本文選題：短時預測 + 交通狀態(tài)�。� 參考：《大連海事大學》2015年碩士論文

【摘要】：近年來,隨著社會經(jīng)濟的發(fā)展,機動車數(shù)量的飛速增長,為生活帶來了交通上的便捷,但同時也加重了道路擁堵程度。由于受土地資源等限制,城市中難以通過擴建道路滿足通行需求,因此,智能交通系統(tǒng)成為國內外解決擁堵的研究熱點。短時交通狀態(tài)預測是ITS中交通誘導和控制的關鍵,短時交通狀態(tài)預測方法的應用,取得了一定的效果,但大部分方法是基于固定檢測器進行預測,難以適應浮動車數(shù)據(jù)的特點。基于浮動車數(shù)據(jù)的預測模型,一般選擇忽略缺失數(shù)據(jù),預測精度較低,不能滿足舒緩交通的需求。為了提高短時交通狀態(tài)預測的準確性,本文在改進的BP神經(jīng)網(wǎng)絡預測模型的基礎上,給出基于歷史和實時數(shù)據(jù)對訓練數(shù)據(jù)進行分類補缺的方法,根據(jù)輸入層數(shù)據(jù)的缺失情況,選擇不同的改進BP神經(jīng)網(wǎng)絡預測模型。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)有浮動車數(shù)據(jù)的特點,采用篩選、擬合、補缺以及降噪的預處理過程,針對補缺處理,給出了基于歷史和實時數(shù)據(jù)的K-means分類補缺方法,并對預處理結果進行了驗證。在分析行程速度的時空相關性的基礎上,分別基于時間、空間和時空維度數(shù)據(jù)對短時交通狀態(tài)進行預測,以大連市部分出租車的實際運行數(shù)據(jù)作為浮動車數(shù)據(jù)對預測結果進行了驗證,給出基于輸入層數(shù)據(jù)缺失的短時交通狀態(tài)綜合預測模型。實驗結果表明,本文給出的模型可以較好地對大連市短時交通狀態(tài)進行估計,具有一定的準確度和可靠性,實例驗證數(shù)據(jù)結果基本符合大連市的實際交通狀況,可以滿足出行者對短時交通狀態(tài)預測的需求。本文的研究結果,對提高城市交通擁堵預測能力具有一定的理論和實際應用價值。
[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.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP183

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本文編號：1757066