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Article(id=1146098716198408892, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, articleNumber=null, orderNo=22, doi=10.3981/j.issn.1000-7857.2024.04.00321, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1712851200000, receivedDateStr=2024-04-12, revisedDate=1736870400000, revisedDateStr=2025-01-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1751180827426, onlineDateStr=2025-06-29, pubDate=1743091200000, pubDateStr=2025-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1744992000000, onlineIssueDateStr=2025-04-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1751180827426, onlineFirstDateStr=2025-06-29, sourceXml=null, magXml=null, createTime=1751180827426, creator=18627231156, updateTime=1774079557665, updator=sys-migrate, issue=Issue{id=1146500022830469878, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='6', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='1743091200000', pubDateStr='2025-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1751276506383, creator='13701087609', updateTime=1774330962150, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243197418926355120, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243197418926355121, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=65, endPage=73, ext={EN=ArticleExt(id=1146098717360231111, articleId=1146098716198408892, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=The nonlinear relationship between age and children's daily travel behavior, columnId=1172890533929304542, journalTitle=Science & Technology Review, columnName=Exclusive: Green Building and Urban Governance, runingTitle=null, highlight=null, articleAbstract=

Understanding the travel behavior of children at different age is crucial for building child friendly communities. In this article, by taking Shuangliu District in Chengdu as an example, a survey on children's travel characteristics was conducted, and random forest model was employed to explore the nonlinear relationship between children's age and their daily travel behavior. The results show: (1) Children's age is the most significant factor affecting their travel behavior among various social demographic and family characteristics, especially the choice of travel companionship, the relative importance of which is 25.37%; (2) The mode of travel for children exhibited nonlinear characteristics as they grow, and became more independent and diverse; (3) The choice of travel companionship and destinations also showed nonlinear characteristics with age. Particularly after the age of 14, children tended to choose to travel alone or with friends, with a significant increase in their travel distance.

, authors=null, authorsList=Yibin AO, Yi LONG, Yuxin TANG, Haimei LI, authorCompany=null, correspAuthors=Yi LONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1146098721713918734, articleId=1146098716198408892, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=年龄与儿童日常出行行为的非线性关系, columnId=1172890534105465311, journalTitle=科技导报, columnName=特色专题：绿色建筑与城市治理, runingTitle=null, highlight=null, articleAbstract=

了解不同年龄阶段儿童的出行特征，对建设儿童友好型社区具有重要意义。以成都市双流区为例，开展儿童出行特征调查，利用随机森林模型，考察了儿童年龄与其日常出行行为的非线性关系。结果表明：儿童年龄是家庭社会人口特征中影响儿童出行行为最重要的因素，特别是在出行陪伴方式中，相对重要性达到了25.37%；儿童出行方式随年龄变化呈现非线性特征，儿童出行方式随年龄变得更独立和多样化；出行陪伴方式和目的地选择也呈现年龄非线性特征，特别是在14岁后，儿童更倾向于选择独自出行和朋友陪伴，同时出行距离也开始显著上升。

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敖仪斌, 教授, 研究方向为农村建成环境与村民日常活动行为关系、工程管理、乡村规划与建设, 电子信箱:

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龙毅(通信作者), 硕士研究生, 研究方向为儿童发展, 电子信箱:
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敖仪斌, 教授, 研究方向为农村建成环境与村民日常活动行为关系、工程管理、乡村规划与建设, 电子信箱:

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敖仪斌, 教授, 研究方向为农村建成环境与村民日常活动行为关系、工程管理、乡村规划与建设, 电子信箱:

