引用检索 快速检索 DOI 高级检索
广州市居民工作日小汽车出行个体与社区双层影响机制
周素红1,2,, 宋江宇1,2, 宋广文1,2
1. 中山大学地理科学与规划学院,广州 510275
2. 广东省城市化与地理环境空间模拟重点实验室,广州 510275

作者简介:周素红(1976-), 女, 博士, 教授, 主要研究方向城市地理学、时空间行为和城乡规划。E-mail:eeszsh@mail.sysu.edu.cn

摘要

小汽车出行与建成环境的关系是城市研究中的热点问题,已有的研究主要分别关注个人社会经济属性与建成环境对小汽车出行的影响。近年来,在中国城市转型的背景下,社会空间分异受到关注,越来越多的研究表明,居民个人社会经济属性与社区建成环境及社区类型间存在关系,考虑三者如何共同对小汽车出行的影响具有一定的现实意义。因此,本文构建包含个体层社会经济属性和社区层建成环境与社区类型的多层Logit模型对工作日小汽车出行方式的影响因素进行分析。研究发现居民个人的社会经济属性和其所居住社区建成环境同时对小汽车出行的选择起作用,且这两层因素的影响与社区类型紧密相关,出行方式差异有76.32%是由社区因素的差异造成。个人社会经济属性因素方面,更高的文化水平、更高的个人月收入水平、非集体单位性质和家庭存在更多未成年孩子的居民工作日小汽车使用率较高;社区建成环境因素方面,提高建筑密度、用地混合度、商业可达性和公交站点密度会降低社区居民工作日出行使用小汽车的可能,而社区周边更高的POI密度则会增多居民对小汽车的使用。两类因素关系背后的机制在于伴随着市场力量对城市居住空间重构影响的增强,相似社会经济属性的居民会倾向选择同一类型社区,而相同类型社区拥有相似的建成环境。这些结论有助于进一步了解城市居民工作日小汽车出行背后的影响因素及机制,并为通过对相同类型社区多层因素的调节来改变居民出行方式结构,进而缓解交通问题提供了借鉴。

关键词: 小汽车出行; 建成环境; 多层Logit模型; 社区类型; 广州;
Examining the dual-levels impact of neighbourhood and individual variables on car use on weekdays in Guangzhou
ZHOU Suhong1,2,, SONG Jiangyu1,2, SONG Guangwen1,2
1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
Abstract

The relationship between car travel and built environment is one of the hot issues in the urban studies. The existing researches mainly focus on the respective effects on car travel of individual demographic variables and built environment variables on car travel. Under the background of China's urban transformation and the social space differentiation, a growing number of scholars have examined the relationship between residents' demographics and their residential built environment. Quantifying the relative roles of the individual social attributes and the built environment in influencing car travel has a policy implication. This study applied a multilevel logit model which contains individual-level variables and neighbourhood-level variables to explore the impact on car use. It is concluded that urban residents' working-day car travel is influenced by multilevel variables associated to neighbourhood types. Most of the variation in the travel mode choice is caused by the difference among neighbourhoods. Travel mode choices have a strong neighbourhood contextual effect on car travel. As for the individual level, lower income and education level, collective unit profession, less minors in a family may help refrain the choice of automobile travel. As for the community level, improving bus stops density, building density, land mixing degree and commercial accessibility may help reduce car use ratio of working-day activities' travel. The mechanism of the relationship between two kinds of variables lies in the strength of market effect on urban residential space reconstruction. The residents who have similar social and economic attributes tend to choose the same type of neighbourhood, which has a similar built environment. These conclusions help us to have a better understanding of the mechanism behind the urban residents' working-day car travel and provide suggestions to alleviate the traffic problem by adjusting the multilevel variables in the similar type of community.

Keyword: car travel; built environment; multilevel logit model; neighbourhood type; Guangzhou;
1 引言

随着中国大城市规模的扩大、空间的重构、以及居民经济收入的不断攀升,小汽车保有量呈现高速的增长趋势。2012年末,广州拥有汽车总量达到269.5万辆,全市民用汽车保有量达到204.16万辆,比上年末增长9.9%[1]。小汽车保有量的快速增长导致了城市交通拥挤、环境污染等一系列问题,严重挑战城市的可持续发展,也影响城市居民的生活质量。因此,深入探讨城市居民选择小汽车出行的影响因素和机制,将为优化交通规划和管理提供科学的支持。

城市居民出行与建成环境的关系是城市研究中的重要领域,以往的研究更多关注城市宏观层面因素与居民日常出行的关系[2-3],普遍认为更紧凑的发展模式使得城市内部通勤距离缩短,有利于降低居民出行对小汽车的依赖[4];城市空间结构,特别是居住与就业空间的关系对居民出行方式选择具有一定影响[5-7]。为了进一步为相关规划政策的制定提供参考,减少居民对小汽车出行的依赖,近年来,中微观的城市建成环境以及居民自选择对出行的影响受到重视[8-9],小汽车出行的影响因素是本领域讨论的重点之一。

总体上,对于小汽车出行影响因素的研究主要存在两方面争论。一部分学者认为,城市居民出行方式受出行者及其家庭社会经济因素的影响。个人社会经济属性决定了出行者对交通方式选择的偏好和支付能力[10-11],不同生命周期影响个人出行方式[12]。研究关注女性、老年人以及低收入群体等特定群体在出行方面受到的机会剥夺和空间限制[13-17];同时出行者所在家庭的社会经济属性对个人产生约束,影响出行方式的选择[18-19]。其他学者则认为居民出行方式受建成环境的影响。由于建成环境中诸如容积率、建筑密度、设施可达性等可作为城市规划管理中的可控因素,研究其与小汽车出行的关系,可作为调节交通的有效手段,近年来逐渐受到关注[9, 20],基于此,已有研究通常从密度(density)、混合度(diversity)、设计(design)、公交邻近度(distance to transit)和目的可达性(destination accessibility)等5个方面归纳描述建成环境[8, 21-23]。一些研究发现,居住地人口密度越高,居民选择小汽车通勤方式的可能性减小[24],居住地不同的土地混合程度以及街道十字路口比重等因素也会对小汽车出行的选择造成影响[25-26]

然而,建成环境与城市居民的社会经济属性是否同时对居民出行方式的选择产生影响?两方面因素之间是否存在互动关系?“自选择”理论从一定程度上表明具有特定社会经济属性的城市居民拥有相应的出行偏好,其在选择住房时会更多考虑具有能够满足其偏好的建成环境的居住社区[27-28]。但已有研究表明,在中国环境下“自选择”并不能够解释观察到的出行行为分异[28-29],建成环境与社区有着较为复杂的关系[30-31]。住房供给系统和居民的可支付能力让居民的住房选择和出行模式选择受到更多约束。伴随着市场化和住房改革的不断推进,社会空间重构下[32-33]城市居民出行方式是否因逐渐自由的居住社区选择以及不同居住社区的建成环境而发生分异?处于相同社区个体间是否会因为居住社区这一重要的地理背景导致出行方式的选择上有较高的趋同性,出现出行方式选择的“背景效应”(contextual effects)?

为回答上述问题,有必要构建适合的方法模型将个体和社区两层因素对小汽车出 行的影响放在同一框架下进行分析。由于影响城市居民小汽车出行的社会经济属性因素是个体层面的非集计数据,而建成环境因素则往往通过社区层面体现,以往研究更多采用传统回归方法控制一方面因素来突出另一方面因素的作用[34-35],割裂了两方面因素的关系,且无法表征小汽车出行的社区分异。加上居民出行数据获取多是建立在分层抽样的方法基础上,不可避免地存在嵌套的多层数据结构,使得传统单一层次估计方法出现偏差[36-37]。多层模型能够度量小汽车出行在社区层面的差异,寻找居民小汽车出行是否存在“背景效应”,并寻求解释个体层小汽车出行的斜率和截距变异的社区层背景变量,得出相应变量对小汽车出行背景效应和社区分异的解释程度,既体现两层变量的互动关系,也关注小汽车出行影响的社区分异及社区内部“邻里效应”(neighborhood effects)。

工作日城市居民出行频繁导致交通问题普遍突出,因此,本文以广州市为例,构建多层Logit模型研究工作日居民小汽车出行的影响因素、不同层因素间的互动关系及背后的机制,对转型背景下中国如何应对小汽车迅速增长的政策制定具有一定的现实意义。

