【摘要】：交通信息采集是智能交通系統(tǒng)的核心子系統(tǒng),是交通應(yīng)用的基礎(chǔ)。通過先進(jìn)的信息技術(shù)采集更高時空精度的交通流數(shù)據(jù),并結(jié)合微觀信號控制系統(tǒng)進(jìn)行控制和誘導(dǎo),均衡交通流在路網(wǎng)上的時空分布,是解決城市交通擁堵問題的關(guān)鍵。傳統(tǒng)的感應(yīng)線圈等交通監(jiān)督技術(shù)只能檢測固定點(diǎn)數(shù)據(jù),實(shí)際應(yīng)用中一般僅部署在干道的主要交叉口,路網(wǎng)上存在大量的信息“真空”,無法全面地感知交通流的動態(tài)變化,限制了信號控制系統(tǒng)的優(yōu)化能力。近年來,移動互聯(lián)網(wǎng)、傳感網(wǎng)、車聯(lián)網(wǎng)等新一代信息技術(shù)不斷涌現(xiàn),如果這些網(wǎng)絡(luò)產(chǎn)生的數(shù)據(jù)與智能交通系統(tǒng)連接起來,將會為交通信息采集開辟新的技術(shù)途徑。研究一種精度高、實(shí)時性好、維護(hù)成本低、適應(yīng)大數(shù)據(jù)時代的交通信息采集技術(shù),具有十分重要的意義。本文以城市交通大數(shù)據(jù)為背景,研究了城市交通信息采集中的一些優(yōu)化問題。論文的創(chuàng)新性工作包括以下幾個方面。第一,在單點(diǎn)數(shù)據(jù)采集方面,研究了基于無線傳感器網(wǎng)絡(luò)的交通流參數(shù)采集優(yōu)化模型和算法。無線傳感器網(wǎng)絡(luò)可以進(jìn)行大規(guī)模部署,在智能交通系統(tǒng)中具有很好的應(yīng)用前景。本文在伯克利大學(xué)P. Varaiya等人提出的自適應(yīng)閾值檢測算法的基礎(chǔ)上,針對閾值更新緩慢及分類算法未考慮車輛長度等問題,提出了一種基于信號相關(guān)性的車輛速度測量算法和一種基于鄰接傳感器網(wǎng)絡(luò)的車輛分類算法。提高了車輛速度估計(jì)和車輛分類的精度,并且在閡值漂移、疊加干擾等條件下也具有較好的精度和魯棒性。第二,研究了群體參與式感知在交通信息采集中的應(yīng)用,提出了可以采集路段交通流數(shù)據(jù)的拉格朗日感知算法。該方法利用傳感器數(shù)據(jù)來求解交通方程對交通流的內(nèi)在的運(yùn)行規(guī)律進(jìn)行預(yù)測,同時使用參與式感知數(shù)據(jù)作為觀測值,基于卡爾曼濾波算法綜合交通方程和實(shí)際觀測數(shù)據(jù)對交通流參數(shù)進(jìn)行最優(yōu)估計(jì),獲取連續(xù)的、具有更高時空精度的交通流數(shù)據(jù)。在此基礎(chǔ)上,提出了道路的堵塞因子,對交通擁堵狀況進(jìn)行實(shí)時度量,并應(yīng)用到路口交通信號配時優(yōu)化場景中,結(jié)合粒子群優(yōu)化對信號相序進(jìn)行優(yōu)化,達(dá)到避免交通擁堵形成的目的。第三,研究了參與式感知中的數(shù)據(jù)集選擇優(yōu)化問題。已有研究成果表明,相對于數(shù)據(jù)的數(shù)量,提供的數(shù)據(jù)所在的位置對于交通流估計(jì)的結(jié)果有更大的影響。在大規(guī)模的城市路網(wǎng)中,參與式感知的數(shù)據(jù)體量非常巨大,如何在大量數(shù)據(jù)中區(qū)分出數(shù)據(jù)價值、選擇最優(yōu)數(shù)據(jù)集是一個重要的問題。本文研究了給定傳感器可選位置條件下的數(shù)據(jù)集選擇優(yōu)化問題,采用互信息熵作為目標(biāo)函數(shù)、以均方根誤差為約束條件建立了傳感器數(shù)據(jù)集選擇的多目標(biāo)優(yōu)化模型,提出了一種基于貝葉斯優(yōu)化解決傳感器數(shù)據(jù)集序貫選擇的算法。第四,針對基于車聯(lián)網(wǎng)和車載終端的參與式感知中傳感器節(jié)點(diǎn)隨著交通流移動的特征,研究了交通流變化及網(wǎng)絡(luò)的拓?fù)鋾r變帶來的動態(tài)不確定性。本文采用時變網(wǎng)絡(luò)模型對移動傳感器網(wǎng)絡(luò)的動態(tài)拓?fù)浼皵?shù)據(jù)價值的時變不確定性進(jìn)行建模,基于傳感器節(jié)點(diǎn)的數(shù)據(jù)效用定義了時變價值網(wǎng)絡(luò),并采用蟻群優(yōu)化進(jìn)行傳感器數(shù)據(jù)集的并行優(yōu)化選擇。此外,針對傳感器節(jié)點(diǎn)的移動性和交通流數(shù)據(jù)的時變特性,提出一種基于Internet的傳輸控制協(xié)議,使控制節(jié)點(diǎn)可以感知交通流模式變化并選擇最優(yōu)價值的數(shù)據(jù),同時對傳感器節(jié)點(diǎn)的數(shù)據(jù)傳輸進(jìn)行反饋和控制優(yōu)化。
[Abstract]:Traffic information collection is the core subsystem of the intelligent transportation system, and it is the foundation of traffic application. It is the key to solve the traffic congestion problem by collecting the traffic flow data with higher temporal and spatial accuracy through advanced information technology and combining the micro signal control system to control and induce the traffic flow on the road network. Traffic supervision technology such as induction coil, such as induction coil, can only detect fixed point data. In practical application, it is generally only deployed at main intersection of the main road. There is a lot of information "vacuum" on the road network. It can not fully perceive the dynamic changes of traffic flow, and limit the optimization ability of the signal control system. In recent years, mobile Internet, sensor network, car Federation The new generation of information technology, such as network, is constantly emerging. If the data generated by these networks are connected with the intelligent transportation system, it will open up a new technical way for the traffic information collection. It is of great significance to study a kind of traffic information collection technology with high precision, good real-time, low maintenance cost and adapt to the age of large data. This paper is based on the city. In the background of city traffic data, some optimization problems