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变量 定义/描述 平均值/比例
出行信息 － －
出行目的地 24类儿童友好设施 －
出行频率 0=从未去过；1=一天一次；2=一周一次；3=两周一次；4=一月一次；5=两月一次；6=半年一次；7=一年一次 0=0%；1=13.48%；2=21.69%；3=8.61%；4=18.81%；5=9.73%；6=14.78%；7=12.89%
陪伴方式 1=独自一人；2=父母陪伴；3=祖父母陪伴；4=朋友陪伴；5=其他 1=16.64%；2=67.09%；3=6.91%；4=8.15%；5=1.21%；
出行距离 按出行方式计算的出行距离（km） 3.67
出行方式 1=步行；2=自行车；3=公交车；4=私家车；5=电动自行车 1=48.19%；2=5.42%；3=3.19%；4=21.54%；5=21.66%；
社会人口特征 － －
年龄 儿童年龄（岁） 9.14
性别 男=1；女=0 1=49.6%；0=50.4%
家庭规模 家庭总人口数 4.67
家庭年收入 家庭年收入水平（万元）（1=1万以下；2=1~3万；3=3~5万；4=5~8万；5=8~12万；6=12~15万；7=15~20万；8=20万以上） 1=3.37%；2=7.01%；3=14.82%；4=20.23%；5=20.05%；6=11.45%；7=9.85%；8=13.22%
父亲职业 1=无业/失业；2=农民；3=专业人士（如教师/医生/律师等）；4=公务员/事业单位/政府工作人员；5=国企职员；6=私企职员；7=打零工；8=个体户；9=其他 1=5.06%；2=9.49%；3=3.28%；4=6.65%；5=6.21%；6=25.02%；7=15.08%；8=20.67%；9=8.52%
父亲文化程度 1=无；2=小学；3=初中；4=高中/中专；5=大学；6=硕士；7=博士 1=1.15%；2=5.24%；3=21.03%；4=38.86%；5=32.48%；6=1.15%；7=0.09%
母亲职业 1=无业/失业；2=农民；3=专业人士（如教师/医生/律师等）；4=公务员/事业单位/政府工作人员；5=国企职员；6=私企职员；7=打零工；8=个体户；9=其他 1=15.62%；2=9.32%；3=4.70%；4=7.81%；5=3.99%；6=24.13%；7=13.93%；8=12.87%；9=7.63%
母亲文化程度 1=无；2=小学；3=初中；4=高中/中专；5=大学；6=硕士；7=博士 1=1.06%；2=3.82%；3=17.57%；4=45.43%；5=34.41%；6=0.71%；7=0%
自行车保有量 自行车的数量 0.30
电动自行车保有量 电动自行车的数量 0.83
私家车保有量 私家车的数量 1.02
), ArticleFig(id=1242142964206608896, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098716198408892, language=CN, label=表1, caption=

变量的描述性统计

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变量 定义/描述 平均值/比例
出行信息 － －
出行目的地 24类儿童友好设施 －
出行频率 0=从未去过；1=一天一次；2=一周一次；3=两周一次；4=一月一次；5=两月一次；6=半年一次；7=一年一次 0=0%；1=13.48%；2=21.69%；3=8.61%；4=18.81%；5=9.73%；6=14.78%；7=12.89%
陪伴方式 1=独自一人；2=父母陪伴；3=祖父母陪伴；4=朋友陪伴；5=其他 1=16.64%；2=67.09%；3=6.91%；4=8.15%；5=1.21%；
出行距离 按出行方式计算的出行距离（km） 3.67
出行方式 1=步行；2=自行车；3=公交车；4=私家车；5=电动自行车 1=48.19%；2=5.42%；3=3.19%；4=21.54%；5=21.66%；
社会人口特征 － －
年龄 儿童年龄（岁） 9.14
性别 男=1；女=0 1=49.6%；0=50.4%
家庭规模 家庭总人口数 4.67
家庭年收入 家庭年收入水平（万元）（1=1万以下；2=1~3万；3=3~5万；4=5~8万；5=8~12万；6=12~15万；7=15~20万；8=20万以上） 1=3.37%；2=7.01%；3=14.82%；4=20.23%；5=20.05%；6=11.45%；7=9.85%；8=13.22%
父亲职业 1=无业/失业；2=农民；3=专业人士（如教师/医生/律师等）；4=公务员/事业单位/政府工作人员；5=国企职员；6=私企职员；7=打零工；8=个体户；9=其他 1=5.06%；2=9.49%；3=3.28%；4=6.65%；5=6.21%；6=25.02%；7=15.08%；8=20.67%；9=8.52%
父亲文化程度 1=无；2=小学；3=初中；4=高中/中专；5=大学；6=硕士；7=博士 1=1.15%；2=5.24%；3=21.03%；4=38.86%；5=32.48%；6=1.15%；7=0.09%
母亲职业 1=无业/失业；2=农民；3=专业人士（如教师/医生/律师等）；4=公务员/事业单位/政府工作人员；5=国企职员；6=私企职员；7=打零工；8=个体户；9=其他 1=15.62%；2=9.32%；3=4.70%；4=7.81%；5=3.99%；6=24.13%；7=13.93%；8=12.87%；9=7.63%
母亲文化程度 1=无；2=小学；3=初中；4=高中/中专；5=大学；6=硕士；7=博士 1=1.06%；2=3.82%；3=17.57%；4=45.43%；5=34.41%；6=0.71%；7=0%
自行车保有量 自行车的数量 0.30
电动自行车保有量 电动自行车的数量 0.83
私家车保有量 私家车的数量 1.02
), ArticleFig(id=1242142964269523457, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098716198408892, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
变量 VIF
年龄 1.133
性别 1.026
家庭规模 1.183
家庭年收入 1.361
父亲职业 1.236
父亲文化程度 1.802
母亲职业 1.280
母亲文化程度 1.829
自行车保有量 1.114
电动自行车保有量 1.224
私家车保有量 1.230
), ArticleFig(id=1242142964353409539, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098716198408892, language=CN, label=表2, caption=