2 数据与方法
2.1 研究区域与数据

研究区域为广州市辖区范围(除增城、从化以外),包括天河区、越秀区、海珠区、番禺区、荔湾区、黄浦区、花都区及白云区等8个区,样本涵盖广州市传统旧城区、城市商业地区、城市郊区地区等18个社区(图1)。为保证样本社区抽样过程的随机性和代表性,通过因子生态分析法对广州进行社会区划分[32],再在不同类型社会区中选择典型样本社区。具体步骤为:首先,以社区居委会为基本单元,选择住房面积、住房月租金、住房产权类型和住户职业类型等反映住房和住户社会经济属性的广州市第六次人口普查中的30个指标,运用主成分分析归纳为8个主因子(购买商品房的机关单位人员,自建住房人员,旧单位社区人员,低租金的非在业人员,月租金较高的其他人员,商业服务业从业人员,保障性住房住户和超大户型的其他住户)。其次,根据主因子得分进行聚类,将广州划分为四类不同的社会区(旧城旧机关类社会区,商业类社会区,高教育旧单位类社会区和郊区类社会区)。最后,在不同类型的社会区中,选择相关主因子得分排序靠前、特征值最突出的社区作为本次问卷调查的抽样调查社区,并在社区内进行随机入户调查(表1)。

表1 研究区域社区类型及特征 Tab. 1 The types and attributes of the study area

本研究基于2013年《城市居民日常出行调查问卷》,调查时间为2013年4月-6月,在全市范围内总计回收有效问卷1604份,每份问卷记录了居民工作日一整天的活动日志。有出行日志并符合本研究的问卷1171份,共得到4055条出行活动记录,每条记录都有个人社会经济属性及相应的出行方式,包括步行、自行车、公共汽车、地铁、轮渡、单位小汽车、私家车、出租车等,和所在社区的建成环境与社区类型。本文所述的小汽车出行包括单位小汽车、私家车和出租车等出行方式,运用其他工具的出行定义为非小汽车出行。

图1 研究区域区位图 Fig. 1 The location of the study area

建成环境变量包括容积率、建筑密度、人口密度、用地混合度、POI密度、商业可达性和公交站点密度等。数据来源为广州市2010年第六次人口普查数据,广州市兴趣点(Point of Interest, POI)数据和广州市土地利用调查数据。建成环境虽有一些调整,但总体相对稳定,且建成环境对小汽车出行具有滞后性影响,因此,采用的建成环境数据满足因果关系作用探讨的合理性。人口密度为社区常住人口除以社区面积;POI密度为社区周边1000 m半径缓冲区内所有兴趣点的密度;公交站点密度为社区周边1000 m半径缓冲区内所有公交车站的密度;商业可达性选取距离社区最近的3个商业中心来衡量,商业网点的选取参考《广州市大型零售商业网点发展规划(2011-2020)》,具体计算公式为:

Center_access = j=1 k 1 k ( 1 d j 2 ) (1)

式中:dj为样本社区到商业中心j的最短路径;k = 3;土地利用混合度代表了社区周边1000 m范围内的商住混合情况,参考已有研究[38],具体计算公式为:

Landuse_mix = ( 1 - u u + T ) × ( 1 - r T - 1 3 + c T - 1 3 + o T - 1 3 4 3 ) T = r + c + o (2)

式中:rcou分别为社区周边半径1000 m范围内居住用地、商业金融用地、其他建设用地和非建设用地的面积;Landuse_mix值为0代表社区周边1000 m范围只有一种土地利用功能,值为1表示居住用地、商业金融用地和其他建设用地比例相同。

2.2 研究方法

本文希望探讨城市居民工作日小汽车出行是否既受到出行居民个人属性的影响,又受其所在社区建成环境与社区类型的影响。文中的18个社区的居民日常活动出行数据,个人属性嵌入在社区属性中,且因变量(出行方式)是二分类变量,即小汽车出行或非小汽车出行,多层Logit模型能够解决这一特点的数据分析,有效避免传统回归方法的估计偏差;也便于描述出行方式的背景效应及社区变量对个体出行社区分异的解释程度[36-37]

多层Logit模型有多种参数设置,针对本文的研究问题和实际情况,不同社区关于小汽车出行的回归线拥有不同的斜率和不同的截距,在多层模型的个体层随机效应模型中将斜率和截距设为随机参数。同时本研究关注不同社区小汽车出行的差异(截距的不同),并寻找社区层能够解释这种差异的变量,因此将个体层得出的截距看作随机系数,添加社区层的解释变量。斜率并不添加解释变量,仍然保持随机,设为随机成分的固定参数。本文借助HLM软件来完成多层模型,模型采用限制性极大似然(restricted maximum likelihood)估计(也称为收缩估计),回归系数采用双尾t检验,方差/协方差部分采用卡方检验。多层模型由于分两层,拟合度衡量较为复杂,采用方差缩减比例来近似估计模型拟合度,类似单一层次回归中的R2 [36-37]

多层模型具体形式为:

第一层模型:

Pr ob ( Y ij = 1 / βj ) = Φ ij (3)

log [ Φ ij / ( 1 - Φ ij ) ] = η ij (4)

η ij = β 0 j + β ij × X ij + r ij (5)

因此,综合完整模型为:

η ij = log [ Φ ij / ( 1 - Φ ij ) ] = β 0 j + β ij × X ij + r ij (6)

第二层模型:

β 0 j = γ 00 + γ 01 W ij + u 0 j (7)

β ij = γ i 0 + u ij (8)

因此,综合为:

log [ Φ ij / ( 1 - Φ ij ) ] = ( γ 00 + γ 01 W ij + γ i 0 X ij ) + ( u 0 j + u ij X ij + r ij ) (9)

式中:log[Φij/(1-Φij)]为选择不同出行方式比值的自然对数;带下标i的表示为个体层的参数和变量,带下标j的表示为社区层的参数和变量;ηij为第j个社区第i个个体的出行方式状况;Xij为第j个社区第i个个体影响小汽车出行发生的社会经济属性变量;Wij为社区层影响小汽车出行发生的社区层变量;个体层随机截距(β0j)被处理为社区层解释变量的线性函数;随机斜率(βij)设为随机系数;(γ0001Wiji0Xij)和(u0j+uijXij+rij)分别是模型的固定成分和随机成分。通过式(5)看出个人社会经济属性对小汽车出行的影响,通过式(7)和式(8)可得社区层变量对个体社会经济属性和小汽车出行的作用,从而得到式(9)出行者个人社会经济属性、所在社区属性与小汽车行为之间的多层级作用关系。

本文希望通过多层模型探讨几个问题:① 哪些个体层解释变量影响小汽车出行?是如何影响的?② 哪些社区层解释变量影响小汽车出行?是如何影响的?③ 小汽车出行是否存在社区间差异?个体层解释变量和小汽车出行之间的关系是否随个体所处的社区地理背景的特征变化而变化?④ 哪些社区背景变量能够解释小汽车出行在社区地理背景下的分异?解释程度如何?相应地,多层Logit模型分为3个步骤:① 构建空模型判断因变量是否存在显著的层间变异;② 构建随机效应模型,判断个体层变量对因变量的影响显著程度及个体层变量对因变量的影响是否存在层间差异;③ 通过完整模型来判断控制条件下各层自变量对因变量分别作用程度,以及第二层变量对于第一层变异所反映背景效应的解释程度。

3 广州市居民工作日小汽车出行特征描述

根据问卷数据,运用交叉表对出行者不同社会经济属性、所在不同类型社区及不同建成环境下小汽车出行情况做描述性统计。结果显示,广州市居民选择小汽车出行与个人社会经济属性、居住地建成环境,以及居住社区类型均有密切关系。

3.1 居民个人社会经济属性与出行方式

出行者不同的社会经济属性反映了其所处在不同的生命周期,有着不同的支付能力、选择偏好和需求,在出行选择和出行偏好上存在差异[35, 39]。从表2看出,不同的个人属性,对应的出行方式差异是明显的。男性小汽车的出行比例明显高于女性;已婚居民小汽车出行的比例几乎是未婚居民出行的两倍;家庭未成年人数多的家庭,出行更倾向于使用小汽车;对于个人月收入,在1000元以下的居民几乎不使用小汽车出行,1000~7999元的居民出行使用小汽车率大约在10%,而8000元以上的居民小汽车出行比率较高。

表2 不同个人属性对应工作日出行方式比例 Tab. 2 The proportion of trip modes of different personal attributes in working-day activities
3.2 不同社区类型及建成环境与出行方式

一些研究表明不同的社区由于社会分异和区位差异,导致不同社区间居民出行方式的分异[3, 38]。由表3可得,不同社区之间的出行方式差异显著,小汽车出行比例跟社区类型密切相关。同时不同社区类型的建成环境存在差异,与出行方式有一定关系。可以看出工作日出行旧城旧机关社区、商业社区小汽车出行比例极低,小于3%;高教育旧单位社区居民小汽车出行比率较前两者高,而郊区社区居民工作日出行小汽车出行比例最高,超过30%。从建成环境上看,位于中心城区的社区的人口密度、用地混合度、商业可达性和公交站点密度均大于郊区社区,而小汽车出行比例低于郊区社区;而位于中心城区的社区中,除去旧城旧机关社区拥有更高的容积率和人口密度外,其余的建成环境指标均较为接近。

上述分析表明,不同个人社会经济属性对应不同出行方式,不同类型社区之间呈现出明显的出行方式分异,郊区社区同非郊区社区的建成环境存在差异,即城市居民工作日小汽车出行表现出个体间与社区间的差异。这两层因素之间是否存在关联?

