本文選題：通勤出行　切入點(diǎn)：通勤方式選擇　出處：《南京大學(xué)》2017年碩士論文

【摘要】：通勤出行是城市居民最主要的出行,同時(shí)也是最重要的出行,它對(duì)城市的日常交通狀況具有決定性的影響,城市交通擁堵表現(xiàn)最顯著的是通勤時(shí)段擁堵。受限于城市交通資源,擴(kuò)大道路供給、提升交通服務(wù)質(zhì)量不能作為永久性措施。從通勤者個(gè)體選擇行為自身特點(diǎn)和偏好出發(fā),另辟新法,是近年來(lái)交通出行行為研究領(lǐng)域的新思路。離散選擇模型是研究通勤者路徑選擇和方式選擇的經(jīng)典方式,它的發(fā)展歷程也隨著人們的需求而不斷被改進(jìn),其中混合Logit模型是可以根據(jù)個(gè)體的隨機(jī)喜好因素來(lái)預(yù)測(cè)其選擇方式。而本文重點(diǎn)關(guān)注的潛在類別模型則是對(duì)不可測(cè)因素的進(jìn)一步研究,而且它還在很大程度上做到控制變量,剔除相互干擾因素,用潛在變量衡量需考察的外顯變量之間的聯(lián)系,在對(duì)一個(gè)群體做出新的分類的同時(shí),也能探求得這個(gè)群體在人群中的比重大小。有利于我們?cè)谶M(jìn)一步研究課題、引導(dǎo)出行者的時(shí)候有的放矢。潛在類別模型方法是對(duì)通勤出行者出行行為選擇異質(zhì)性研究的重要補(bǔ)充。本文具體的研究?jī)?nèi)容和主要結(jié)論如下:首先,通過(guò)對(duì)通勤出行的重新認(rèn)識(shí),了解城市通勤出行的空間特點(diǎn)、強(qiáng)度特點(diǎn)、目的特點(diǎn)。其次,基于傳統(tǒng)模型的出行行為選擇方式研究領(lǐng)域的成果,認(rèn)識(shí)到在很多情形之下,增修道路、高架和隧道等方法只是短期內(nèi)有明顯效果。接下來(lái)介紹并分析潛在類別模型的數(shù)學(xué)原理,通過(guò)幾個(gè)基本算例來(lái)區(qū)分與既有分類的方式細(xì)分人群的差別。最后,通過(guò)陳述性調(diào)查法收集數(shù)據(jù),運(yùn)用潛在類別模型對(duì)調(diào)查人群進(jìn)行分類,得到每項(xiàng)屬性的參數(shù)估計(jì)和影響因子系數(shù),并給每個(gè)類別命名,分析其類別特點(diǎn)。本文通過(guò)對(duì)經(jīng)濟(jì)水平、天氣狀況、通勤距離、通勤花費(fèi)、出勤緊要、舒適度、身份認(rèn)同感這七項(xiàng)屬性被選擇來(lái)做陳述性調(diào)查,每項(xiàng)屬性選擇的分為三個(gè)水平,通過(guò)LatentGold軟件分析收集到的數(shù)據(jù),將被試分成三個(gè)潛在類別,潛在類別概率分別是0.5487,0.3914,0.0599,根據(jù)其數(shù)據(jù)分布和結(jié)構(gòu)特點(diǎn),分別將它們命名為:"通勤花費(fèi)敏感型"、"看重工作型"、"注重體驗(yàn)型",并得出個(gè)類別下的條件概率估計(jì)值。最后對(duì)每一個(gè)類別下的選擇異質(zhì)性進(jìn)行分析。
[Abstract]:Commuting is not only the most important trip for urban residents, but also the most important travel. It has a decisive impact on the daily traffic conditions of the city. The most significant performance of urban traffic congestion is the traffic congestion during commuting hours, which is limited by urban traffic resources. Expanding the supply of roads and improving the quality of transportation services cannot be taken as a permanent measure. The discrete choice model is a classical way to study the path choice and mode choice of commuters, and its development process is improved with the demand of people. The hybrid Logit model can be used to predict the selection pattern according to the individual's random preferences, while the potential category model, which is focused on the further study of unmeasurable factors, to a large extent, controls variables. The association between explicit variables that need to be examined is measured by potential variables, and a new classification of a population is made at the same time. We can also find out the proportion of this group in the crowd, which is conducive to our further study of the subject. The potential class model method is an important supplement to the study on the heterogeneity of commuter travel behavior. The specific contents and main conclusions of this paper are as follows: first of all, Through a new understanding of commuting trip, we can understand the spatial characteristics, intensity characteristics, purpose characteristics of urban commuting trip. Secondly, the achievements in the field of travel behavior selection based on traditional model are realized, and it is recognized that, in many cases, The methods of adding roads, elevated structures and tunnels have obvious effect only in the short term. Then introduce and analyze the mathematical principle of the model of potential categories, and use several basic examples to distinguish the difference between the existing classification and the subdivision of the population. Finally, Collecting data by declarative survey method, classifying the surveyed population by using the potential category model, obtaining the parameter estimation and influence factor coefficient of each attribute, naming each category, analyzing the characteristics of each category. Weather conditions, commuting distance, commuting costs, attendance matters, comfort, identity were selected to do declarative surveys, each attribute selected was divided into three levels, the data collected through LatentGold software analysis, The subjects were divided into three potential categories. The probability of potential categories was 0.54870.39140.5999. According to their data distribution and structural characteristics, They are named as "commute cost sensitive type", "emphasis on work type", "emphasis on experience type", and the conditional probability estimates under each category are obtained. Finally, the selection heterogeneity under each category is analyzed.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F572

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