in urban traffic information collection are studied. The innovative work of this paper includes the following aspects. First, in the single point of data acquisition, the optimization model and calculation method of traffic flow parameter acquisition based on wireless sensor network is studied. In this paper, based on the adaptive threshold detection algorithm proposed by P. Varaiya and others in Berkeley University, this paper proposes a vehicle speed measurement algorithm based on signal correlation and a neighborhood based on the slow updating of threshold and the length of the vehicle. The vehicle classification algorithm of sensor network improves the accuracy of vehicle speed estimation and vehicle classification, and has good accuracy and robustness under the conditions of threshold drift and superposition interference. Second, the application of group participatory perception in traffic information collection is studied, and rag Lang, which can collect traffic flow data of sections, is proposed. The method uses the sensor data to predict the internal running rules of traffic flow by using the sensor data, and uses the participatory perception data as the observation value. Based on the Calman filtering algorithm, the traffic flow equation and the actual observation data are optimized to obtain the continuous and higher time. The traffic flow data of empty precision. On this basis, the congestion factor of the road is proposed, and the traffic congestion is measured in real time. In the optimization scene of traffic signal timing in the intersection, combined with particle swarm optimization to optimize the phase sequence of the signal, the purpose of avoiding traffic congestion is achieved. Third, the data in the participatory perception are studied. The existing research results show that the location of the data provided has a greater impact on the results of traffic flow estimation than the number of data. In the large-scale urban road network, the participatory data volume is very large. How to distinguish the value of data in a large number of data and select the best data set is one. An important problem. In this paper, the selection and optimization of data sets in the optional position of a given sensor is studied. Using the mutual information entropy as the objective function and the mean square root error as the constraint condition, a multi-objective optimization model for the selection of sensor data sets is established. A new method is proposed based on Bayesian optimization to solve the sequential selection of sensor data sets. Fourth, in view of the characteristics of the sensor nodes in the participatory perception of vehicle networking and vehicle terminal, the dynamic uncertainty caused by the change of traffic flow and the time-varying topology of the network is studied. In this paper, the time-varying network model is used to change the dynamic topology and the data value of the mobile sensor network. The time-varying value network is defined based on the data utility of sensor nodes, and the parallel optimization selection of sensor data sets is carried out by ant colony optimization. In addition, a transmission control protocol based on Internet is proposed to enable the control nodes to be aware of the mobility of sensor nodes and the time-varying characteristics of traffic flow data. The traffic flow pattern changes and selects the best value data, and feedback and control optimization for data transmission of sensor nodes.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：U495;TP212.9;TN929.5

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