表3 不同类型社区建成环境对应出行方式比例 Tab. 3 The proportion of trip modes of different communities with different built environments
4 广州市居民工作日出行方式多层因素影响模型及机制分析

为了更好地分析不同因素对城市居民工作日小汽车出行的影响,后文借助多层Logit模型,探讨工作日小汽车出行的影响因素在个体层面和社区层面的差异及其背后的机制。

4.1 影响变量的选取

参考相关研究[9, 23-25, 35],本文将个体层面变量作为第一层,包括社会经济状况、家庭状况2个方面的10项指标。社区层面变量作为第二层,包括密度、混合度、目的可达性、公交临近度和社区类型5个方面的9项指标,由于反映设计方面的交叉口密度指标与公交站点密度存在较强的共线性,而公交站点密度对居民小汽车出行选择的影响更加直接,故剔除了交叉口密度指标。社区类型设为虚拟变量,以高教育旧单位社区为参照类。模型指标如表4所示。

表4 居民工作日小汽车出行影响多层因素指标及描述性统计 Tab. 4 The descriptive statistics of the variables in the dataset in the study
4.2 城市居民工作日小汽车出行多层因素影响模型

4.2.1 模型1:工作日小汽车出行影响空模型 在构建完整模型之前,需要建构两层均没有解释变量的工作日小汽车出行影响空模型。空模型通过方差贡献可以确定工作日居民出行方式在社区内相关程度,同时表征小汽车出行的社区层变异是否显著。如表5所示,由工作日出行小汽车出行影响空模型结果计算组间相关系数(ICC)为0.76,表明不同社区的居民工作日小汽车出行存在显著的差异,出行方式变异中有76.32%是由社区的差异造成的,其余23.68%的变异来自于人口的个人属性及其家庭状况。因此,有必要对工作日出行小汽车出行状况进行多层模型分析。

表5 工作日出行小汽车出行选择影响因素空模型的方差估计 Tab. 5 Variance component of variables of car use on weekdays in the Null Model

4.2.2 模型2:仅包含个体层次变量的工作日小汽车出行影响因素模型 工作日小汽车出行影响空模型说明出行者所在社区层因素是导致城市居民工作日出行方式差异的主要原因,出行者的个体层因素也占一定比例。对仅包含第一层变量的居民小汽车出行影响模型进行分析,考察城市居民个体社会经济属性变量对工作日出行方式的作用。该模型中个体层变量设为自变量,是否小汽车出行设为因变量,检验二者的相关性,并检验所有个体变量的斜率和截距在社区层上的变异。参数估计结果如表6所示。

表6 仅包含第一层变量的基本工作日出行小汽车出行影响因素模型 Tab. 6 The effect model on car use on weekdays without community variables

(1)个人社会经济属性影响因素。文化程度对小汽车出行产生显著的正向作用,文化程度每提高一个层级,居民小汽车使用的发生比是原来的1.84倍。可能的原因是高文化程度的居民从事高技术、管理类的工作可能性大,需要使用小汽车来增加通勤时空间的弹性,另一方面高文化程度居民对文化知识活动方面需求较大,借助小汽车能够增大可达性。个人月收入水平是显著的正向预测因子,工作日出行中个人收入水平每增长一个层级,小汽车使用的发生比是原来的5.56倍。小汽车的购置、保养以及油费是需要一定的收入基础才能够承担的,因此收入显著影响小汽车出行的选择。单位性质,从事业机关单位到国企集体单位,再到个体、私营,居民上班出行小汽车使用发生比后者是前者的1.28倍。由于中国特有的单位制的影响,在单位的居民有较小的职—住距离,通常通过步行或自行车即可完成上班出行,同时在这样的“单位”社区中,公共服务设施较为完善,步行范围即可完成大部分的维持活动。但与已有研究不同的是,在性别方面并没有显示出对小汽车出行选择显著的影响,越来越多的女性倾向使用小汽车出行。

(2)家庭状况影响因素。全家未成年人数与选择小汽车出行具有正向关系,工作日出行中居民每多一个未成年小孩,小汽车使用发生比是原来的3.28倍。这说明居民更愿意使用小汽车接送小孩,使出行更具舒适性并节约时间。虽然家庭房屋面积与选择小汽车出行也显示有正向关系,但其发生比接近为1。房屋面积对小汽车出行的影响可能是双重的。一方面房屋面积是所在小区设施状况的一个反映,房屋面积大的小区一般配有较为完备的停车等设施,使得居民更容易趋向使用小汽车;另一方面房屋面积大的居民一般已经拥有小汽车,生活方式稳定的情况下很难会更多使用小汽车,而房屋小的居民可能会购置小汽车从而增大小汽车使用率。

4.2.3 模型3:加入社区层建成环境变量的工作日小汽车出行影响因素完整模型 模型的随机部分方差全部显著,意味着个体层变量的影响在社区层面上存在显著的分异,建立包括社区层建成环境变量的工作日出行方式影响因素模型,在截距部分增加社区层建成环境解释变量,所有显著变量设随机成分的固定参数,检验不同社区建成环境因素对小汽车出行背景效应及社区间差异的解释程度,分析结果如表7所示。

表7 社区层建成环境变量对工作日小汽车出行影响因素模型 Tab. 7 The effect model of community built environment variables on car use on weekdays

分析结果表明,建成环境因素显著影响居民工作日小汽车出行。在密度方面,建筑密度与小汽车出行呈显著负相关关系,但发生率差异并不大,可能原因在于高密度地区地铁和公交服务相对密集,可分流一部分原本打算小汽车出行的居民。混合度方面,用地混合度是显著的负影响因子,居住社区周边用地混合度越高,居民出行小汽车出行发生率变得越低。社区周边用地混合度越高,越能促进社区居民的职住平衡进而减少小汽车出行。且用地混合度越大,土地功能越丰富,居民的大部分维持活动都能够在社区周边范围内进行,大大降低了要通过使用小汽车到远处完成维持活动的需求;POI密度高的社区,居民更倾向于选择小汽车出行,POI密度每增加一个单位,小汽车出行发生率增加68%,这与西方发达国家的研究POI密度越大,土地越混合进而减少小汽车出行的结论不同[24],可能因为本研究POI密度更体现为设施密度尤其是商业设施密度,其值越大,社区周边设施吸引力越强,增加了小汽车使用的概率,使得POI密度对小汽车出行的综合影响效应为正。同时也说明POI密度对小汽车出行的正向效应通过其他诸如居民的社会经济属性偏好等渠道作用,因此多层模型分析更显必要。目的可达性方面,商业可达性对小汽车出行正向显著影响,居住地社区商业可达性每增加一个单位,居民小汽车出行发生率接近为原来的2倍,原因在于随着居住社区商业可达性的提高,增加居民驾驶小汽车到附近商业中心完成诸如买菜、购买生活用品、吃饭等活动的次数。公交临近度方面,公交站点密度系数为负且统计显著,居住社区周边公交站点密度越高,居民工作日出行小汽车使用比变得极低。更高的公交站点密度,使得居民出行使用公交来得更加便捷,同时,面向公交的友好设计,抑制了小汽车出入社区及行驶的容易性,从而降低小汽车的使用比。

对比工作日小汽车出行方式模型2的原始方差9.26和模型3的条件方差7.28,方差缩减比例指数为0.2138,即使用小汽车的发生概率的平均水平在不同社区间21.38%的变异被社区层建成环境的相关变量解释。社区层建成环境一定程度上解释个体层小汽车出行在不同社区间差异的社区背景效应。

4.2.4 模型4:加入社区层社区类型变量的工作日小汽车出行影响因素完整模型 从加入社区建成环境的模型可以看出,模型建成环境变量对居民小汽车出行的社区差异有一定解释度,但并未解释完全。社区类型变量反映居住区分异,因此在模型2基础上单独将社区类型设为虚拟变量,以高教育旧单位社区为参照类,建立包括社区层社区类型的工作日出行方式影响因素模型,检验不同社区类型是否对居民小汽车出行有显著影响及社区类型对个体层截距变异的解释程度,分析结果如表8所示。

