Article(id=1245407862948741653, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156262727438951343, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2402765, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1713196800000, receivedDateStr=2024-04-16, revisedDate=1717516800000, revisedDateStr=2024-06-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1774857973086, onlineDateStr=2026-03-30, pubDate=1741363200000, pubDateStr=2025-03-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1774857973086, onlineIssueDateStr=2026-03-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774857973086, creator=13701087609, updateTime=1774857973086, updator=13701087609, issue=Issue{id=1156262727438951343, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='7', pageStart='2193', pageEnd='3077', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753604116544, creator=13701087609, updateTime=1753771263994, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1156963794699248405, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156262727438951343, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1156963794699248406, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156262727438951343, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3018, endPage=3025, ext={EN=ArticleExt(id=1245407863624024657, articleId=1245407862948741653, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Evaluation Model of On-street Parking Efficiency, columnId=1156262728772735295, journalTitle=Science Technology and Engineering, columnName=Papers·Traffics and Transportations, runingTitle=null, highlight=null, articleAbstract=

The evaluation of on-street parking efficiency is considered a necessary basis for the optimization and management of on-street parking as well as the development of urban intelligent parking systems. Therefore, an evaluation method of on-street parking efficiency based on the entropy weight Critic-Topsis model was proposed. Firstly, nine indexes, such as the number of parking spaces, the remaining width of the road, and the cumulative number of parking, were selected from three aspects: parking space attributes, operating characteristics, and traffic impact, to construct the evaluation index system of on-street parking efficiency. Secondly, an evaluation method of the entropy weight Critic-Topsis model was proposed based on the data characteristics of the evaluation index of on-street parking efficiency. The entropy weight method and Critic method were employed to calculate the weight of each index, and then the improved topsis model was utilized to quantitatively evaluate the parking lots. Finally, this model was utilized to analyze the data of 10 on-street parking lots in Beijing, resulting in the evaluation results and rankings for each parking lot. It is found that in the evaluation index system of on-street parking efficiency, the number of parking spaces, the utilization rate of parking spaces, and the remaining width of the road have significant influences on the evaluation results, with weights of 22.99%, 13.72%, and 13.71% respectively. In the case analysis of 10 on-street parking lots in Beijing, higher scores are observed for on-street parking lots such as Gongti East Road, Keyuan Road, and Huayuan North Road, which are 0.659 4, 0.611 3, and 0.608 1, respectively. Moreover, relevant suggestions and optimization measures are provided for the on-street parking lots of Chunhe Road and Lingnan Road, which have lower scores, aiming to enhance the long-term management system of urban on-street parking and improve the level of urban traffic management.

, correspAuthors=She-qiang MA, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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, authorCompany=null, fund=null, authors=null, authorsList=Dong-fang WANG, She-qiang MA, Jing TIAN, Xiao-xu WANG), CN=ArticleExt(id=1245407865641485046, articleId=1245407862948741653, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=路内停车效率评价模型, columnId=1156262730664366426, journalTitle=科学技术与工程, columnName=论文·交通运输, runingTitle=null, highlight=null, articleAbstract=

路内停车效率评价是开展路内停车优化与管理、建设城市智慧停车系统的必要依据。为此,提出一种基于熵权Critic-Topsis模型的路内停车效率评价方法。首先,从停车位属性、运营特性、交通影响3个方面选取停车位数量、道路剩余宽度、累计停车数等9个指标构建路内停车效率评价指标体系。其次,针对路内停车效率评价指标的数据特点,提出熵权Critic-Topsis模型的评价方法,使用熵权法和Critic法综合计算各指标权重,再利用改进的Topsis模型对停车场进行量化评价。最后,以此模型对北京市10个路内停车场数据进行实例分析,得到各个路内停车场评价结果以及排名。研究结果表明:在路内停车效率评价指标体系中,停车位数量、停车位利用率和道路剩余宽度对评价结果影响较大,权重分别为22.99%,13.72%和13.71%;在对北京市10个路内停车场实例分析中,工体东路、科苑路、花园北路等路内停车场得分较高,分别为0.659 4、0.611 3、0.608 1,并且对得分较低的春和路和岭南路路内停车场给出了相关建议和优化措施,以期完善城市路内停车长效管理体系,提高城市交通管理水平。

, correspAuthors=马社强, authorNote=null, correspAuthorsNote=
* 马社强(1973—),男,汉族,陕西宝鸡人,博士,副教授。研究方向:交通安全。E-mail:
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王东方(2002—),男,汉族,河南周口人,硕士研究生。研究方向:交通安全。E-mail:

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王东方(2002—),男,汉族,河南周口人,硕士研究生。研究方向:交通安全。E-mail:

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Journal of Transport Information and Safety, 2014, 32 (2): 68-71., articleTitle=An evaluation system of the parking operation index, refAbstract=null)], funds=[Fund(id=1245407870225858785, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, awardId=2023SYL09, language=CN, fundingSource=中国人民公安大学交通管理工程双一流创新研究专项(2023SYL09), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1245407865956057879, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, xref=1, ext=[AuthorCompanyExt(id=1245407865960252184, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, companyId=1245407865956057879, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 School of Traffic Management, People’s Public Security University of China, Beijing 100038, China), AuthorCompanyExt(id=1245407865968640794, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, companyId=1245407865956057879, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 中国人民公安大学交通管理学院, 北京 100038)]), AuthorCompany(id=1245407866086081323, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, xref=2, ext=[AuthorCompanyExt(id=1245407866094469932, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, companyId=1245407866086081323, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Beijing Traffic Management Bureau, Beijing 100037, China), AuthorCompanyExt(id=1245407866102858542, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, companyId=1245407866086081323, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 北京市公安局交通管理局, 北京 100037)])], figs=[ArticleFig(id=1245407868556525633, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Fig.1, caption=On-street parking efficiency evaluation system, figureFileSmall=n/YaAPs5mRQxqRut4+s0vg==, figureFileBig=V8IWymWsFiShUacaWjw8/w==, tableContent=null), ArticleFig(id=1245407868627828810, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=图1, caption=路内停车效率评价体系, figureFileSmall=n/YaAPs5mRQxqRut4+s0vg==, figureFileBig=V8IWymWsFiShUacaWjw8/w==, tableContent=null), ArticleFig(id=1245407868850126946, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Fig.2, caption=On-street parking location distribution map, figureFileSmall=ewa8NShp3MRjV0cGuH4lAw==, figureFileBig=2Q+TmlqzymJAivcGZgIQQw==, tableContent=null), ArticleFig(id=1245407868959178860, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=图2, caption=路内停车场地理位置分布图, figureFileSmall=ewa8NShp3MRjV0cGuH4lAw==, figureFileBig=2Q+TmlqzymJAivcGZgIQQw==, tableContent=null), ArticleFig(id=1245407869043064951, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Table 1, caption=

Location and basic characteristics of on-street parking lot

, figureFileSmall=null, figureFileBig=null, tableContent=
停车场
道路名称
行政
区域
数量/个 道路横
断面
单/双侧 周围用
地性质
安贞路 朝阳区 79 四幅路 双侧 医院类
工体北路 朝阳区 77 四幅路 双侧 娱乐类
工体东路 朝阳区 82 四幅路 双侧 娱乐类
春和路 大兴区 255 单幅路 双侧 商务类
金苑路 大兴区 233 双幅路 双侧 商务类
科苑路 大兴区 174 四幅路 双侧 住宅类
盛坊路 大兴区 216 四幅路 双侧 住宅类
樊家村路 丰台区 214 四幅路 双侧 住宅类
花园北路 海淀区 109 四幅路 双侧 住宅类
岭南路 海淀区 186 单幅路 双侧 学校类
), ArticleFig(id=1245407869135339646, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=表1, caption=

路内停车场位置与基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
停车场
道路名称
行政
区域
数量/个 道路横
断面
单/双侧 周围用
地性质
安贞路 朝阳区 79 四幅路 双侧 医院类
工体北路 朝阳区 77 四幅路 双侧 娱乐类
工体东路 朝阳区 82 四幅路 双侧 娱乐类
春和路 大兴区 255 单幅路 双侧 商务类
金苑路 大兴区 233 双幅路 双侧 商务类
科苑路 大兴区 174 四幅路 双侧 住宅类
盛坊路 大兴区 216 四幅路 双侧 住宅类
樊家村路 丰台区 214 四幅路 双侧 住宅类
花园北路 海淀区 109 四幅路 双侧 住宅类
岭南路 海淀区 186 单幅路 双侧 学校类
), ArticleFig(id=1245407869277945995, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Table 2, caption=

Average value of evaluation index of on-street parking lot

, figureFileSmall=null, figureFileBig=null, tableContent=
停车场名称 C1 C2 C3 C4 C5 C6 C7 C8 C9
安贞路 79.00 24.00 440.00 5.99 0.79 0.56 105.20 33.78 31.42
花园北路 109.00 13.80 475.00 4.49 0.58 0.60 101.87 34.98 33.95
工体东路 82.00 24.20 184.00 2.24 0.16 0.30 54.34 13.11 13.11
工体北路 77.00 23.80 570.00 7.97 0.82 0.72 82.00 43.84 40.69
春和路 255.00 12.20 746.00 2.92 0.40 0.29 107.40 53.26 50.81
金苑路 233.00 8.60 890.00 3.82 0.27 0.25 55.30 63.56 62.26
科苑路 174.00 7.60 688.00 3.95 0.26 0.24 51.74 49.11 47.93
盛坊路 216.00 16.80 287.00 1.33 0.24 0.16 138.83 20.47 19.39
樊家村路 214.00 28.80 375.00 1.75 0.41 0.13 183.56 26.76 24.91
岭南路 186.00 6.80 486.00 2.61 0.98 0.38 328.84 34.71 29.80
), ArticleFig(id=1245407869391192218, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=表2, caption=

