本文選題：高速公路 + 清障救援　； 參考：《交通運(yùn)輸部公路科學(xué)研究所》2017年碩士論文

【摘要】：隨著我國(guó)高速公路里程與機(jī)動(dòng)車保有量的迅速增長(zhǎng),使得高速公路的安全、高效運(yùn)營(yíng)的保障任務(wù)日益艱巨。作為高速公路“保通保暢”工作實(shí)施的重要基礎(chǔ)條件,清障救援資源對(duì)減少高速公路交通事故所造成生命財(cái)產(chǎn)損失、提高路網(wǎng)通行效率等方面發(fā)揮著重要作用。針對(duì)清障救援資源的配置管理使用,合理地預(yù)測(cè)需求是進(jìn)行后續(xù)資源配置調(diào)度等工作的前提條件。研究高速公路清障救援資源配置需求預(yù)測(cè)問題具有重要的理論與實(shí)際意義。通過介紹案例推理方法理論框架并分析其應(yīng)用于應(yīng)急救援領(lǐng)域的優(yōu)勢(shì),將案例推理引入清障救援資源需求預(yù)測(cè)。通過分析常用知識(shí)表達(dá)方法,提出資源預(yù)測(cè)案例的二元組框架表示方法。從道路路線、路基路面等方面分析高速公路交通事故影響因素,構(gòu)建包含道路長(zhǎng)度等要素在內(nèi)的案例特征屬性空間。根據(jù)目前救援資源實(shí)際使用狀況及裝備參數(shù)統(tǒng)計(jì)數(shù)據(jù),形成案例數(shù)量結(jié)果的分類型分能力表示方法。針對(duì)案例特征屬性空間構(gòu)建過程中可能存在的冗余信息,依據(jù)特征選擇過程一般過程及搜索策略,在闡述隨機(jī)森林方法原理、構(gòu)建過程、關(guān)鍵參數(shù)、應(yīng)用優(yōu)勢(shì)的基礎(chǔ)上,應(yīng)用隨機(jī)森林方法袋外誤差降低率衡量特征重要性程度,采取后向搜索策略對(duì)案例特征進(jìn)行選擇�；趯�(shí)際數(shù)據(jù)給出了特征算法的實(shí)例應(yīng)用過程,確定包含了道路長(zhǎng)度,交通量等關(guān)鍵因素的清障救援車輛數(shù)量預(yù)測(cè)特征集合。通過對(duì)傳統(tǒng)案例檢索方法的優(yōu)缺點(diǎn)分析,提出結(jié)合徑向基神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)案例檢索以提高檢索過程效率。通過介紹徑向基神經(jīng)網(wǎng)絡(luò)一般原理及常用學(xué)習(xí)方法,針對(duì)徑向基網(wǎng)絡(luò)隱含層難以確定的問題,設(shè)計(jì)了利用動(dòng)態(tài)衰減方法改進(jìn)徑向基網(wǎng)絡(luò)學(xué)習(xí)方法,并經(jīng)過實(shí)例數(shù)據(jù)驗(yàn)證改進(jìn)后的徑向基神經(jīng)網(wǎng)絡(luò)檢索能在保證檢索精度的情況下有效提高學(xué)習(xí)的速度。
[Abstract]:With the rapid growth of highway mileage and the number of motor vehicles in our country, the task of ensuring highway safety and efficient operation is becoming increasingly arduous. As an important basic condition for the implementation of the expressway "Baotong Baochang", the obstacle clearing and rescue resources play an important role in reducing the loss of life and property caused by the expressway traffic accident and improving the efficiency of the road network. In view of the allocation management and use of obstacle clearing and rescue resources, reasonable prediction of demand is the prerequisite for the subsequent resource allocation and scheduling. It is of great theoretical and practical significance to study the demand prediction of freeway rescue resource allocation. By introducing the framework of Case-Based reasoning (CBR) and analyzing its advantages in the field of emergency rescue, Case-Based reasoning (CBR) is introduced to predict the resource demand of obstacle clearing and rescue. Based on the analysis of common knowledge representation methods, a binary group framework representation method for resource prediction cases is proposed. This paper analyzes the influence factors of expressway traffic accidents from the aspects of road route, roadbed and pavement, and constructs the case characteristic attribute space including road length and other factors. According to the statistical data of the actual use of rescue resources and the equipment parameters at present, a method of expressing the ability of classification and classification of the results of the number of cases is formed. According to the general process of feature selection and the search strategy, the principle, construction process, key parameters and application advantages of stochastic forest method are expounded, based on the redundant information that may exist in the process of constructing case feature attribute space, based on the general process of feature selection and the search strategy. The importance of feature is measured by the reduction rate of out-of-bag error of stochastic forest method, and the case feature is selected by backward search strategy. Based on the actual data, the application process of the feature algorithm is given, and the prediction feature set of the number of obstacle clearing and rescue vehicles including road length, traffic volume and other key factors is determined. Based on the analysis of the advantages and disadvantages of the traditional case retrieval methods, a learning case retrieval method combined with radial basis function neural network (RBF) was proposed to improve the efficiency of the retrieval process. This paper introduces the general principle and common learning methods of radial basis function neural network, aiming at the problem that the hidden layer of radial basis function network is difficult to be determined, a dynamic attenuation method is designed to improve the learning method of radial basis function network. The improved radial basis function neural network retrieval method can effectively improve the learning speed under the condition of guaranteeing the retrieval accuracy.
【學(xué)位授予單位】：交通運(yùn)輸部公路科學(xué)研究所
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U492.8

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