表8 社区类型对工作日小汽车出行影响因素模型 Tab. 8 The effect model of community types on car travel of working-day activities

表8可知,社区类型对居民工作日小汽车出行影响显著,以高教育旧单位社区为参照类,商业社区里的居民工作日小汽车出行发生比最低,不到高教育旧单位社区居民的10%;住在旧城旧机关社区居民上班出行小汽车使用发生比为高教育旧单位社区居民的39%,住在郊区社区居民的小汽车使用发生比为高教育旧单位社区的8.81倍。对比工作日出行方式模型的原始方差9.26和模型4的条件方差1.96,使用小汽车的发生概率的平均水平在不同社区间78.83%的变异被社区类型变量解释。可见,社区间的出行分异已经凸显,城市居民小汽车的发生概率在出行者个人社会经济属性上的差异能够在很大程度上被社区类型解释,即选择小汽车出行的居民会倾向于居住在相同类型的社区中,造成社区间的出行分异。社区类型对于居民出行方式社区背景效应的解释程度(78.83%)相较社区建成环境的解释程度(21.38%)更大,说明就出行方式而言,居民选择社区不仅仅考虑建成环境,还有考虑与工作地的距离,社区名声以及社区区位等因素。

4.3 城市居民工作日小汽车出行影响机制框架

上述分析表明,工作日城市居民小汽车出行受个人层面社会经济属性因素、社区层面的建成环境因素和反映社会区特征的社区类型因素的共同影响,且两层因素间存在关系及相互作用机制。

首先,显著影响小汽车出行方式的个人社会经济属性因素同影响社区分异的住房面积、住房月租金、住房产权类型和住户职业类型等因素有很强的耦合性,因此出现不同类型居住社区间明显的小汽车出行分异。

其次,在中国特有的制度历史环境和社会经济转型背景下,建成环境与社区类型有着复杂的关系[31]。在计划经济时期的单位制度下,单位职工的住房、生活福利、养老、医疗、子女入学、就业问题等统一由所在单位承担和管理,因此建成环境根据所在单位大院配套,而同一单位的成员社会经济属性之间异质性小[5, 30]。虽然目前单位制的影响受住宅商品化的淡化,但原单位社区在一定时期内仍对建成环境起到约束作用。另一方面,伴随着住房的市场化和货币化,居住区分异已经形成[32-33],相似社会经济属性的居民在价格杠杆作用下会选择居住在具有类似建成环境的居住社区里,并且社区建成环境会依据所住居民的需求不断调整。

因此,对于工作日小汽车选择影响因素,出行者社会经济属性与其所住社区建成环境的关系背后的机制在于,相似社会经济属性的居民会倾向选择同一类型社区,而相同类型社区拥有相似的建成环境(图2)。但就小汽车出行影响而言,居民选择社区不仅仅考虑建成环境,还有考虑与工作地的距离,社区名声以及社区区位等因素。

图2 工作日小汽车出行选择影响机制图 Fig. 2 The influence mechanism on car travel of working-day activities

5 结论与讨论
5.1 结论

特大城市交通问题是世界范围内普遍存在的问题,对小汽车出行方式影响因素及其机制的研究对解决交通问题提供了理论支撑。国内外缺乏从社区层和个体层共同作用的角度探究居民出行方式影响因素的研究,本文基于广州市居民日常出行活动的入户调查问卷,构建两层变量互动的多层Logit模型,分析居民工作日小汽车出行的影响因素及机制。研究发现:

① 工作日城市居民小汽车出行社区间分异明显,同时受个体层因素与社区层因素影响,社区层因素影响比例更大,受到明显的地理背景效应的作用。② 对于影响居民工作日出行方式的两层因素,个体社会经济因素方面,更高的文化水平、更高的个人月收入水平、非集体单位性质和家庭存在更多未成年孩子的居民工作日小汽车使用率较高;社区建成环境因素方面,提高建筑密度、用地混合度、商业可达性和公交站点密度会降低社区居民工作日出行使用小汽车的可能,而社区内更高的POI密度却会增多居民对小汽车的使用。③ 社区层面因素,建成环境因素和社区类型因素均在一定程度上解释个体层小汽车出行社区变异的背景效应(建成环境因素解释社区间21.38%个体出行方式的变异,社区类型因素解释社区间变异达78.83%),其中社区类型因素的解释程度更好,即住在相同类型社区中的居民在出行方式选择上更加趋同,而社区类型表征的是社会分异。

5.2 讨论

本文研究结论对城市规划和管理采取相应措施来缓解交通问题有一定启示和实践意义。首先,不同类型社区内居民工作日小汽车出行分异明显,解决出行交通问题有必要从过去关注道路或城市分区尺度转到关注社区尺度。其次,在相关规划、政策中通过限制或引导社区层面的因素(如建筑密度、公交车站密度、用地混合度等),来降低小汽车出行比例的措施在中国背景下同样适用,同时更加需要关注相同类型社区建成环境因素的调整。另外,出行者不同社会经济属性使用小汽车的发生概率一部分能够被社区建成环境因素解释,但其背后的机制在于相似社会经济属性的居民会倾向选择同一类型社区,而相同类型社区拥有相似的建成环境。最后,工作日居民出行方式分异与社会空间分异有很强的耦合性,应当充分考虑中国城市社会空间分异与重构的背景,才能对居民出行行为的研究有更深刻和全面的理解。

本文也存在一些不足。首先,在文中更多侧重出行者所居住社区的属性,对于活动目的地的属性状况特别是工作地建成环境缺乏分析;其次,探讨建成环境对小汽车出行的影响没有深入分析居民的主动选择效应,还需结合更多维度进一步改进。另外,模型方面也只是对截面数据做了分析。

The authors have declared that no competing interests exist.

