(yù)處理結(jié)果進(jìn)行了驗(yàn)證。在分析行程速度的時(shí)空相關(guān)性的基礎(chǔ)上,分別基于時(shí)間、空間和時(shí)空維度數(shù)據(jù)對短時(shí)交通狀態(tài)進(jìn)行預(yù)測,以大連市部分出租車的實(shí)際運(yùn)行數(shù)據(jù)作為浮動(dòng)車數(shù)據(jù)對預(yù)測結(jié)果進(jìn)行了驗(yàn)證,給出基于輸入層數(shù)據(jù)缺失的短時(shí)交通狀態(tài)綜合預(yù)測模型。實(shí)驗(yàn)結(jié)果表明,本文給出的模型可以較好地對大連市短時(shí)交通狀態(tài)進(jìn)行估計(jì),具有一定的準(zhǔn)確度和可靠性,實(shí)例驗(yàn)證數(shù)據(jù)結(jié)果基本符合大連市的實(shí)際交通狀況,可以滿足出行者對短時(shí)交通狀態(tài)預(yù)測的需求。本文的研究結(jié)果,對提高城市交通擁堵預(yù)測能力具有一定的理論和實(shí)際應(yīng)用價(jià)值。
[Abstract]:In recent years, with the development of social economy and the rapid growth of the number of motor vehicles, it has brought convenience to life, but also aggravated the degree of road congestion.Due to the limitation of land resources, it is difficult to meet the traffic demand by expanding roads in cities. Therefore, Intelligent Transportation system (its) has become the research hotspot in solving congestion at home and abroad.Short-time traffic state prediction is the key to traffic guidance and control in ITS. The application of short-time traffic state prediction method has achieved some results, but most of the methods are based on fixed detector to predict, so it is difficult to adapt to the characteristics of floating vehicle data.Based on the prediction model of floating vehicle data, the missing data is generally ignored, and the prediction accuracy is low, which can not meet the needs of traffic relief.In order to improve the accuracy of short-term traffic state prediction, based on the improved BP neural network prediction model, this paper presents a method of classifying and filling the training data based on historical and real-time data, according to the lack of input layer data.Different improved BP neural network prediction models are selected.In this paper, the travel speed is selected as the parameter to represent the traffic state. According to the characteristics of the existing floating vehicle data, the pre-processing process of screening, fitting, filling and noise reduction is adopted.The method of K-means classification and filling based on historical and real time data is presented, and the preprocessing results are verified.On the basis of analyzing the temporal and spatial correlation of travel speed, the short-time traffic state is predicted based on time, space and space-time dimension data, respectively.The actual operation data of some taxis in Dalian are used as floating vehicle data to verify the prediction results, and a comprehensive short-term traffic state prediction model based on the lack of input layer data is presented.The experimental results show that the model presented in this paper can estimate the traffic state of Dalian in a short time, and has certain accuracy and reliability.It can meet the demand of travelers for short-time traffic state prediction.The results of this paper have certain theoretical and practical application value to improve the ability of urban traffic congestion prediction.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;TP183

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本文編號(hào)：1757066