多重共线性检验

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变量 VIF
年龄 1.133
性别 1.026
家庭规模 1.183
家庭年收入 1.361
父亲职业 1.236
父亲文化程度 1.802
母亲职业 1.280
母亲文化程度 1.829
自行车保有量 1.114
电动自行车保有量 1.224
私家车保有量 1.230
), ArticleFig(id=1242142964424712708, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098716198408892, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
自变量/因变量 出行目的地占比/% 出行陪伴方式占比/% 出行方式占比/% 出行距离占比/% 出行频率占比/%
性别 4.10 3.57 3.82 4.34 3.31
自行车保有量 5.20 4.18 6.17 4.82 5.39
私家车保有量 6.05 6.03 8.51 6.42 7.14
母亲文化程度 7.71 7.15 5.94 7.68 7.45
父亲文化程度 7.14 6.83 6.50 8.33 7.31
电动自行车保有量 7.19 7.02 11.50 6.65 7.58
家庭规模 11.01 8.72 9.44 10.59 10.82
母亲职业 12.10 9.77 10.13 11.68 10.46
父亲职业 11.86 10.15 11.79 12.51 11.20
家庭年收入 12.73 11.21 13.00 11.38 11.89
儿童年龄 14.90 25.37 13.21 15.61 17.45
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自变量对出行特征的重要性排序

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自变量/因变量 出行目的地占比/% 出行陪伴方式占比/% 出行方式占比/% 出行距离占比/% 出行频率占比/%
性别 4.10 3.57 3.82 4.34 3.31
自行车保有量 5.20 4.18 6.17 4.82 5.39
私家车保有量 6.05 6.03 8.51 6.42 7.14
母亲文化程度 7.71 7.15 5.94 7.68 7.45
父亲文化程度 7.14 6.83 6.50 8.33 7.31
电动自行车保有量 7.19 7.02 11.50 6.65 7.58
家庭规模 11.01 8.72 9.44 10.59 10.82
母亲职业 12.10 9.77 10.13 11.68 10.46
父亲职业 11.86 10.15 11.79 12.51 11.20
家庭年收入 12.73 11.21 13.00 11.38 11.89
儿童年龄 14.90 25.37 13.21 15.61 17.45
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年龄与儿童日常出行行为的非线性关系
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敖仪斌 1 , 龙毅 1, * , 唐雨欣 1 , 李海梅 2
科技导报 | 特色专题：绿色建筑与城市治理 2025,43(6): 65-73
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科技导报 |特色专题：绿色建筑与城市治理 2025 , 43 (6) : 65 -73
年龄与儿童日常出行行为的非线性关系
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敖仪斌1 , 龙毅1, * , 唐雨欣1, 李海梅2
作者信息
  • 1. 成都理工大学环境与土木工程学院, 成都 610059
  • 2. 成都理工大学文法学院, 成都 610059
通讯作者:
龙毅(通信作者), 硕士研究生, 研究方向为儿童发展, 电子信箱:
The nonlinear relationship between age and children's daily travel behavior
Yibin AO1 , Yi LONG1, * , Yuxin TANG1, Haimei LI2
Affiliations
  • 1. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
  • 2. Humanities and Law School, Chengdu University of Technology, Chengdu 610059, China
出版时间: 2025-03-28 doi: 10.3981/j.issn.1000-7857.2024.04.00321
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了解不同年龄阶段儿童的出行特征，对建设儿童友好型社区具有重要意义。以成都市双流区为例，开展儿童出行特征调查，利用随机森林模型，考察了儿童年龄与其日常出行行为的非线性关系。结果表明：儿童年龄是家庭社会人口特征中影响儿童出行行为最重要的因素，特别是在出行陪伴方式中，相对重要性达到了25.37%；儿童出行方式随年龄变化呈现非线性特征，儿童出行方式随年龄变得更独立和多样化；出行陪伴方式和目的地选择也呈现年龄非线性特征，特别是在14岁后，儿童更倾向于选择独自出行和朋友陪伴，同时出行距离也开始显著上升。

