﻿ 居民非工作活动和出行的结构方程建模
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 同济大学学报(自然科学版)  2017, Vol. 45 Issue (9): 1311-1318.  DOI: 10.11908/j.issn.0253-374x.2017.09.009 0

引用本文

ZHANG Ping, DENG Nengjing, JIANG Yaoyao. Structural Equations Analysis on Non-work Trip of Residents' Activity and Travel Behavior[J]. Journal of Tongji University (Natural Science), 2017, 45(9): 1311-1318. DOI: 10.11908/j.issn.0253-374x.2017.09.009.

文章历史

1. 同济大学 道路与交通工程教育部重点实验室，上海 200092;
2. 上海日景规划建筑设计有限公司，上海 200092

Structural Equations Analysis on Non-work Trip of Residents' Activity and Travel Behavior
ZHANG Ping1, DENG Nengjing1, JIANG Yaoyao2
1. State Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China;
2. Shanghai Rijing Planning and Architecture Design Co. Ltd., Shanghai 200092, China
Abstract: Non-work travel in the proportion of the structure of travel is on the rise. Structural equation modeling (SEM) was adopted to analyze the influence mechanism of residents' non-work activity and travel behavior based on the Shanghai Household Travel Survey. The results of SEM show that socio-demographic characteristics and neighborhood features both affect residents' non-work activity and travel, but neighborhood features have stronger influence than socio-demographic characteristics. The most significant characteristic in individual is the employment situation. The most significant characteristic in household is the number of automobiles. And the most significant characteristic in neighborhood is the coverage of neighborhood services.
Key words: non-work activity    travel behavior    structural equations model

1 研究设计和数据 1.1 模型选择

 图 1 本文分析框架 Fig.1 Analysis framework in this paper

1.2 数学表达

 $y = \mathit{\pmb{B}}y + \mathit{\pmb{\Gamma} }x + \zeta$ (1)

1.3 参数估计

1.4 模型评价

1.5 数据来源

 图 2 研究社区区位图 Fig.2 The zone map of neighborhoods

2 模型构建 2.1 数据分析

(1) 中心区社区居民和外围区社区居民在出行行为特征方面的差异性不大，郊区社区居民与中心城社区居民的出行行为特征差异较大.

(2) 3个圈层社区居民在进行非工作活动出行时均以慢行出行为主，主要在社区内部或者周边进行非工作活动，平均出行距离较短；中心区社区居民小汽车出行比例明显高于其他两个圈层社区居民，其居民家庭拥有小汽车的比例较高.

(3) 3个圈层社区居民的活动链模式都以简单维持性活动链(家维持性活动家，H-M-H)和复杂非工作活动链为主；中心区社区居民复杂工作活动链比例明显高于其他两个圈层社区居民，表明中心区社区居民在以通勤为主的过程中，更多能够进行一些非工作活动.

(4) 3个圈层社区居民的日出行次数和链个数差别不大，但郊区社区居民短出行次数明显多于其他两个圈层社区居民，表明郊区社区居民更依赖社区内部公共服务设施进行非工作活动.

(1) H-M-H：简单维持性活动链；

(2) H-L-H：简单休闲性活动链；

(3) 复杂非工作活动链：活动链中包含2次及以上非工作活动出行，活动链代码可以表示为H-NW-[NW]-H-[NW-[NW]-H](所表示的活动次数可以取0次及以上，但一条活动链中所表示的活动次数不能同时都取0)，例如H-M-L-H和H-M-H-L-H等；

(4) 复杂工作活动链：以通勤为主的出行包含非工作活动，活动链代码可以表示为H-[NW]-[W]-[NW]-H-[[NW]-[W]-[NW]-H]，例如H-W-M-H、H-M-H-W-H等.

2.2 变量选择

2.3 模型结构

 图 3 模型结构设定 Fig.3 Model structure setting
2.4 模型拟合及评价

3 结果分析与讨论 3.1 个人特征对非工作活动和出行行为的效应分析

3.2 家庭特征对非工作活动和出行行为的效应分析

3.3 社区特征对非工作活动和出行行为的效应分析

3.4 内生变量之间的效应分析

 图 4 内生变量直接效应路径图 Fig.4 Endogenous variable direct effect path

4 结论

(1) 通过结构方程模型，能够捕捉到居民社会人口特征、社区特征、非工作活动参与和出行行为之间的复杂关系.特别是模型结果显示，在模型中包含进社区特征外生变量，能够比仅有社会人口特征变量更好地解释非工作出行行为.

(2) 个人特征、家庭特征和社区特征都对居民非工作活动和出行行为产生影响，但影响强度不同.整体而言，社区特征的影响强度大于出行者个人特征、家庭特征.

(3) 个人特征中影响最为显著的变量是就业情况，由于居民就业情况的差异导致活动特征的差异，进一步造成出行特征的差异；家庭特征中影响最为显著的变量是小汽车数量，小汽车数量对维持性活动出行距离的直接效应为0.103，对出行方式的直接效应为0.289；社区特征中影响最为显著的变量是社区公共设施服务覆盖率.

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