路内停车场评价指标平均值

, figureFileSmall=null, figureFileBig=null, tableContent=
停车场名称 C1 C2 C3 C4 C5 C6 C7 C8 C9
安贞路 79.00 24.00 440.00 5.99 0.79 0.56 105.20 33.78 31.42
花园北路 109.00 13.80 475.00 4.49 0.58 0.60 101.87 34.98 33.95
工体东路 82.00 24.20 184.00 2.24 0.16 0.30 54.34 13.11 13.11
工体北路 77.00 23.80 570.00 7.97 0.82 0.72 82.00 43.84 40.69
春和路 255.00 12.20 746.00 2.92 0.40 0.29 107.40 53.26 50.81
金苑路 233.00 8.60 890.00 3.82 0.27 0.25 55.30 63.56 62.26
科苑路 174.00 7.60 688.00 3.95 0.26 0.24 51.74 49.11 47.93
盛坊路 216.00 16.80 287.00 1.33 0.24 0.16 138.83 20.47 19.39
樊家村路 214.00 28.80 375.00 1.75 0.41 0.13 183.56 26.76 24.91
岭南路 186.00 6.80 486.00 2.61 0.98 0.38 328.84 34.71 29.80
), ArticleFig(id=1245407869479272609, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Table 3, caption=

Descriptive analysis results table of original data

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 样本量 最小值 最大值 平均值 标准差 中位数 变异系数/%
C1 50 77.00 255.00 162.50 65.84 180.00 40.52
C2 50 6.80 28.80 16.66 7.63 15.30 45.77
C3 50 184.00 890.00 514.10 204.00 480.50 39.68
C4 50 1.33 7.97 3.71 1.94 3.37 52.25
C5 50 0.16 1.10 0.50 0.29 0.40 58.56
C6 50 0.13 0.72 0.36 0.19 0.30 51.81
C7 50 51.74 328.84 120.91 79.57 103.54 65.81
C8 50 13.11 63.56 37.36 14.60 34.85 39.07
C9 50 13.11 62.26 35.43 14.34 32.69 40.48
), ArticleFig(id=1245407869596713129, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=表3, caption=

原始数据描述性分析结果表

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 样本量 最小值 最大值 平均值 标准差 中位数 变异系数/%
C1 50 77.00 255.00 162.50 65.84 180.00 40.52
C2 50 6.80 28.80 16.66 7.63 15.30 45.77
C3 50 184.00 890.00 514.10 204.00 480.50 39.68
C4 50 1.33 7.97 3.71 1.94 3.37 52.25
C5 50 0.16 1.10 0.50 0.29 0.40 58.56
C6 50 0.13 0.72 0.36 0.19 0.30 51.81
C7 50 51.74 328.84 120.91 79.57 103.54 65.81
C8 50 13.11 63.56 37.36 14.60 34.85 39.07
C9 50 13.11 62.26 35.43 14.34 32.69 40.48
), ArticleFig(id=1245407869680599218, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Table 4, caption=

Calculate the weight of each index layer

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 权重/%
C1 C2 C3 C4 C5 C6 C7 C8 C9
熵权法 15.49 14.06 8.86 13.29 14.89 13.64 4.73 7.71 7.33
Critic权重法 16.79 11.04 11.67 8.91 10.43 9.62 10.49 10.51 10.54
组合权重 22.99 13.71 9.14 10.47 13.72 11.59 4.39 7.17 6.82
), ArticleFig(id=1245407869785456825, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=表4, caption=

各指标计算权重

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 权重/%
C1 C2 C3 C4 C5 C6 C7 C8 C9
熵权法 15.49 14.06 8.86 13.29 14.89 13.64 4.73 7.71 7.33
Critic权重法 16.79 11.04 11.67 8.91 10.43 9.62 10.49 10.51 10.54
组合权重 22.99 13.71 9.14 10.47 13.72 11.59 4.39 7.17 6.82
), ArticleFig(id=1245407869919674564, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=EN, label=Table 5, caption=

The scoring and ranking of Topsis method

, figureFileSmall=null, figureFileBig=null, tableContent=
评价对象 正理想解
距离D+
负理想解
距离D-
最终评分M 排序结果
安贞路 0.195 6 0.247 7 0.558 8 4
花园北路 0.155 4 0.241 2 0.608 1 3
工体东路 0.157 1 0.304 2 0.659 4 1
工体北路 0.224 4 0.245 8 0.522 8 5
春和路 0.252 2 0.201 9 0.444 7 9
金苑路 0.230 4 0.231 8 0.501 5 7
科苑路 0.158 1 0.248 6 0.611 3 2
盛坊路 0.210 9 0.230 7 0.522 4 6
樊家村路 0.240 3 0.203 7 0.458 8 8
岭南路 0.215 8 0.210 2 0.439 4 10
), ArticleFig(id=1245407870074863827, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1245407862948741653, language=CN, label=表5, caption=