儿童年龄  /  出行行为  /  非线性  /  儿童友好型社区

Understanding the travel behavior of children at different age is crucial for building child friendly communities. In this article, by taking Shuangliu District in Chengdu as an example, a survey on children's travel characteristics was conducted, and random forest model was employed to explore the nonlinear relationship between children's age and their daily travel behavior. The results show: (1) Children's age is the most significant factor affecting their travel behavior among various social demographic and family characteristics, especially the choice of travel companionship, the relative importance of which is 25.37%; (2) The mode of travel for children exhibited nonlinear characteristics as they grow, and became more independent and diverse; (3) The choice of travel companionship and destinations also showed nonlinear characteristics with age. Particularly after the age of 14, children tended to choose to travel alone or with friends, with a significant increase in their travel distance.

children's age  /  travel behavior  /  nonlinear  /  child-friendly communities
敖仪斌, 龙毅, 唐雨欣, 李海梅. 年龄与儿童日常出行行为的非线性关系. 科技导报, 2025 , 43 (6) : 65 -73 . DOI: 10.3981/j.issn.1000-7857.2024.04.00321
Yibin AO, Yi LONG, Yuxin TANG, Haimei LI. The nonlinear relationship between age and children's daily travel behavior[J]. Science & Technology Review, 2025 , 43 (6) : 65 -73 . DOI: 10.3981/j.issn.1000-7857.2024.04.00321
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建设儿童友好城市已然成为当前社会各界所关注的热点话题[1−2]。近年来，中国经济快速发展和城市化率稳步上升，城市空间结构变化为城市居民带来便捷的同时，也引发了一些现实问题，如儿童出行安全度降低、游憩空间缩减、独立活动性降低等[3]。不同年龄阶段的儿童，在出行行为上存在显著差异，这些差异不仅涉及出行模式的选择，还包括陪伴方式和出行距离等多个方面。