Reference
[1] Guangzhou Transport Planning Research Institute. Guangzhou Transport Development Annual Report 2013. 2013.
[本文引用:1]
[广州市交通规划研究所. 2013广州城市交通运行报告. 广州市交通规划研究院. 2013.]
[2] Zhou Suhong, Yan Xiaopei.The relationship between urban structure and traffic demand in Guangzhou. Acta Geographica Sinica, 2005, 60(1): 130-141.
[本文引用:1]
[周素红, 闫小培. 广州城市空间结构与交通需求关系. 地理学报, 2005, 60(1): 130-141.]
在综述国内外相关研究的基础上,以广州为案例研究城市空间结构与 交通需求的关系.结果表明,在城市形态与交通需求的关系方面,广州城市二维形态和三维形态与交通需求的空间分布之间具有一定的耦合性,二者存在一定互为因 果的关系;交通方式的转化对城市形态的演化具有一定的驱动作用;城市形态引导着交通需求的总量和布局;同时,社会经济活动及其发展是城市形态与交通需求产 生的原因和相互联系的纽带.在城市内部空间结构与交通需求的关系方面,模型分析表明,近年来广州城市内部居住与就业的空间均衡性增强,直接影响了交通需求 的产生与分布和交通方式的选择,有利于居民就近选择就业,并使通勤交通的空间分布更加均衡;此外,居住与就业人口迁移先行,引导公共设施的发展也是近年来 广州城市发展的主要特征之一;公共设施服务空间与交通需求之间存在需求作用型和供给作用型两种相互作用模式.
[3] Zhou Suhong, Yan Xiaopei.The impact of commuters' travel pattern on urban structure: A case study in some typical communities in Guangzhou. Acta Geographica Sinica, 2006, 61(2): 179-189.
[本文引用:2]
[周素红, 闫小培. 基于居民通勤行为分析的城市空间解读: 以广州市典型街区为案例. 地理学报, 2006, 61(2): 179-189.]
行为研究是近年来人文地理研究中颇受关注的领域之一,对微观行为进行宏观归纳,进而解释人地关系是提升其理论价值的重要方向之一.GIS的应用在很大程度上解决了研究手段上的瓶颈问题.本文将GIS与传统的研究方法相结合,构建基于智能图的微观行为调查方法体系,并利用GIS实现微观行为及空间感知的宏观空间模拟,探讨基于城市居民通勤行为分析的空间解读方法.同时,将该方法运用于广州市,选择具有典型意义的街区进行研究.通过调查,获取典型街区实体空间信息、被调查者社会属性、通勤行为空间和对通勤沿路相关实体要素的感知等信息,进行分析与模拟.一方面,分析认为,居民通勤行为空间在很大程度上反映实体空间的现状及其演化,同时,与社会空间有一定的关系;另一方面,以调查获取的数据为实验基础,利用GIS进行基于个体对实体空间感知的宏观效果模拟实验,该方法能较好地解读实体空间的特征和存在的主要问题,并具有一定的可拓展性.
[4] Chatman D G.How density and mixed uses at the workplace affect personal commercial travel and commute mode choice. Transportation Research Record: Journal of the Transportation Research Board, 2003,1831(1): 193-201.
A high density of shops and services near the workplace may make it easier to carry out personal commercial activities on foot before, during, and after work, enabling reduced vehicle use during the rest of the day. Investigating this question is an important addition to the current research, which has focused on residential neighborhoods. Data from the 1995 Nationwide Personal Transportation Survey are used to investigate the influence of workplace employment density and share of retail employment on commute mode choice and vehicle miles traveled (VMT) to access personal commercial activities. The analysis controls for socioeconomic characteristics and accounts for the endogeneity of commute mode choice and personal commercial VMT by employing a joint logit-Tobit model. Employment density at the workplace is found to be associated with a lower likelihood of automobile commuting and reduced personal commercial VMT, while the presence of employment in the retail category does not play a significant role. Workplace density is more clearly related to reduced VMT and automobile commuting than to characteristics of workers' residential neighborhoods and could have significant influences on personal commercial VMT and automobile commuting when increasing over a large area. The results suggest that land use planners should focus on encouraging employment density to a greater extent than is the current practice, although further research is needed on the role played by correlated factors such as higher parking costs, increased road congestion, and better transit service.
DOI:10.3141/1831-22      [本文引用:1]
[5] Wang D, Chai Y.The jobs-housing relationship and commuting in Beijing, China: The legacy of Danwei. Journal of Transport Geography, 2009, 17(1): 30-38.
China’s market-oriented reform has not only revitalized the economy but also changed the physical structure of Chinese cities, which used to be largely determined by the socialist Danwei (or work unit) system. In order to understand the impacts of the reform and the influence of Danwei on jobs–housing relationships and commuting behavior, this study investigates whether there are differences in commuting behavior between individuals who live in houses provided by Danwei and those who reside in houses from private market sources in urban China. We apply the structural equations model to investigate the interactions between housing source (from Danwei or not), jobs–housing relationship, transport mode and commuting time in Beijing, the capital city of China. The results show that Danwei housing commuters have shorter commuting trips and higher usage of non-motorized transport mode than those who live in houses from the market sources. This finding implies that the diminishing influence of the traditional Danwei system and the market-oriented reform in urban development may have changed the jobs–housing balance and increased travel demand in Chinese cities.
DOI:10.1016/j.jtrangeo.2008.04.005      [本文引用:2]
[6] Zhou Suhong, Yang Lijun.Study on the spatial characteristic of commuting in Guangzhou. Urban Transport of China, 2005, 3(1): 62-67.
[本文引用:]
[周素红, 杨利军. 广州城市居民通勤空间特征研究. 城市交通, 2005, 3(1): 62-67.]
通勤行为作为居民日常出行最主要的类型之一,其空间组织的合理与否在一定程度上对城市交通的组织起决定性作用.同样,与通勤密切相关的居住与就业两类用地,其空间组织对城市空间格局也起着重要的作用.随着中国城市社会经济和城市规划建设的发展,城市居住-就业的空间格局发生了巨大的变化,伴随着这种变化的是居民生活方式的改变,特别是出行行为的转变.在综述国内外相关研究的基础上,以广州为例,分别从广州市居住与就业空间组织对通勤行为的影响、居民通勤的基本特征出发,来探讨通勤的空间特征.
[7] Sun Bindong, Pan Xin, Ning Yuemin.Analysis on influence of job-housing balance on commute travel in Shanghai. Urban Planning Forum, 2008 (1): 77-82.
[本文引用:1]
[孙斌栋, 潘鑫, 宁越敏. 上海市就业与居住空间均衡对交通出行的影响分析.城市规划学刊, 2008(1): 77-82.]
借鉴国外文献关于就业与居住空间均衡对交通出行影响的研究方法,构造了测度就业一居住空间均衡的指数,对上海进行了实证分析.结果显示,上海的就业-居住空间均衡性趋于减弱,表现为,核心区和紧邻外围区以服务业就业为主导功能,多数外围区和近郊区以居住功能占主导,远郊区又是以就业为主要性质,但以制造业为主.城市交通层面上的后果是,跨区交通出行增加,平均出行时距和距离上升.针对上述结论对现行城市规划政策进行检讨,并得出应该增加用地功能复合性等政策启示.
[8] Cao Xinyu.Examining the relationship between neighborhood built environment and travel behavior: A review from the US perspective. Urban Planning International, 2015, 30(4): 46-52.
[本文引用:2]
[曹新宇. 社区建成环境和交通行为研究回顾与展望: 以美国为鉴. 国际城市规划, 2015, 30(4): 46-52.]
西方国家各级政府已经采用各种土地利用和交通政策来抑制蔓延式发展的负面效应。西方规划和交通学者通过分析建成环境和交通行为的关系来评价这些政策的作用,并取得了丰硕的成果。这对西方国家的规划实践具有重要的指导意义。中国近年的城市发展已经呈现出美国的蔓延式发展特征。它的负面效应也逐渐显现出来。然而,由于中国建成环境和交通行为的研究起步较晚,很多方面亟待学习和发展。本文主要以美国的研究进程为例,回顾了建成环境和交通行为研究理念、方法和理论基础的演变,总结了以往的研究问题和成果,并结合中国的实际对学术前沿的热点问题进行展望。
[9] Sun Bindong, Dan Bo.Impact of urban built environment on residential choice of commuting mode: A case study of Shanghai. Acta Geographica Sinica, 2015, 70(10): 1664-1674.
[本文引用:3]
[孙斌栋, 但波. 上海城市建成环境对居民通勤方式选择的影响. 地理学报, 2015, 70(10): 1664-1674.]
伴随中国快速城市化与机动化进程,私人汽车拥有量不断增长,由此引起的交通拥堵和环境问题已成为制约中国城市可持续发展的难题。基于上海市区的居民通勤问卷调查数据,采用多项Logit模型检验了街道尺度城市建成环境对于居民通勤方式选择的影响,结果表明,在控制了其他因素后,提高居住地的人口密度、土地利用混合度与十字路口比重,可以减少小汽车通勤方式的选择,而就业地建成环境对居民通勤方式选择影响相对较弱;建成环境对通勤方式选择的影响会因个体的社会经济异质性而不同。这些结论为通过优化土地利用规划来优化居民通勤结构的城市交通和城市规划政策提供了启示。
DOI:10.11821/dlxb201510010     
[10] Limtanakool N, Dijst M, Schwanen T.The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium-and longer-distance trips. Journal of Transport Geography, 2006, 14(5): 327-341.
This paper contributes to the limited number of investigations into the influence of the spatial configuration of land use and transport systems on mode choice for medium- and longer-distance travel (defined here as home-based trips of 50聽km and over) in the Netherlands. We have employed data from the 1998 Netherlands National Travel Survey to address the question as to how socioeconomic factors, land use attributes, and travel time affect mode choice for medium- and longer-distance travel, and how their role varies across trip purposes: commuting, business, and leisure. The empirical analysis indicates that land use attributes and travel time considerations are important in explaining the variation in mode choice for medium- and longer-distance travel when controlling for the socioeconomic characteristics of travellers.
DOI:10.1016/j.jtrangeo.2005.06.004      [本文引用:1]
[11] Zhou Suhong, Deng Lifang.Spatio-temporal pattern of residents' daily activities based on T-GIS: A case study in Guangzhou, China. Acta Geographica Sinica, 2010, 65(12): 1454-1463.
[本文引用:1]
[周素红, 邓丽芳. 基于T-GIS的广州市居民日常活动时空关系. 地理学报, 2010, 65(12): 1454-1463.]
With the rise of humanism and improvement of living standards, improving the quality of lives is now one of the hot topics, which is also the foremost purpose of Time Geography. The using of T-GIS helps to meet the needs of Time Geography, with a strong requirement of representing the temporal and spatial relations. Based on the theories of T-GIS and Time Geography, the household survey is conducted on people's daily activities and travel logs in Guangzhou, China. A series of functions for recording and representing the spatio-temporal pattern for daily activities and travel chains is developed by VBA secondary development platform of ArcGIS. It is shown that urban center in inner cities is still attractive, which lure a lot of people during the whole day. Residents, especially those who live in suburban areas have to change their daily activity pattern to adapt to the sprawl of the city. The spatio-temporal patterns of residents' activities are different among people who come from different classes. on this basis, three classes are divided. The activity space of lower class is smaller, mostly concentrated in inner city and the area around their residential communities, and their payment for transportation is the lowest. But the activity space of upper class is larger, and most of the activity space is around new center of the city, Their time spending on outdoor activities is well-regulated, and their payment for transportation is the highest. There is a close relationship between resident behavior and urban internal spatial structure, which will provide a reliable basis for urban planning and urban management.