Topsis法评分及排序

, figureFileSmall=null, figureFileBig=null, tableContent=
评价对象 正理想解
距离D+
负理想解
距离D-
最终评分M 排序结果
安贞路 0.195 6 0.247 7 0.558 8 4
花园北路 0.155 4 0.241 2 0.608 1 3
工体东路 0.157 1 0.304 2 0.659 4 1
工体北路 0.224 4 0.245 8 0.522 8 5
春和路 0.252 2 0.201 9 0.444 7 9
金苑路 0.230 4 0.231 8 0.501 5 7
科苑路 0.158 1 0.248 6 0.611 3 2
盛坊路 0.210 9 0.230 7 0.522 4 6
樊家村路 0.240 3 0.203 7 0.458 8 8
岭南路 0.215 8 0.210 2 0.439 4 10
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路内停车效率评价模型
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王东方 1 , 马社强 1, * , 田婧 1 , 王晓旭 2
科学技术与工程 | 论文·交通运输 2025,25(7): 3018-3025
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科学技术与工程 | 论文·交通运输 2025, 25(7): 3018-3025
路内停车效率评价模型
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王东方1 , 马社强1, * , 田婧1, 王晓旭2
作者信息
  • 1 中国人民公安大学交通管理学院, 北京 100038
  • 2 北京市公安局交通管理局, 北京 100037
  • 王东方(2002—),男,汉族,河南周口人,硕士研究生。研究方向:交通安全。E-mail:

通讯作者:

* 马社强(1973—),男,汉族,陕西宝鸡人,博士,副教授。研究方向:交通安全。E-mail:
Evaluation Model of On-street Parking Efficiency
Dong-fang WANG1 , She-qiang MA1, * , Jing TIAN1, Xiao-xu WANG2
Affiliations
  • 1 School of Traffic Management, People’s Public Security University of China, Beijing 100038, China
  • 2 Beijing Traffic Management Bureau, Beijing 100037, China
出版时间: 2025-03-08 doi: 10.12404/j.issn.1671-1815.2402765
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路内停车效率评价是开展路内停车优化与管理、建设城市智慧停车系统的必要依据。为此,提出一种基于熵权Critic-Topsis模型的路内停车效率评价方法。首先,从停车位属性、运营特性、交通影响3个方面选取停车位数量、道路剩余宽度、累计停车数等9个指标构建路内停车效率评价指标体系。其次,针对路内停车效率评价指标的数据特点,提出熵权Critic-Topsis模型的评价方法,使用熵权法和Critic法综合计算各指标权重,再利用改进的Topsis模型对停车场进行量化评价。最后,以此模型对北京市10个路内停车场数据进行实例分析,得到各个路内停车场评价结果以及排名。研究结果表明:在路内停车效率评价指标体系中,停车位数量、停车位利用率和道路剩余宽度对评价结果影响较大,权重分别为22.99%,13.72%和13.71%;在对北京市10个路内停车场实例分析中,工体东路、科苑路、花园北路等路内停车场得分较高,分别为0.659 4、0.611 3、0.608 1,并且对得分较低的春和路和岭南路路内停车场给出了相关建议和优化措施,以期完善城市路内停车长效管理体系,提高城市交通管理水平。

交通管理工程  /  路内停车  /  熵权-Critic赋权法  /  Topsis 评价法

The evaluation of on-street parking efficiency is considered a necessary basis for the optimization and management of on-street parking as well as the development of urban intelligent parking systems. Therefore, an evaluation method of on-street parking efficiency based on the entropy weight Critic-Topsis model was proposed. Firstly, nine indexes, such as the number of parking spaces, the remaining width of the road, and the cumulative number of parking, were selected from three aspects: parking space attributes, operating characteristics, and traffic impact, to construct the evaluation index system of on-street parking efficiency. Secondly, an evaluation method of the entropy weight Critic-Topsis model was proposed based on the data characteristics of the evaluation index of on-street parking efficiency. The entropy weight method and Critic method were employed to calculate the weight of each index, and then the improved topsis model was utilized to quantitatively evaluate the parking lots. Finally, this model was utilized to analyze the data of 10 on-street parking lots in Beijing, resulting in the evaluation results and rankings for each parking lot. It is found that in the evaluation index system of on-street parking efficiency, the number of parking spaces, the utilization rate of parking spaces, and the remaining width of the road have significant influences on the evaluation results, with weights of 22.99%, 13.72%, and 13.71% respectively. In the case analysis of 10 on-street parking lots in Beijing, higher scores are observed for on-street parking lots such as Gongti East Road, Keyuan Road, and Huayuan North Road, which are 0.659 4, 0.611 3, and 0.608 1, respectively. Moreover, relevant suggestions and optimization measures are provided for the on-street parking lots of Chunhe Road and Lingnan Road, which have lower scores, aiming to enhance the long-term management system of urban on-street parking and improve the level of urban traffic management.