关于儿童出行行为的研究，国内外研究人员已经进行了广泛讨论[4−6]，有研究表明，年龄较小的儿童在上学或进行休闲活动时更偏向于选择积极主动的出行方式[7]（如步行、骑自行车等），但是由于出行环境和父母对孩子安全的担忧等因素的影响，年龄较小的儿童大多是由父母陪伴出行[8−9]，而成年人出行方式更倾向于汽车出行[10]，导致儿童积极出行的频率降低。部分学者认为距离和安全是积极出行的重要影响因素[11]，在出行距离较短且路线安全的情况下，年龄较小的儿童才更有可能采用积极出行方式[12−13]。随着年龄的增长，儿童会具有更好的独立性和更高的安全意识[14]，父母对儿童出行的干预会相对减少，主要表现在儿童出行目的地、出行方式和出行陪伴方式的选择，从而同龄人结伴出行的现象就更为普遍，积极出行的概率提高，出行距离也会随之扩大。在出行距离较远时大龄儿童会更倾向于被动出行（如乘坐私家车、公共交通等机动车出行），对汽车使用的态度更加积极[7]，而积极出行的概率则会随之减少。人们普遍认为，适当的体育活动对儿童的身体及心理健康有重要影响[4]，有研究表明，经常运动的儿童患有心血管疾病的概率更低[15−16]，而积极出行与儿童的身体活动呈正相关[17]，所以研究儿童年龄与出行行为之间的关系，对儿童成长至关重要。
以往的研究，从线性的角度出发，主要采用传统参数统计模型来研究分析儿童的出行行为，常采用的方法有离散选择模型[18]、结构方程模型[19−20]、随机效应模型[21]等。例如Roman等[21]采用随机效应模型研究加拿大6~16岁青少年采用积极出行方式上学的变化情况，表明儿童使用主动交通工具（步行或骑行）上学的可能性在10岁之前更高，但随着儿童年龄的增长呈现一个负相关的趋势；Ikeda等[22]通过建立混合效应多项式logistic回归模型对儿童通学出行行为进行研究，发现年龄较大（12~14岁）的儿童积极出行的概率较高。然而，这些方法忽视了变量（年龄、家庭规模）对儿童出行行为的非线性影响。近年来，一些研究人员从非线性影响的角度出发进行了一些研究[23−25]，主要运用机器学习模型例如随机森林模型[26]、梯度提升决策树[27]等量化出行行为与各类变量之间的非线性关系。刘吉祥等[23]利用极限梯度提升模型研究建成环境对青少年步行通学的非线性影响，表明建成环境以及主要的社会经济属性变量（如性别、年龄、家庭规模等）对儿童出行存在非线性影响，并有着一定的阈值效应。机器学习模型在各领域的运用发展迅速，不需要基于严格的先验假设，在一定程度上改善了传统参数统计模型的局限性[28]，不仅可以分析儿童出行行为影响因素的相对重要性，还能更准确地描绘影响因素的非线性作用机制，使其研究结果更具有参考性。但在儿童友好城市建设背景下，探索儿童日常活动出行行为非线性特征的研究还非常有限。
本文以成都市双流区118个社区为例，针对该区域年龄在6~18岁的儿童进行问卷调查，获得了儿童的家庭人口特征和出行信息，并建立随机森林模型分析儿童年龄对日常出行的非线性影响，研究结果将有助于政策制定者和城市规划者更好地理解不同年龄阶段儿童的出行特征，为儿童友好城市建设和社区规划提供更好的理论参考。
1 数据
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1.1 研究区域和数据来源
选择四川省成都市双流区作为研究区域，双流区是成都市首批儿童友好社区建设的试点社区，具有典型的儿童友好城市建设特征和研究价值，能够提供丰富的数据和研究背景，有助于了解不同年龄阶段儿童的出行行为，为儿童友好社区的构建提供有益的经验和指导。
研究团队与四川省儿童保护与发展研究中心合作，针对双流区118个社区的6~18岁儿童开展了出行调查。采用电子问卷的方式，对双流区118个社区内的儿童进行问卷调查，儿童在家长的辅助下填写问卷，调查工作从2022年8月持续至11月，历时近4个月。在此期间，总共回收了3332份问卷。通过测谎题和突兀数据的筛选，最终获得了1127份有效问卷，包含了3227条可用的儿童出行数据，主要获得了儿童的家庭社会人口特征和儿童的出行信息。图 1展示了受访儿童家庭的空间位置分布，覆盖了双流区118个社区，具有全域代表性。
1.2 描述性统计分析
本文主要聚焦于出行目的地、出行频率、陪伴方式、出行距离和出行方式5个重要儿童日常活动出行行为特征。通过对这些因变量的分析，揭示不同年龄阶段儿童出行行为的非线性特征，为构建儿童友好社区提供理论参考。关注的社会人口统计学特征包括儿童的年龄、性别、家庭年收入等共计11个变量。从表 1中的描述性统计数据可以看出，样本中的儿童平均年龄约为9岁，男女比例相对均衡，家庭规模平均为4人。家庭年收入的分布较为广泛，涵盖了不同收入水平的家庭。父母的职业和文化程度存在显著差异，父母的职业范围涵盖了从无业到各类职业的广泛领域，而父母的文化程度主要集中在高中/中专及以上。另外，家庭拥有的自行车、电动自行车和私家车的数量相对较低。