DOI:10.11821/xb201012002     
[12] Gu Jie, Zhou Suhong, Yan Xiaopei.The space-time paths of residential mobility in Guangzhou from a perspective of life course. Geographical Research, 2013, 32(1): 157-165.
[本文引用:1]
[古杰, 周素红, 闫小培. 生命历程视角下的广州市居民居住迁移的时空路径. 地理研究, 2013, 32(1): 157-165.]
转型与重构是现阶段中国城市发展的主旋律,在这一背景下,从个体生命历程的视角研究居住迁移的时空规律和影响因素有助于从深层次理解城市空间结构重构的内置机制,同时,对公共服务设施供给和提高居民生活质量等有一定的现实意义。以2007年进行的一次人户调查资料作为基础,研究了广州市居民居住迁移时空路径的生命历程特征。结果显示,广州市居民居住迁移的时空路径呈现出年龄偏好和空间偏好两个基本规律,这两个规律可以用“N”形曲线和“微笑曲线”来表达。“N”形曲线揭示了居住迁移率随年龄变化呈现出先上升后下降最后再上升的规律。微笑曲线则揭示了老人与孩子的居住迁移的方向较为集中而中年人的居住迁移方向较为离散的特点。文章据此从不同生命历程居民居住偏好的角度分析了产生上述居住迁移宏观规律的原因。从而证实了广州市居民居住迁移的时空路径具有若干生命历程的特征,对转型期广州市住房的供给与公共设施和服务的空间配置有着积极的意义。
DOI:10.11821/yj2013010016     
[13] Kwan M P.Gender and individual access to urban opportunities: A study using space-time measures. Professional Geographer, 1999, 51(2): 210-227.
Conventional accessibility measures based on the notion of locational proximity ignore the role of complex travel behavior and space–time constraints in determining individual accessibility. As these factors are especially significant in women's everyday lives, all conventional accessibility measures suffer from an inherent “gender bias.” This study conceptualizes individual accessibility as space–time feasibility and provides formulations of accessibility measures based on the space–time prism construct. Using a subsample of European Americans from a travel diary data set collected in Franklin County, Ohio, space–time accessibility measures are implemented with a network-based GIS method. Results of the study indicate that women have lower levels of individual access to urban opportunities when compared to men, although there is no difference in the types of opportunities and areas they can reach given their space–time constraints. Further, individual accessibility has no relationship with the length of the commute trip, suggesting that the journey to work may not be an appropriate measure of job access.
DOI:10.1111/0033-0124.00158      [本文引用:1]
[14] Chai Yanwei, Wen Guilan, Liu Zhilin.Feminist geographical research on the behavior spaces of female residents in Chinese cities. Human Geography, 2003, 18(4): 1-4.
[本文引用:]
[柴彦威, 翁桂兰. 刘志林. 中国城市女性居民行为空间研究的女性主义视角. 人文地理, 2003, 18(4): 1-4.]
在以人为本、重视差异性等后现代思潮的大背景下,基于女性主义视 角的西方城市女性居民行为空间的研究有了长足的发展.中国城市中的女性群体,作为城市社会中的一个重要亚群体单元,在改革开放后经历着重大变化的同时也面 临着新的挑战,对中国城市女性居民行为空间的女性主义研究已悄然起步,今后的研究视角不仅要关注女性自身的特点及其与男性的对比,而且更要重视女性内部的 差异性以及城市内部和城市之间的比较.
[15] Shergold I, Lyons G, Hubers C.Future mobility in an ageing society: Where are we heading? Journal of Transport & Health, 2015, 2(1): 86-94.
61Assistive technologies influence where and how older people will live.61Scenario planning reveals plausible but divergent futures for older people.61Assistive technologies coupled with state support for care indirectly affect travel.61Shaping change rather than anticipating change may be a better policy response.
DOI:10.1016/j.jth.2014.10.005      [本文引用:]
[16] Zhou S, Deng L, Kwan M P, et al.Social and spatial differentiation of high and low income groups' out-of-home activities in Guangzhou, China. Cities, 2015(45): 81-90.
With China’s rapid urbanisation driving its growing economy, the enlarging socio-spatial inequalities in the cities have received wide attention. Rather than following the largely studied residential spaces, this paper focuses on socio-spatial differentiation based on the spaces of one’s out-of-home activities. Using data of 1006 individuals collected by door to door questionnaires, this paper sets up the spatial and temporal autocorrelation GT coefficient to examine the spatial heterogeneity characteristics of high- and low income groups’ out-of-home activities in a continuous spatiotemporal framework. The factors and different mechanisms influencing the clustering of the activities are discussed to better understand social diversity in post-reform urban China. The results suggest that there is obvious spatial and temporal variation in high- and low income groups’ out-of-home activities, indicating that differing social spaces are not just limited to the macro-static residence-based living space, but also exist in the individual’s daily-activities space. Both high- and low income people have drastically different activity spaces and they may not interact much with each other. This is socially very significant because it means that there is considerable social isolation or segregation for both groups. The results also show that within the same income group there exists a divisive cluster with different formation mechanisms, including the job–housing relationship, the correlation of activity opportunities with those surrounding residential areas and the individual’s ability to access activities (that is, space–time accessibility). Structural transition can also impact on activities choices of various social groups.
DOI:10.1016/j.cities.2015.03.002      [本文引用:]
[17] Srinivasan S, Rogers P.Travel behavior of low-income residents: Studying two contrasting locations in the city of Chennai, India. Journal of Transport Geography, 2005, 13(3): 265-274.
Data on travel behavior in developing countries like India is minimal. This is especially true for the relatively poor residents of urban India. They are dependent on fewer options for transportation and have little choice in terms of employment location given their dependence on walking or bicycles. This is significant in cities like Chennai because employment is highly concentrated in the center of the city. In this study, the results of a survey of 70 households in Chennai were analyzed to estimate statistical models of travel behavior with respect to mode choice and trip frequency. The households were located in two different parts of the city: one group of households lived close to the city center (in a settlement called Srinivasapuram) and the other at the periphery (in a location called Kannagi Nagar). We analyze the differences in travel behavior due to differences in accessibility to employment and services between the two settlement locations. The results indicate that differences in accessibility appear to strongly affect travel behavior. Residents in the centrally located settlement were more likely to use non-motorized modes for travel (walk or bicycle) than the peripherally located residents. It is vital therefore that, policy makers in India consider location of employment in the planning of new housing for low-income households.
DOI:10.1016/j.jtrangeo.2004.07.008      [本文引用:1]
[18] De Jong G C. An indirect utility model of car ownership and private car use. European Economic Review, 1990, 34(5): 971-985.
In this paper a model is developed for the simultaneous determination of private car ownership and private car use, measured as the annual number of kilometers. This model is a micro-economic utility model, in which the fixed and variable car costs enter through the budget restriction. This allows us to do micro-simulations of increasing those costs, which is a policy issue in the Netherlands. It turns out that in this model both fixed and variable car costs are effective measures for reducing traffic (growth), the former working primarily through decreasing car ownership levels, the latter having a more direct effect on car use.
DOI:10.1016/0014-2921(90)90018-T      [本文引用:1]
[19] Gliebe J P, Koppelman F S.A model of joint activity participation between household members. Transportation, 2002, 29(1): 49-72.
A proportional shares model of daily time allocation is developed and applied to the analysis of joint activity participation between adult household members. The model is unique in its simultaneous representation of each decision maker's decisions concerning independent activity participation, allocation of time to joint activities, and the interplay between individual and joint activities. Further, the model structure ensures that predicted shares of joint activity outcomes be the same for both decision makers, an improvement over models that do not make interpersonal linkages explicit. The empirical analysis of travel diary data shows that employment commitments and childcare responsibilities have significant effects on tradeoffs between joint and independent activities. In addition, evidence is presented for the continued relevance of gender-based role differences in caring for children and employment participation.
DOI:10.1023/A:1012995509777      [本文引用:1]
[20] Zhao P.The impact of the built environment on individual workers' commuting behavior in Beijing. International Journal of Sustainable Transportation, 2013, 7(5): 389-415.
The increasing emission of transport-related pollutants has become a key issue in relation to climate change mitigation and the improvement of air quality in China's cities. This article aims to examine the effects of changes in the built environment on transportation by examining the case of Beijing. Looking at household survey data, the analysis found that individual workers鈥 commuting behavior (concerning travel destination, mode choice and travel time) is significantly related to some aspects of the built environment when socioeconomic and demographic characteristics are taken into account. There are obvious differences in the effects of the built environment on commuting across income groups, occupations and industries.
DOI:10.1080/15568318.2012.692173      [本文引用:1]
[21] Cervero R, Kockelman K.Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, 1997, 2(3): 199-219.