traffic management engineering  /  on-street parking  /  entropy weight-critic weighting method  /  topsis evaluation method
王东方, 马社强, 田婧, 王晓旭. 路内停车效率评价模型. 科学技术与工程, 2025 , 25 (7) : 3018 -3025 . DOI: 10.12404/j.issn.1671-1815.2402765
Dong-fang WANG, She-qiang MA, Jing TIAN, Xiao-xu WANG. Evaluation Model of On-street Parking Efficiency[J]. Science Technology and Engineering, 2025 , 25 (7) : 3018 -3025 . DOI: 10.12404/j.issn.1671-1815.2402765
随着中国机动车保有量迅速增加,城市面临停车位供需不平衡的问题,尤其是停车难成为城市交通管理的一大难题。路内停车位因其施划简单、成本低、易于操作等特点,成为很多城市缓解停车难的一种方式。然而,在实践中,路内停车位的设置常常存在不合理之处,管理方式也相对落后。通过这一量化评价模型,公安交通管理部门能够对现有的路内停车位进行科学评估和优化调整,从而有效解决城市停车难的问题,这对于完善城市交通管理体系,提高城市管理水平具有重要意义。
现有的城市路内停车研究以对路内停车规划、交通影响评价、定价收费等为主,对于路内停车效率评价的研究相对较少。
首先,研究者们运用仿真模型,通过集成新的行为定价公式,在现实动态框架中分析不同停车政策的影响。该模型能够在考虑旅行时间的同时,动态寻求最大化系统中所有参与者的总效用[1],并且通过具体仿真实验优化了停车位布置方式,提高了停车管理的科学性和实用性[2]。此外,基于特定项目的研究建立了停车占用率与寻找停车位的概率和巡航次数之间的关系,提出通过控制最优停车位占用率可以显著减少巡游交通量[3]
另一项研究通过自动停车交易数据的实证分析,探讨了路内停车政策实施前后的各项指标变化,同时通过使用特定的分析框架和方法,如K均值最小二乘法,对不同区域的路边停车需求进行了量化分析,特别适用于新开发区域的停车时长和需求估算[4],
研究还有对停车费用调整对停车特性影响的分析,结果显示停车费增加可以有效降低停车时长,对停车周转率的短期影响不显著,但可以减少停车需求。尤其是商业区对价格变化的敏感度远高于住宅和办公区,建议周期性调整停车费并配合其他措施以有效管理停车需求和缓解交通拥堵[5-6],并且有研究探讨了共享停车泊位的分配和定价问题,通过构建非线性规划模型和遗传算法,实现了停车资源的高效利用和收益最大化[7]
在路内停车效率评价方面,研究者们采用对城市路内停车系统进行了综合评估,并提出了针对停车违法问题的管理策略,这些策略已在实际城市中进行了应用效果分析[8-10]。此外,还研究了特定城市中心区域的路内停车现状和存在的问题,提出了面向需求的管理策略[11-12]。针对商业区的停车管理水平,研究者利用层次分析法和模糊综合评价法求解评价模型[13]
熵权法基于信息熵原理,认为指标变异性越小,其所携带的信息量越少,因此权重越低,但存在忽视指标重要性的问题[14]
Critic法则侧重于通过数据的波动性和指标间的冲突性来确定权重,其中波动性通过标准差表示,冲突性通过相关系数判断,以此确保指标权重的客观性和数据波动及相关性的综合考虑。但未充分考虑指标间离散程度[15]
为实现准确的路内停车效率评价,提出一种熵权Critic-Topsis模型。该模型融合熵权法和Critic法的优势,并引入Topsis模型,基于各评价对象与理想解的接近度进行排序,以寻找最优方案。该方法既能避免Topsis方法中指标权重相同的假设,通过熵权Critic方法确定的组合权重被引入决策矩阵,使得评价结果更加真实反映了路内停车位设置的合理性。
本文提出的熵权Critic-Topsis 模型能为路内停车位效率的评价问题提供量化评分这一形式的解决方案。这项研究可为中国及其他城市制定有效的停车政策和管理策略提供宝贵的参考。
指标体系建立需要遵循5个原则:①系统性:确保指标体系构建具有逻辑完整性和内在一致性。②独立性:选择具有代表性和互相独立性的指标,避免重复或相互影响。③同向性:确保指标数值与评价对象的特征和程度一致,反映出优劣。④可比、可操作、可量化性:确保指标具备简洁易懂、易收集、可比较、可量化的特点,方便数学计算和分析。⑤全面性:尽可能涵盖相关的评价指标,以获取更全面和综合的评价结果。
针对国家规范以及文献[16]进行梳理,综合考虑停车场规划者、管理者、使用者等多方面意见和需求建立停车场评价指标体系,并给出各个指标属性。评价指标体系包括停车位属性、运营特性、交通影响三方面,共9个评价指标。指标体系的整体框架及具体指标明细见图1
评价路内停车位属性可采用以下指标:
(1)停车位数量:指某一路内停车场停车位总数,用于评价该路内停车场的最大容量。
(2)车行道路面剩余宽度:指车行道被路内停车位占据后剩余的横向富裕空间,这里规定其计算方式为车行道宽度减去垂直车行道方向的停车位宽度,用于评价对道路条件的改变程度。
评价路内停车运营特性可采用以下指标:
(1)累积停车数:指在一定时间内停车设施所累积实际停放车辆数,用于评价一定时间内停车设施被使用情况。
(2)停放周转率:指调查时间内每个停车位的停车次数,用于评价路内停车位的利用效率,其计算公式为
α=$\frac{S}{C}$
式(1)中:S为调查时间实际停车数量,辆;C为停车位数量,个。
(3)停车位利用率:指调查时间内停车位是否被车辆占用以及被占用的时长,用于评价停车位被使用强度,其计算公式为
U=$\frac{\sum _{i=1}^{S}{t}_{i}}{TC}$×100%
式(2)中:ti为第i辆车停车时间,min;T为调查时间,min。
(4)停车集中指数:指某一时刻路内停车的实际停车数量与其停车能力的比值,分为高峰小时停车集中指数与平均停车集中指数,用于评价某一时刻的停车场拥挤程度。此处选择高峰小时停车集中指数λ作为评价指标,其计算公式为
λ=$\frac{{N}_{j}}{C}$
式(3)中:Nj为高峰时段停车数量,辆。
(5)平均停车时间:其长短可以反映一个停车处的交通负荷和周转率,其计算公式为
$\stackrel{-}{t}$=$\frac{\sum _{i=1}^{S}{t}_{i}}{S}$
式(4)中:$\stackrel{-}{t}$为平均停车时间,min。
评价路内停车位对道路交通的影响可采用以下指标。
(1)停放车辆到达流率:指单位时间内驶入路内停车场的车流量。
(2)停放车辆驶出流率:指单位时间内驶出路内停车场的车辆数。
本节提出一种基于熵权Critic-Topsis的路内停车效率评价模型,共包括熵权Critic组合权重模型和Topsis评价模型两部分。各评价指标权重通过熵权Critic综合模型计算实现;各路内停车场利用Topsis评价模型实现,最终获取路内停车效率评价结果。
对输入矩阵进行标准化处理。假设原始矩阵X是由i个评价对象,j个评价指标构成的。首先需要对其中的极小型指标和中间型指标进行正向化处理,然后再对全部正向化的指标采用极值标准化方法进行标准化处理,得到标准化非负矩阵Z。标准化处理的目的是为了消除不同指标量纲的影响。具体计算步骤如下。
原始矩阵X表达式为
X=$\left(\begin{array}{llll}{x}_{11}& {x}_{12}& \dots & {x}_{1n}\\ {x}_{21}& {x}_{22}& \dots & {x}_{2n}\\ ︙& ︙&  & ︙\\ {x}_{m1}& {x}_{m2}& \dots & {x}_{mn}\end{array}\right)$
极小型指标正向化表达式为
x'ij={x1j,x2j,…,xnj}-xij
中间型指标正向化表达式为
x'ij=1-$\frac{|{x}_{ij}-\left.a\right|}{max\left(\right|{x}_{ij}-\left.a\right|)}$
极大型指标正向化表达式为
x'ij=xij
正向化矩阵元素标准化表达式为
$\begin{aligned}z_{i j}= & \frac{0.998\left(x_{i j}^{\prime}-\min \left\{x_{1 j}^{\prime}, x_{2 j}^{\prime}, \cdots, x_{i j}^{\prime}\right\}\right)}{\max \left\{x_{1 j}^{\prime}, x_{2 j}^{\prime}, \cdots, x_{i j}^{\prime}\right\}-\min \left\{x_{1 j}^{\prime}, x_{2 j}^{\prime}, \cdots, x_{i j}^{\prime}\right\}}+ \\& 0.002\end{aligned}$
Z=$\left(\begin{array}{llll}{z}_{11}& {z}_{12}& \dots & {z}_{1n}\\ {z}_{21}& {z}_{22}& \dots & {z}_{2n}\\ ︙& ︙&  & ︙\\ {z}_{m1}& {z}_{m2}& \dots & {z}_{mn}\end{array}\right)$
式中:xij为正向化矩阵元素;zij为标准化矩阵元素;a为最佳值,取a=1。
首先计算第j项指标下第i个样本所占的比重,并将其看作相对熵计算中的概率。其次,利用信息熵公式计算指标信息熵及信息效用值,并将信息效用值进行归一化,得到各个指标熵权。具体计算步骤表达式为
Pij=$\frac{{z}_{ij}}{\sum _{i=1}^{n}{z}_{ij}}$
ej=-$\frac{1}{lnn}\sum _{i=1}^{n}$pijlnpij
dj=1-ej
wj=$\frac{{d}_{j}}{\sum _{j=1}^{m}{d}_{j}}$
式中:pij为相对熵概率;ej为信息熵;dj为信息效用值;wj为熵权。
首先计算指标对比强度,即计算标准差σi。其次计算指标冲突性,这里使用皮尔逊线性相关系数构造相关性矩阵R。综合指标对比强度和相关性,计算信息量。最后将信息量归一化处理得到Critic权重。计算步骤表达式为
σj=$\sqrt{\frac{\sum _{i=1}^{n}({x}_{ij}-{\stackrel{-}{x}}_{j}{)}^{2}}{n-1}}$