2 研究方法
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采用随机森林模型，这是一种基于决策树的集成学习算法。该算法通过自助抽样和特征的随机选择，生成多棵决策树，并将它们的预测结果综合起来，得出最终预测结果[29]。随机森林模型具有强大的预测能力，能够实现高精度和泛化性能。Hagenauer等[30]发现，在7种不同的学习分类器中，随机森林在准确率上表现最优。与传统的线性模型相比，随机森林在处理分类问题时具有更好的预测性能[31]。在各个领域，包括交通、医疗和城市规划等[32−34]，随机森林模型都得到了广泛应用。特别是在处理非线性关联问题时，随机森林表现出色[35]。本研究运用随机森林模型来探究社会人口特征对儿童日常出行行为的非线性影响，以期更准确地揭示影响因素之间的复杂关系。
随机森林模型主要通过基尼指数进行特征重要性评估，假设有k个特征变量X1，X2，X3，...，Xk，I棵决策树，N个类别，VIMk（Gini）表示每个特征变量Xk的基尼指数得分。第i棵树节点m的基尼指数的计算公式如式（1）所示，其中N代表有N个类别，表Pmn示节点m中类别n所占的比例，n∈（1，2，···，N）。
$G I N I_m^{(i)}=1-\sum_{n=1}^{|\mathrm{N}|} P_{m n}^2$
特征变量Xk在第i棵树节点m的重要性，即节点m分枝前后的基尼指数变化量为
$V I M_k^{(\mathrm{Gini})(i)}=G I N I_m^{(i)}-G I N I_l^{(i)}-G I N I_r^{(i)}$
式中，GINI（li）和GINI（ri）分别表示分枝后2个新节点的基尼指数。如果特征变量Xk在第i棵树中出现M次，则变量在第i棵树的重要性为：
$V I M_k^{(\text {Gini })}=\sum_{m=1}^M V I M_{k m}^{(\text {Gini })(i)}$
最后经过归一化处理，特征变量Xk在随机森林模型中的基尼指数重要性定义为
$V I M_k^{(\text {Gini })}=\frac{\sum_{i=1}^I \sum_{m=1}^M V I M_{k m}^{(\text {Gini })(i)}}{\sum_{k^{\prime}}^K V I M_{k^{\prime}}^{(\text {Gini })}}$
本研究将部分依赖图（partial dependence plots，PDP）应用于可视化解析变量与儿童出行方式选择之间的非线性关系。使用部分依赖图可以展示自变量对因变量的边际效应，相比传统的统计模型，这种方法不需要先验假设，更能准确反映自变量与因变量之间的关系。特征变量偏相关计算方法为
$\hat{\phi}\left(X_1\right)=\frac{1}{T} \sum_{i=1}^{\mathrm{T}} f\left(X_1, X_{i 2}, \ldots, X_{i j}\right)$
式中，因变量为f（X），$\hat{\varphi}$（X1）为特征变量X1的偏相关函数，T表示数据集中的实例数，X1，Xi2，...，Xij为样本i的第j个特征的实际观测值。
3 研究结果与讨论
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为提高模型的表现，采用网格搜索技术[36]对随机森林3个关键的参数进行调整：最大树深度、单棵树特征数和树的数量。具体步骤如下：预设参数范围，最大树深度设定为1~20，单棵树特征数设定为2~10，树的数量设定为从10~1000（以10为步长）；接着，对18000（20×9×100）种可能组合进行评估，并通过袋外误差测试模型性能[37]。经过测试后，发现当最大深度为16、特征数为2，以及树的数量为110时，模型性能表现最优，根据此最优模型展开后续分析。
