【摘要】：在經(jīng)濟(jì)迅猛增長(zhǎng)的今天，城市交通運(yùn)行態(tài)勢(shì)日趨嚴(yán)峻的同時(shí)高速公路從原來的解決制約經(jīng)濟(jì)運(yùn)輸發(fā)展的助力，已經(jīng)開始演變成又一個(gè)亟需緩解的問題。 伴隨著我國(guó)的汽車保有量逐年大幅度的上升，我國(guó)的許多高速公路上也相繼產(chǎn)生了交通擁堵的問題并有加劇的趨勢(shì)，整個(gè)高速公路路網(wǎng)急速駛?cè)搿皳矶聲r(shí)代”。針對(duì)以上現(xiàn)象，本文從交通運(yùn)行狀態(tài)含義而提出高速公路運(yùn)行狀態(tài)體系，從基于對(duì)于高速公路的環(huán)境來具體分析高速公路運(yùn)行狀態(tài)體系的需求出發(fā)，進(jìn)而建立運(yùn)行狀態(tài)體系。本文從設(shè)計(jì)原則到指標(biāo)選取，從體系的搭建到狀態(tài)指標(biāo)的量化進(jìn)行了詳細(xì)的論述。 在對(duì)交通狀態(tài)指標(biāo)體系架構(gòu)分析的基礎(chǔ)上，本文確立了由區(qū)間交通參數(shù)決定的交通擁擠評(píng)價(jià)標(biāo)準(zhǔn)和由地點(diǎn)交通參數(shù)決定的交通擁擠評(píng)價(jià)標(biāo)準(zhǔn)，針對(duì)高速公路中這個(gè)特殊交通運(yùn)行環(huán)境提出交通運(yùn)行狀態(tài)判別的算法。算法結(jié)合交通擁擠評(píng)價(jià)標(biāo)準(zhǔn)的要求，評(píng)判模型由地點(diǎn)參數(shù)決定的判別、由區(qū)間參數(shù)決定的判別和由數(shù)據(jù)融合決定的判別聯(lián)合構(gòu)成。研究分別對(duì)各方法進(jìn)行了細(xì)致的闡述，最終達(dá)到可靠分析交通狀態(tài)，，合理監(jiān)控、分析并實(shí)施預(yù)警，實(shí)現(xiàn)交通管理服務(wù)的目的。 同時(shí)本文分析了交通緊急事件檢測(cè)的重要作用，從高速公路交通事件檢測(cè)的基本原理剖析，論述了高速公路交通事件的影響因素，概述了自動(dòng)檢測(cè)交通事件經(jīng)典算法，并以此提出了基于支持向量機(jī)的高速公路交通緊急事件判別算法，接著對(duì)支持向量機(jī)參數(shù)進(jìn)行最優(yōu)值尋找，又引入改進(jìn)的粒子群算法，提出了基于改進(jìn)的粒子群-支持向量機(jī)的高速公路交通緊急事件判別模型。通過基于網(wǎng)絡(luò)搜索的參數(shù)尋優(yōu)實(shí)驗(yàn)、基于基本粒子群的參數(shù)尋優(yōu)實(shí)驗(yàn)和基于改進(jìn)粒子群的參數(shù)尋優(yōu)實(shí)驗(yàn)，驗(yàn)證了本文設(shè)計(jì)算法的性能。
[Abstract]:With the rapid growth of economy, the situation of urban traffic operation is becoming more and more severe. Meanwhile, the expressway has become another problem that needs to be alleviated from the original solution to restrict the development of economic transportation. With the increase of the number of cars in our country, the traffic congestion on many expressways in our country has also been increased, and the whole highway network has rapidly entered the "congestion era". In view of the above phenomenon, this paper puts forward the expressway running state system from the meaning of the traffic running state, and starts from the concrete analysis of the expressway running state system based on the environment of the expressway, and then establishes the running state system. This paper discusses in detail from design principle to index selection, from system construction to quantification of state index. Based on the analysis of traffic state index system, this paper establishes the evaluation criteria of traffic congestion determined by interval traffic parameters and traffic congestion evaluation standards determined by location traffic parameters. This paper presents an algorithm for judging the traffic running state in this special traffic environment in expressway. The algorithm combines with the requirements of traffic congestion evaluation standard, and the judgment model is composed of location parameters, interval parameters and data fusion. The methods are elaborated in detail, and the traffic condition is analyzed reliably, the traffic condition is monitored reasonably, the early warning is analyzed and implemented, and the purpose of traffic management and service is realized. At the same time, this paper analyzes the important role of traffic emergency detection, analyzes the basic principle of expressway traffic incident detection, discusses the influencing factors of expressway traffic incident, and summarizes the classical algorithm of automatic traffic event detection. Based on support vector machine (SVM), a highway traffic emergency identification algorithm is proposed, and then the optimal value of support vector machine parameters is found, and an improved particle swarm optimization algorithm is introduced. An improved particle swarm optimization (PSO)-support vector machine (SVM) based expressway traffic emergency discrimination model is proposed. The performance of the proposed algorithm is verified by the experiments of parameter optimization based on network search, parameter optimization based on basic particle swarm optimization and parameter optimization based on improved particle swarm optimization.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491

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