The built environment is thought to influence travel demand along three principal dimensions —density, diversity, and design. This paper tests this proposition by examining how the ‘3Ds’ affect trip rates and mode choice of residents in the San Francisco Bay Area. Using 1990 travel diary data and land-use records obtained from the U.S. census, regional inventories, and field surveys, models are estimated that relate features of the built environment to variations in vehicle miles traveled per household and mode choice, mainly for non-work trips. Factor analysis is used to linearly combine variables into the density and design dimensions of the built environment. The research finds that density, land-use diversity, and pedestrian-oriented designs generally reduce trip rates and encourage non-auto travel in statistically significant ways, though their influences appear to be fairly marginal. Elasticities between variables and factors that capture the 3Ds and various measures of travel demand are generally in the 0.06 to 0.18 range, expressed in absolute terms. Compact development was found to exert the strongest influence on personal business trips. Within-neighborhood retail shops, on the other hand, were most strongly associated with mode choice for work trips. And while a factor capturing ‘walking quality’ was only moderately related to mode choice for non-work trips, those living in neighborhoods with grid-iron street designs and restricted commercial parking were nonetheless found to average significantly less vehicle miles of travel and rely less on single-occupant vehicles for non-work trips. Overall, this research shows that the elasticities between each dimension of the built environment and travel demand are modest to moderate, though certainly not inconsequential. Thus it supports the contention of new urbanists and others that creating more compact, diverse, and pedestrian-orientated neighborhoods, in combination, can meaningfully influence how Americans travel.
DOI:10.1016/S1361-9209(97)00009-6      [本文引用:1]
[22] Ewing R, Cervero R.Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 2010, 76(3): 265-294.
Some of today’s most vexing problems, including sprawl, congestion, oil dependence, and climate change, are prompting states and localities to turn to land planning and urban design to rein in automobile use. Many have concluded that roads cannot be built fast enough to keep up with rising travel demand induced by the road building itself and the sprawl it spawns. The purpose of this meta-analysis is to summarize empirical results on associations between the built environment and travel, especially nonwork travel.
DOI:10.1080/01944361003766766      [本文引用:]
[23] Frank L D, Engelke P, Schmid T.Health and community design: The impacts of the built environment on physical activity. Washington, DC: Island Press, 2003.
[本文引用:2]
[24] Cervero R.Mixed land-uses and commuting: Evidence from the American housing survey. Transportation Research Part A: Policy and Practice, 1996, 30(5): 361-377.
Past research suggests that mixed land-uses encourage non-auto commuting; however, the evidence remains sketchy. This paper explores this question by investigating how the presence of retail activities in neighborhoods influences the commuting choices of residents using data from the 1985 American Housing Survey. Having grocery stores and other consumer services within 300 feet of one's residence is found to encourage commuting by mass transit, walking and bicycling, controlling for such factors as residential densities and vehicle ownership levels. When retail shops are beyond 300 feet yet within 1 mile of residences, however, they tend to encourage auto-commuting, ostensibly because of the ability to efficiently link work and shop trips by car. The presence of nearby commercial land-uses is also associated with relatively low vehicle ownership rates and short commuting distances among residents of a mixed-use neighborhood. Overall, residential densities exerted a stronger influence on commuting mode choices than levels of land-use mixture, except for walking and bicycle commutes. For non-motorized commuting, the presence or absence of neighborhood shops is a better predictor of mode choice than residential densities.
DOI:10.1016/0965-8564(95)00033-X      [本文引用:2]
[25] Acker V V, Witlox F.Commuting trips within tours: How is commuting related to land use? Transportation, 2011, 38(3): 465-486.
Studies that model the effects of land use on commuting generally use a trip-based approach or a more aggregated individual-based approach: i.e. commuting is conceptualized in terms of modal choice, distance and time per single trip, or in terms of daily commuting distance or time. However, people try to schedule activities in a daily pattern and, thus, consider tours instead of trips. Data from the 2000 to 2001 Travel Behaviour Survey in Ghent (Belgium) illustrate that car use and commuting times significantly differ between commuting trips within work-only tours and more complex tours. Therefore, this paper considers trip-related decisions simultaneously with tour-related decisions. A multiple group structural equation model (SEM) confirmed that the relationship between land use and commuting differs between work-only tours and more complex tours. Trips should be considered within tours in order to correctly understand the effect of land use scenarios such as densifying on commuting. Moreover, the use of multiple group SEM enabled us to address the issue of the complex nature of commuting. Due to interactions between various explanatory variables, land use patterns do not always have the presumed effect on commuting. Land use policy can successfully influence commuting, but only if it simultaneously accounts for the effects on car availability, car use, commuting distance and commuting time.
DOI:10.1007/s11116-010-9309-6      [本文引用:2]
[26] Zhang M.The role of land use in travel mode choice: Evidence from Boston and Hong Kong. Journal of the American Planning Association, 2004, 70(3): 344-360.
This article presents a study that analyzed the influence of land use on travel mode choice using survey data from Metropolitan Boston and Hong Kong. In Boston, the focus of inquiry was on whether land use would still matter for mode choice (and if so, to what extent) when mode attributes and traveler socioeconomic characteristics were taken into account. In Hong Kong, where the role of land use in mode choice is obvious due to the densely built environment, the focus was on whether land use completely explained the transit-dominated travel pattern. The empirical modeling confirmed that the role of land use in influencing travel was independent from travel time and monetary costs. Elasticity estimates show that the composite effect of land use on driving could be comparable in magnitude to that of driving cost. Yet being place specific, land use strategies are limited by the spatial extent to which they can be implemented. Land use strategies influence travel more effectively when complemented by pricing policies.
DOI:10.1080/01944360408976383      [本文引用:1]
[27] Cao X, Fan Y.Exploring the influences of density on travel behavior using propensity score matching. Environment and Planning B: Planning and Design, 2012, 39(3): 459-470.
The causality issue has become one of the key questions in the debate over the relationship between the built environment and travel behavior. Since a residential self-selection effect exists, it is important to know if the observed influence of the built environment on travel behavior diminishes substantially once we control for self-selection. Using 5537 adult respondents to the 2006 Great Triangle Travel Survey in North Carolina, this study applied the propensity score matching approach to identify the causal effect of density on travel behavior and the relative contribution of self-selection to travel behavior. The results showed that, after removing self-selection bias, residents living in high-density neighborhoods travel, on average, 3.31 fewer miles per person per day than those who live in low-density neighborhoods. Self-selection effects account for 28%, 64%, and 49% of the observed influences of density on personal miles travelled, driving duration, and transit duration, respectively. We also found that different modeling approaches produce different point estimates, and that interval estimates of treatment effects tend to have a large variation. This points to a caveat of using point estimates to evaluate the impacts of the built environment on travel behavior.
DOI:10.1068/b36168      [本文引用:1]
[28] Wang D, Lin T.Residential self-selection, built environment, and travel behavior in the Chinese context. Journal of Transport and Land Use, 2014, 7(3): 5-14.
Residential self-selection has been reported to be a factor confounding the observed relationship between built environment and travel behavior. By incorporating residential self-selection, studies have generated much insight into the causalities involved in the relationship between built environment and travel behavior. However, most of these studies were conducted in North American cities, where individuals may have the opportunity to realize their preferences in residential and transport mode choices. There are not many similar studies for other parts of the world, such as China, where residential and transport choices are probably more constrained than in North America. This paper aims to partly fill the gap by discussing the specificities of the residential self-selection issue in urban China and suggesting how to cope with this issue when examining the relationship between built environment and travel behavior in the Chinese context. We argue that studies addressing the residential self-selection issue in China need to consider the housing source, which has implications for residential choice, and acknowledge the importance of some travel-related attitudes such as preferences for short commutes, good accessibility to public transport, and proximity to markets for daily goods shopping.
