大都市郊区居民通勤特征及空间分布研究

发布时间：2018-05-25 14:13

  本文选题：大都市郊区 + 通勤交通　； 参考：《南京林业大学》2015年硕士论文

【摘要】：目前大都市城市形态已经由传统的单中心模式逐步发展为区域化的网络节点成长模式,这种新的发展趋势导致城市周边广大郊区的土地利用发生了根本性的变化;国内目前尚不明确大都市郊区通勤的具体变化规律,而该规律对区域城市规划、城际交通、轨道交通规划具有重要参考依据。本文以大都市郊区居民通勤特征及空间分布研究为中心,以大都市郊区轨道交通影响区域及干线公路影响区典型样本区居民的通勤特征为切入点,采用南京市2012年居民OD调查数据为基础数据。本文首先阐述了通勤、可达性、职住平衡等基本概念,进一步介绍郊区通勤空间分布、通勤特征与结构、通勤行为与影响因素等理论,结合本文研究引入通勤矢量的概念,提出通勤矢量包含通勤时间、距离、效率及方向四个维度。通过因子分析与K-means聚类分析,将南京市郊区居民结合其个人属性、社会经济属性及家庭属性等进行聚类划分,发现将通勤人群划分为四类较为合理,不同通勤群类具有各自的通勤矢量特性;从通勤矢量四个维度对轨道交通及干线公路影响区域典型居民区进行分类及对比分析,探讨不同居住区位的通勤人群在通勤矢量属性上的差异性;由于居民自身属性及居住需求的不同最终形成不同人群的居住区位,同时通勤矢量属性的分异特征也被逐步强化。通过交叉对比分析发现轨道沿线与干线公路沿线样本区中不同类别人群分布呈现明显差异性,轨道交通沿线Ⅲ、Ⅳ类人群为主,干线公路沿线以Ⅰ、Ⅱ类人群为主。最后对大都市郊区居民居住区位与通勤交通方式选择进行建模,基于不同通勤群类选择效用最大化,建立非集计模型。假设就业区位一致的情况下,居住区位在10-20km与20km圈层内,轨道交通与公交影响区域,两种交通方式较其他通勤方式表现出较大优势;不同之处在于,轨道交通出行在干线公路影响区域随着通勤距离增加,通勤时间弹性变换更为敏感;而在长距离通勤出行中,公交与小汽车通勤出行表现出较大差异,前者对通勤时间弹性的变化更为敏感,而后者在两种情况下表现较为一致,且随着通勤距离的增加对时间弹性的敏感度表现不明显;但在干线公路沿线,随着通勤距离的增加,轨道交通通勤出行的时间弹性变化显著增加,在20km区域时,轨道交通对通勤者居住区位选择及通勤出行上吸引力最大。
[Abstract]:At present, the form of metropolitan cities has gradually developed from the traditional single-center mode to the regional network node growth model, which leads to the fundamental changes of the land use in the suburbs around the city. At present, it is not clear in our country that the specific changing law of commuting in the suburbs of metropolis, and this law has important reference basis for regional urban planning, intercity transportation and rail transit planning. This paper focuses on the study of the commuting characteristics and spatial distribution of the residents in the suburbs of metropolis, and takes the commuting characteristics of the typical sample areas of the affected areas of the urban rail transit and the trunk roads as the breakthrough point. Nanjing 2012 residents using OD survey data as the basic data. In this paper, the basic concepts of commuting, reachability and work-accommodation balance are introduced, and the theory of suburban commuting space distribution, commuting characteristics and structure, commuting behavior and influencing factors are introduced, and the concept of commuting vector is introduced in this paper. The commuting vector includes four dimensions: commuting time, distance, efficiency and direction. Through factor analysis and K-means cluster analysis, this paper classifies the suburban residents of Nanjing with their personal attributes, social and economic attributes and family attributes, and finds that it is reasonable to divide commute people into four categories. Different commuting groups have their own characteristics of commuting vector, and classify and compare the typical residential areas affected by rail transit and trunk highway from four dimensions of commuting vector. This paper discusses the differences of commuting vector attributes in different residential areas. Because of the difference of residents' own attributes and residential needs, the residential areas of different populations are formed, and the differentiation characteristics of commuting vector attributes are gradually strengthened. It is found that the distribution of different groups of people in the sample areas along the track and along the trunk road is obviously different, the type 鈪，

本文编号：1933457