为检验解释变量之间的相互依存关系，本研究在建模之前进行了多重共线性检验。采用Pearson相关检验探讨变量之间的共线性。模型中所有自变量的方差膨胀因子（VIF）值均小于5，如表 2所示，表明本研究选取的影响因素之间不存在显著多重共线性。
3.1 自变量的相对重要性
表 3和图 2展示了儿童的家庭社会人口特征对其出行特征的相对重要性，其中，“儿童年龄”在所有出行特征中的相对重要性最高，特别是在“出行陪伴方式”中，其重要性达到了25.37%。随着儿童年龄的增加，他们更倾向于与同龄人一起或独自出行，而不是依赖家长或其他成人。这与Ma等[38]的研究结果一致，他们发现家庭的社会经济和文化背景对儿童的情感和行为有着显著的影响。“家庭年收入”对儿童“出行方式”和“出行频率”影响的相对重要性分别为13.00% 和11.89%，这意味着家庭的经济实力对儿童的出行方式和频率有重要影响。经济条件更好的家庭可能更偏向于使用私家车。“家庭规模”在“出行方式”和“出行频率”中的相对重要性分别为9.44% 和10.82%。这反映了家庭成员较多的家庭会选择特定的出行方式，并且出行频率也相对较高。“父母的职业和文化程度”在所有出行特征中都显示出了较高的相对重要性，这说明父母的职业和教育背景对儿童的出行方式和频率有显著的影响。至于“交通工具的保有量”，如自行车、私家车和电动自行车的保有量情况对出行方式都有显著的影响，因为拥有交通工具可以提高出行的可选择性。总之，儿童的出行特征受到多种社会人口因素的综合影响，其中儿童的年龄、家庭的经济状况、家庭规模、父母的职业和文化程度，以及交通工具的保有量都是决定性的关键因素。
3.2 儿童年龄与其出行行为的非线性关系解析
图 3展示了儿童年龄与儿童出行行为的非线性关系，横轴表示年龄的取值，纵轴表示出行行为测度变量的倾向值。图 3中的部分依赖图揭示了不同年龄阶段儿童对各种出行行为的偏好变化。
在图 3（a）中，儿童年龄对出行方式选择产生了显著的非线性影响。随着年龄的增长，步行作为首选交通方式的偏好呈现出逐渐下降的趋势，尤其是在7~10岁这一关键发展阶段，但在10~13岁，步行的偏好又有所回升，随后在13岁以后再度下降。这一波动与儿童活动范围的扩大及步行便利性的相对降低有关。同时，随着年龄的增长，选择自行车作为首选交通方式的概率显著提升，特别是在9岁以后，反映了儿童对自行车操作技能的掌握以及对独立出行需求的增长[39]。公交车作为出行方式的选择概率虽然整体上随年龄增长而提高，但增长速度相对缓慢，与儿童对公共交通的熟悉度和可访问性的逐步提升有关。私家车作为出行方式的选择概率在儿童年龄增长过程中呈现出下降趋势，特别是在8岁之后更为明显，父母也更多地鼓励儿童独立出行[40]。电动自行车的选择倾向在儿童年龄增长过程中呈现先降后升的趋势，在儿童年龄较小时，父母可能更倾向于使用电动自行车带领孩子出行，而随着儿童年龄的增长，尽管儿童对独立出行的需求增加，但出于安全考虑，父母通常不会允许儿童单独骑电动自行车[41]。
图 3（b）展现了儿童年龄对出行陪伴方式选择的非线性影响。随着年龄的增长，独自一人出行的概率逐渐增加，特别是在14岁之后，增长速度加快，反映了青少年对独立出行需求的增加以及自我能力的提升[42]。父母陪伴出行的概率随年龄增长而显著下降，儿童逐渐获得更多自主性出行。祖父母陪伴的概率在整个年龄段内相对稳定，但在整体比例中占据较小部分，与家庭结构和文化背景有关，祖父母在日常生活中的参与度不如父母[39]。朋友陪伴的概率在8岁之后开始显著增加，与学龄儿童社交圈的扩大和社交需求的增长有关。而其他陪伴方式的概率在整个年龄段内变化不大，表明除了家庭成员和朋友外，其他陪伴方式在儿童出行中的影响较小。
图 3（c）关注儿童年龄对出行目的地选择的非线性影响。随着年龄的增长，儿童对综合服务设施的偏好逐渐增加，尤其在17岁以后增长迅速，与青少年对多元化服务需求的增加有关[43]。公共文化设施的偏好在整个年龄段内变化不大，而医疗健康设施的偏好在6~8岁有所增加，之后趋于稳定。反映了儿童在成长的早期阶段，对医疗健康服务的需求较高。公共教育设施的偏好在6~14岁逐渐增加，14岁之后趋于平稳，这与学龄儿童对教育资源的需求增长有关。休闲购物设施的偏好在6岁时最高，随后逐渐下降，这与儿童随着年龄的增长，对购物和休闲活动的兴趣转向更多样化的休闲活动有关，休闲活动选择是一个复杂的决策过程，受到家庭生命周期阶段的影响[44]。