R=$\frac{\sum _{j,k=1}^{n}({x}_{ij}-{\stackrel{-}{x}}_{j}\left)\right({x}_{ik}-{\stackrel{-}{x}}_{k})}{\sqrt{\sum _{j=1}^{n}({x}_{ij}-{\stackrel{-}{x}}_{j}{)}^{2}\sum _{k=1}^{n}({x}_{ik}-{\stackrel{-}{x}}_{k}{)}^{2}}}$
Sj=$\sum _{i=1}^{n}$(1-rij)
Cj=σjSj
Wj=$\frac{{C}_{j}}{\sum _{j=1}^{n}{C}_{j}}$
式中:$\overline{{x}_{j}}$=$\frac{1}{n}\sum _{i=1}^{n}$xij;rij为评价指标ij的相关系数;Sj为指标冲突性;Cj为信息量;Wj为Critic权重。
将两种赋权方法取得的权重值组合,并进行归一化处理,得到组合权重值W组合j,计算公式为
W组合j=$\frac{{w}_{j}{W}_{j}}{\sum \left({w}_{j}{W}_{j}\right)}$
首先建立决策加权矩阵。将确定的组合权重W组合j考虑到决策矩阵Z中,得到决策加权矩阵V。然后寻找正负理想解,并计算每个评价对象与正负理想解的距离。最后据此计算最终评分。
计算决策加权矩阵为
V=$\left(\begin{array}{llll}{v}_{11}& {v}_{12}& \dots & {v}_{1n}\\ {v}_{21}& {v}_{22}& \dots & {v}_{2n}\\ ︙& ︙&  & ︙\\ {v}_{m1}& {v}_{m2}& \dots & {v}_{mn}\end{array}\right)$
=$\left(\begin{array}{llll}{z}_{11}{W}_{\mathrm{组}\mathrm{合}1}& {z}_{12}{W}_{\mathrm{组}\mathrm{合}1}& \dots & {z}_{1j}{W}_{\mathrm{组}\mathrm{合}1}\\ {z}_{21}{W}_{\mathrm{组}\mathrm{合}2}& {z}_{22}{W}_{\mathrm{组}\mathrm{合}2}& \dots & {z}_{2j}{W}_{\mathrm{组}\mathrm{合}2}\\ ︙& ︙&  & ︙\\ {z}_{i1}{W}_{\mathrm{组}\mathrm{合}j}& {z}_{i2}{W}_{\mathrm{组}\mathrm{合}j}& \dots & {z}_{ij}{W}_{\mathrm{组}\mathrm{合}j}\end{array}\right)$
计算正负理想解,公式为
V+=(${V}_{1}^{+}$,${V}_{2}^{+}$,…,${V}_{j}^{+}$)=(max{v11,
v21,…,vi1},…,max{v1j,v2j,…,vij}
V-=(${V}_{1}^{-}$,${V}_{2}^{-}$,…,${V}_{j}^{-}$)=(max{v11,
v21,…,vi1},…,max{v1j,v2j,…,vij}
计算评价对象到正负理想解的距离,公式为
${D}_{i}^{+}$=$\sqrt{\sum _{j=1}^{m}({V}_{j}^{+}-{v}_{ij}{)}^{2}}$
${D}_{i}^{-}$=$\sqrt{\sum _{j=1}^{m}({V}_{j}^{-}-{v}_{ij}{)}^{2}}$
计算评价对象与理想解的相对贴近度,并视作综合评价得分,公式为
Mi=$\frac{{D}_{i}^{-}}{{D}_{i}^{+}+{D}_{i}^{-}}$
式(26)中:V+为正理想解;V-为最劣解;${D}_{i}^{+}$为第i个评价对象与最大值的距离;${D}_{i}^{-}$为第i个评价对象与最小值的距离;Mi为各个评价对象的归一化评分。0≤Mi≤1,且Mi越大${D}_{i}^{+}$越大,越接近最大值。
本文使用的是北京市10个路内停车场2023年3月13日—17日和2023年3月20日—24日(周一至周五)订单数据以及对10个停车场属性的调查数据作为原始评价指标数据。具体停车场信息可见表1,其10个路内停车场具体地理位置可见图2
表1图2可知,该10个路内停车场覆盖北京市多个行政区域,道路横断面形式为四幅路居多,并且都是路面双侧停车,其周围用地性质也不尽相同,其中住宅类用地最多。由此可见,其10个路内停车场具有一定代表性,可进行处理使用。
在运用数据进行评价之前,要先对数据的质量进行评估,本文中采取描述性分析。描述性分析主要通过平均值或中位数描述定量数据的整体情况,根据分析结果可以判断数据质量。
表2表3描述性分析的结果显示,当前数据中没有异常值出现,可直接针对平均值进行描述分析。
首先,对评价指标数据使用式(5)~式(10)进行标准化处理后,消除不同指标量纲的影响,以便于后续计算。然后分别利用式(11)~式(14)即熵权法以及式(15)~式(19)即Critic权重法得到每个指标在不同方法下所占比重,最后利用式(20)计算得到每个指标综合权重,结果如表4所示。
结合该10个路内停车场数据,熵权 Critic 法权重计算结果显示,停车位数量、停车位利用率以及车行道剩余宽度这三个指标对于综合评价结果的影响最大,高峰停车指数、停车周转率以及累计车辆数次之,到达流率、驶出流率以及停放时间的影响程度最小。需要指出的是,随着模型的推广应用,权重可能会随着不同区域交通特点以及样本数据量的增大而有所调整。