DOI:10.5198/jtlu.v7i3.486      [本文引用:2]
[29] Naess P.Residential location, travel, and energy use in the Hangzhou metropolitan area. Journal of Transport and Land Use, 2010, 3(3): 27-59.
This paper presents the results of a study examining the influence of residential location on travel behavior in the Hangzhou Metropolitan Area, China. The location of the dwelling relative to the center hierarchy of the metropolitan area is found to exert a considerable influence on the travel behavior of the respondents. On average, living close to the center of Hangzhou contributes to less overall travel, a higher proportion of trips by bicycle and on foot, and lower consumption of energy for transport. The location of the dwelling relative to the closest second-order and third-order center also influences travel, but not to the same extent as proximity to the city center. These geographical differences in travel behavior are independent of residential preferences and of attitudes toward transport and environmental issues, and therefore cannot be explained by residential self-selection.
DOI:10.5198/jtlu.v3i3.98      [本文引用:1]
[30] Wang D, Chai Y, Li F.Built environment diversities and activity-travel behavior variations in Beijing, China. Journal of Transport Geography, 2011,19(6): 1173-1186.
The association between built environment and travel behaviour has received considerable research attention in recent years. In an attempt to contribute to this growing literature, this paper investigates the connections between urban built environments and activity–travel patterns in Beijing, the capital city of China. We characterize the built environment in Beijing and establish associations between built environment and activity–travel behaviour in terms of car ownership, time spent for out-of-home activities, and daily trip frequencies and travel time. Activity diaries from 1119 respondents living in ten different neighbourhoods were collected by face-to-face interviews. A household-level structure equations model incorporating intra-household interactions is developed to analyse this data. The empirical results show that residents of different types of neighbourhoods in Beijing demonstrate significant differences in car ownership, time spent for out-of-home activities, trip rate, and travel time. Further, the characteristics of the built environment are found to have more significant impacts on the activity–travel behaviour of the male head than that of the female head.Highlights? Establish the association between built environment and activity-travel behavior in Beijing, China. ? Develop a household-level structure equations model incorporating intra-household activity-travel interactions. ? Study how the built environment impacts on different members of household. ? Examine the interaction effects of built environment variables.
DOI:10.1016/j.jtrangeo.2011.03.008      [本文引用:2]
[31] Chai Yanwei, Liu Zhilin, Li Zhengrong, et al.Time-space Structure of Chinese Cities. Beijing: Peking University Press, 2002: 1-3.
[本文引用:2]
[柴彦威, 刘志林, 李峥嵘, . 中国城市的时空间结构. 北京: 北京大学出版社, 2002: 1-3.]
[32] Xu Xueqiang, Hu Huaying, Yeh A G O. A factorial ecological study of social spatial structure in Guangzhou. Acta Geographica Sinica, 1989, 44(4): 385-399.
[本文引用:3]
[许学强, 胡华颖, 叶嘉安. 广州市社会空间结构的因子生态分析. 地理学报, 1989, 44(4): 385-399.]
[33] Zhou Chunshan, Luo Renze, Dai Dandan.Evolution and mechanism of the residential spatial structure from 2000 to 2010 in Guangzhou. Geographical Research, 2015, 34(6): 1109-1124.
[本文引用:2]
[周春山, 罗仁泽, 代丹丹. 2000-2010年广州市居住空间结构演变及机制分析. 地理研究, 2015, 34(6): 1109-1124.]
<p>首先运用因子生态分析方法对广州市2010年的居住空间结构与居住人口特征进行分析,提取出6个主因子并划分为9类居住区,得出广州市居住空间具有明显的分异性。然后与2000年的居住空间结构进行比较,归纳出广州市居住空间演变具有历史延续性、市场及政策因素影响突出、空间拓展与城市发展同步、整体居住空间呈现&#x0201c;圈层+扇形&#x0201d;融合发展等特征。根据不同的空间层次特征,概括出四种演变模式:中心区稳定发展模式、近郊区商品房拓展模式、远郊区糅合发展模式和特定区保障房镶嵌模式。探讨了广州市居住空间结构的演变机制,包括历史发展惯性、房地产发展带动、住房保障影响、城市规划引导等四个方面。最后结合国内其他大城市相关研究推导出转型期中国大城市的居住空间结构。</p>
DOI:10.11821/dlyj201506010     
[34] Tana, Chai Yanwei, Kwan M P. The relationship between the built environment and car travel distance on weekdays in Beijing. Acta Geographica Sinica, 2015, 70(10): 1675-1685.
[本文引用:1]
[塔娜, 柴彦威, 关美宝. 建成环境对北京市郊区居民工作日汽车出行的影响. 地理学报, 2015, 70(10): 1675-1685.]
郊区化导致的汽车出行增加及相关的城市环境与社会问题日益成为城市研究关注的焦点,但目前国内对建成环境与汽车出行行为的研究刚刚起步。基于GPS与活动日志相结合的居民一周活动与出行数据,利用GIS空间分析分别以居住地、工作地和活动空间作为地理背景,分析建成环境对于郊区居民汽车出行距离的影响因素。研究发现,建成环境对工作日汽车出行的影响因地理背景的选择而有不同。整日出行受到工作地和活动空间的影响,工作地与活动空间建设密度增高汽车出行减少,但是居住空间的影响不显著;通勤出行受到居住地、工作地和活动空间的影响,居住地商业密度提高和建设密度降低、工作地和活动空间建设密度提高,汽车出行减少;非工作活动出行也受到居住地、工作地和活动空间的影响,居住地、工作地和活动空间的公交密度低、工作地和活动空间建设密度高,汽车出行少。基于研究结果,本文对地理背景不确定性问题进行了探讨,提出出行行为的研究需要考虑居住地以外其他地理背景的影响,并对控制汽车使用的公共政策提出了建议。
DOI:10.11821/dlxb201510011     
[35] Wang Fenglong, Wang Donggen.Characteristics and determinants of car use in Beijing. Acta Geographica Sinica, 2014,69(6): 771-781.
[本文引用:3]
[王丰龙, 王冬根. 北京市居民汽车使用的特征及其影响因素. 地理学报, 2014, 69(6): 771-781.]
The increased private car ownership in China especially large cities has facilitated trip making and activity engagement behavior of citizens, but also led to many urban problems including traffic congestion and air pollution. While car ownership control has received much attention from academics and policy makers, not much research on private car usage in Chinese cities has been conducted. To fill in this gap, this paper examines the patterns and determinants of private car use in Beijing. Data are derived from an activitytravel behavior survey conducted in Beijing from November 2011 to July 2012. We develop econometric models from the data and find that the intensity of car usage in Beijing is significantly influenced by the purpose of car use, built environment variables and personal and household socioeconomic characteristics. Specifically, commuting is the most important purpose for car use in Beijing and the share of car use for this purpose is much higher than for other purposes; poor public transportation service and convenient parking facilities are two major driving forces of car use in Beijing; individual and household socioeconomic characteristics, such as marriage status, employment, household size and age, also play an important role. There is not much difference in frequency and duration of car usage between weekdays and weekends. These findings have important implications for transportation policymaking. Policies aiming at controlling car use may need to act on the factors that are found here as significant determinants of car use (e.g., built environment variables). It is argued that more studies on car use are required to better understand patterns and determinants of car use in Chinese cities and inform policies that can effectively reduce car use and alleviate car dependency.
DOI:10.11821/dlxb201406005     
[36] Zhang Lei, Lei Li, Guo Boliang, et al.Applied Multilevel Data Analysis. Beijing: Educational Science Publishing House, 2003.
[本文引用:3]
[张雷, 雷雳, 郭伯良. 多层线性模型应用. 北京: 教育科学出版社, 2003.]
[37] Xu Y, Wen M, Wang F.Multilevel built environment features and individual odds of overweight and obesity in Utah. Applied Geography, 2014, 60: 197-203.
Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environments and individual odds of overweight and obesity after controlling for individual risk factors. The BRFSS data include information on 21,961 individuals geocoded to zip code areas. Individual variables include BMI (body mass index) and socio-demographic attributes such as age, gender, race, marital status, education attainment, employment status, and whether an individual smokes. Neighborhood built environment factors measured at both zip code and county levels include street connectivity, walk score, distance to parks, and food environment. Two additional neighborhood variables, namely the poverty rate and urbanicity, are also included as control variables. MLM results show that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with stronger fast food presence linked to higher odds of overweight and obesity. These findings suggest that obesity risk factors lie in multiple neighborhood levels and built environment features need to be defined at a neighborhood size relevant to residents' activity space.
DOI:10.1016/j.apgeog.2014.10.006      [本文引用:3]
[38] Rajamani J, Bhat C R, Handy S, Knaap G and Song Y. Assessing impact of urban form measures on nonwork trip mode choice after controlling for demographic and level-of-service effects. Transportation Research Record, 2003(1831): 158-165.
The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The current paper investigates the significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice. The travel data used for analysis is the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song (2002) and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment using the Portland data. The results of the multinomial logit mode choice model indicate that mixed-uses promote walking behavior for nonwork activities.
DOI:10.3141/1831-18      [本文引用:2]
版权所有 © 2013 《地理学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发