Average IGD values of two algorithems.
<div><p>The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urb...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1852019170176139264 |
|---|---|
| author | Xiaodan Li (132227) |
| author2 | Yunci Guo (21578231) Zhen Liu (74646) Dandan Sun (448563) Yidi Liu (11248476) Wencan Wang (8623785) |
| author2_role | author author author author author |
| author_facet | Xiaodan Li (132227) Yunci Guo (21578231) Zhen Liu (74646) Dandan Sun (448563) Yidi Liu (11248476) Wencan Wang (8623785) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xiaodan Li (132227) Yunci Guo (21578231) Zhen Liu (74646) Dandan Sun (448563) Yidi Liu (11248476) Wencan Wang (8623785) |
| dc.date.none.fl_str_mv | 2025-06-20T17:25:04Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0326455.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Average_IGD_values_of_two_algorithems_/29372411 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Sociology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified limited land resources dynamically adjusts crossover density residential areas constructed considering convenience sustainable urban development density urban areas jing &# 8217 balance multiple objectives objective optimization methods pareto solution set objective optimization model shanghai </ p objective optimization urban populations optimal balance flexible solution &# 8220 xlink "> world data widely applied varying preferences specific strategies solved using representative region relationships among rapid growth quantitatively analyzed parking demand offering scenario mutation rates location selection large cities improved non ii ), global urbanization global issue critical issue chinese megacity |
| dc.title.none.fl_str_mv | Average IGD values of two algorithems. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urban development. Multi-objective optimization methods are widely applied to tackle such challenges, providing decision-makers with a set of optimal solutions that balance multiple objectives. However, existing studies often lack quantitative analyses of the relationships among these solutions, limiting their applicability in accommodating decision-makers with varying preferences. This study focuses on Jing’an District in Shanghai, a representative region of a Chinese megacity, to address this global issue. Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. Furthermore, the Pareto solution set is quantitatively analyzed from a cost-benefit perspective by integrating marginal benefit theory. This approach provides robust support for decision-makers seeking an optimal balance between cost and benefit, offering scenario-specific strategies. The findings of this study not only present an innovative, systematic, and flexible solution to the “parking dilemma” in high-density residential areas but also provide practical guidance and insights for other large cities in the planning and implementation of smart underground parking facilities.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_3fe3ff213bbb0ca93e89bc7e7b64c3c4 |
| identifier_str_mv | 10.1371/journal.pone.0326455.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29372411 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Average IGD values of two algorithems.Xiaodan Li (132227)Yunci Guo (21578231)Zhen Liu (74646)Dandan Sun (448563)Yidi Liu (11248476)Wencan Wang (8623785)EcologySociologyScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedlimited land resourcesdynamically adjusts crossoverdensity residential areasconstructed considering conveniencesustainable urban developmentdensity urban areasjing &# 8217balance multiple objectivesobjective optimization methodspareto solution setobjective optimization modelshanghai </ pobjective optimizationurban populationsoptimal balanceflexible solution&# 8220xlink ">world datawidely appliedvarying preferencesspecific strategiessolved usingrepresentative regionrelationships amongrapid growthquantitatively analyzedparking demandoffering scenariomutation rateslocation selectionlarge citiesimproved nonii ),global urbanizationglobal issuecritical issuechinese megacity<div><p>The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urban development. Multi-objective optimization methods are widely applied to tackle such challenges, providing decision-makers with a set of optimal solutions that balance multiple objectives. However, existing studies often lack quantitative analyses of the relationships among these solutions, limiting their applicability in accommodating decision-makers with varying preferences. This study focuses on Jing’an District in Shanghai, a representative region of a Chinese megacity, to address this global issue. Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. Furthermore, the Pareto solution set is quantitatively analyzed from a cost-benefit perspective by integrating marginal benefit theory. This approach provides robust support for decision-makers seeking an optimal balance between cost and benefit, offering scenario-specific strategies. The findings of this study not only present an innovative, systematic, and flexible solution to the “parking dilemma” in high-density residential areas but also provide practical guidance and insights for other large cities in the planning and implementation of smart underground parking facilities.</p></div>2025-06-20T17:25:04ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0326455.t004https://figshare.com/articles/dataset/Average_IGD_values_of_two_algorithems_/29372411CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293724112025-06-20T17:25:04Z |
| spellingShingle | Average IGD values of two algorithems. Xiaodan Li (132227) Ecology Sociology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified limited land resources dynamically adjusts crossover density residential areas constructed considering convenience sustainable urban development density urban areas jing &# 8217 balance multiple objectives objective optimization methods pareto solution set objective optimization model shanghai </ p objective optimization urban populations optimal balance flexible solution &# 8220 xlink "> world data widely applied varying preferences specific strategies solved using representative region relationships among rapid growth quantitatively analyzed parking demand offering scenario mutation rates location selection large cities improved non ii ), global urbanization global issue critical issue chinese megacity |
| status_str | publishedVersion |
| title | Average IGD values of two algorithems. |
| title_full | Average IGD values of two algorithems. |
| title_fullStr | Average IGD values of two algorithems. |
| title_full_unstemmed | Average IGD values of two algorithems. |
| title_short | Average IGD values of two algorithems. |
| title_sort | Average IGD values of two algorithems. |
| topic | Ecology Sociology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified limited land resources dynamically adjusts crossover density residential areas constructed considering convenience sustainable urban development density urban areas jing &# 8217 balance multiple objectives objective optimization methods pareto solution set objective optimization model shanghai </ p objective optimization urban populations optimal balance flexible solution &# 8220 xlink "> world data widely applied varying preferences specific strategies solved using representative region relationships among rapid growth quantitatively analyzed parking demand offering scenario mutation rates location selection large cities improved non ii ), global urbanization global issue critical issue chinese megacity |