依据熵权Critic法得到的指标权重,进行式(21)~式(26)评价计算,最后据此计算得出10个路内停车场的最终评分,具体评分如表5所示。
综合评价结果显示,工体东路路内停车场与正理想解距离为0.157 1,与负理想解距离为0.304 2,在10个停车场中距离负理想解最远,综合评分为0.659 4,排名第1;科苑路路内停车场与正理想解距离为0.158 1,稍远于工体东路,与负理想解距离为0.248 6,与工体东路相比要近,则综合评分为0.611 3,排名第2;花园北路路内停车场与正理想解距离相较于花园北路的更近,为0.155 4,但其负理想解距离为0.241 2,近于科苑路,所以综合评分为0.608 1,排名第3。其他道路路内停车场排名顺序与上述同理。
(1)春和路段内的停车位设置在非机动车道的平行式停车位,紧靠人行道。为保证非机动车和驶入停车场车辆的安全,应当在停车位外侧设置 0.5 m的安全开门区,并施划地面三角提醒标线和临时停车标志。根据评价结果,春和路停车场得分为0.444 7,综合排名第9,其停放周转率为2.90,停车位利用率为40%,高峰停车指数为0.29,平均停放时间为107 min,说明该停车场承担了长时间停车的任务,功能定位发生偏差。对于设置在商业用地周围的春和路路内停车场应该调整日间允许停车的时长或者增加日间长时间停车的费用,并且相关停车管理部门应当积极与周围商业网点进行沟通,允许私家车占用其门前空间,以实现停车分流。
(2)岭南路段内的停车位设置也在非机动车道的平行式停车位,紧靠人行道。岭南路停车场得分为0.439 4,综合排名第10,其停放周转率为2.60,停车位利用率为98%,高峰停车指数为0.38,平均停放时间长达329 min,并且路边两侧停车后,剩余车道宽度只有6.8 m,严重影响道路通行,所以应当在该路内停车场附近另开辟路外停车场,以满足停车需求。
针对城市路内停车的一系列问题,包括不合理的空间配置、供求关系失衡及管理体系的不完善等,建议采取综合性的改进措施。首先,公安交通管理部门应拓展执法范围,定期举行专项执法行动,通过严格执行执法措施,加强公众对遵守交通规则重要性的认识,对反复违规者依法予以严厉处罚。同时,增加远程执法技术装备,提高执法准确性和效率,构建严密的执法网络,有效遏制违法停车行为。
在行政管理方面,加强公众教育和宣传,利用新媒体平台开展宣传教育活动,根植文明停车和遵法停车观念。推动停车行业产业化,探索“警企合作”模式,共建共治共享新模式。调控停车需求,如增加公共交通投资,执行机动车限购等政策。
在技术层面,应优化和完善路内停车配套设施,推进智能停车系统建设,实现信息化管理,进行交通状况评估和需求预测。在经济策略上,根据市场趋势,科学定价,提供多元化停车服务选项,以提高城市路内停车管理的整体效能和水平。
(1)根据现有研究建立了路内停车位设置的评价指标体系,运用熵权Critic法确定了指标层中各个指标的客观组合权重,建立了Topsis路内停车位评价模型。
(2)对所构建的路内停车位效率评价模型进行了实例分析。结合北京市10个路内停车场的指标数据,通过模型求解得出停车位数量、停车位利用率以及车行道剩余宽度等三个指标的综合权重,依次排列最大,然后对10个路内停车场进行评分及排名,其中工体东路、科苑路和花园北路停车场得分依次排列最高,最后根据结果提出了优化和管理对策。
(3)模型结果可以进一步分析。例如运用根据熵权Critic-Topsis模型求解的路内停车位评分和相关聚类算法来确定路内停车位等级,识别供需矛盾突出的路内停车场。
  • 中国人民公安大学交通管理工程双一流创新研究专项(2023SYL09)
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2025年第25卷第7期
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doi: 10.12404/j.issn.1671-1815.2402765
  • 接收时间:2024-04-16
  • 首发时间:2026-03-30
  • 出版时间:2025-03-08
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  • 收稿日期:2024-04-16
  • 修回日期:2024-06-05
基金
中国人民公安大学交通管理工程双一流创新研究专项(2023SYL09)
作者信息
    1 中国人民公安大学交通管理学院, 北京 100038
    2 北京市公安局交通管理局, 北京 100037

通讯作者:

* 马社强(1973—),男,汉族,陕西宝鸡人,博士,副教授。研究方向:交通安全。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
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
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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