图 3（d）揭示了儿童年龄对出行频率和距离的非线性影响。随着年龄的增长，出行距离呈现出明显的上升趋势。这一趋势与儿童到青少年过渡期间社交圈的扩大和参与更多社会活动有关[45]。此外，出行频率的数值随年龄增长而增加，这意味着儿童随着年龄的增长，实际上是减少了对某些特定常去目的地的访问频率。尤其是在15岁之后，出行频率的数值显著增加，这表明这个年龄段的儿童开始减少对一些常去地点的访问频率[46]。这些变化与儿童的生活方式和社会活动的变化有关。随着年龄的增长，儿童更倾向于独立出行，并探索更远的地点，从而减少了对某些常去地点的频繁访问。同时，这也反映了儿童对独立性的追求和生活方式的变化，他们更倾向于参与其他形式的身体活动，而不是频繁地访问特定的地点[47]。
4 结论
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本研究采用机器学习中的随机森林模型，探讨了在儿童友好社区建设背景下，儿童年龄与儿童出行行为之间的非线性关系，避免了传统线性模型中固有的函数关系假设限制。通过实证分析，为城市规划和交通政策制定提供借鉴，尤其是在将儿童视为城市交通系统一个不可或缺的部分时。主要结论如下。
1）家庭社会人口特征中，儿童年龄在所有出行特征中相对重要性最高。在出行陪伴方式的相对重要性中占比达到25.37%。
2）儿童的出行方式选择随年龄的增长显示出非线性变化，步行偏好在7~10岁下降，10~13岁回升，之后再下降，而自行车和公交车的选择概率随年龄增长而提高，私家车选择概率下降，显示出随着年龄增长儿童趋向于更独立和多样化的出行方式。
3）儿童的出行陪伴方式和目的地选择也随年龄呈现非线性变化。独自出行的概率随着年龄的增长而提升，父母陪同的概率则下降，这反映了儿童独立性的增强。同时，对综合服务设施的偏好随年龄增长而增加，尤其在青少年时期，而对教育和医疗设施的偏好在特定年龄段有所提升。这些变化与儿童的社交需求、独立性追求和生活方式的变化有关。出行距离整体呈上升趋势，尤其是在青少年时期，与社交圈的扩大和参与更多社会活动有关。
本研究不仅为双流区儿童友好社区建设提供了实证数据支持，也为儿童出行行为的研究提供了新的视角。然而，本研究也存在一定的局限性，主要集中在成都市双流区的研究结果可能限制了其普遍性。未来的研究可以进一步探讨不同地区、不同文化背景下的儿童出行行为差异，并探讨儿童个体特征如性格、健康状况对出行行为的影响。
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  • 国家自然科学基金面上项目(72171028)
  • 中国博士后科学基金特别资助项目(2022T150077)
  • 中国博士后科学基金面上项目(2022M710496)
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2025年第43卷第6期
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doi: 10.3981/j.issn.1000-7857.2024.04.00321
  • 接收时间：2024-04-12
  • 首发时间：2025-06-29
  • 出版时间：2025-03-28
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  • 收稿日期：2024-04-12
  • 修回日期：2025-01-15
基金
国家自然科学基金面上项目(72171028)
中国博士后科学基金特别资助项目(2022T150077)
中国博士后科学基金面上项目(2022M710496)
作者信息
    1. 成都理工大学环境与土木工程学院, 成都 610059
    2. 成都理工大学文法学院, 成都 610059

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龙毅(通信作者), 硕士研究生, 研究方向为儿童发展, 电子信箱:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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