View in EDS

An Optimization Method for the Land-Use Index in a Site Area: Improving the BP Neural Network Model Based on a Genetic Algorithm.

Saved in:
Bibliographic Details
Title: An Optimization Method for the Land-Use Index in a Site Area: Improving the BP Neural Network Model Based on a Genetic Algorithm.
Authors: Li, Xiang1 (AUTHOR) boundarier@sina.com, Dai, Runlong1 (AUTHOR), Shu, Xingchuan1 (AUTHOR), Liu, Ziyi1 (AUTHOR), Wei, Yao1 (AUTHOR), Yanling, Siqi1 (AUTHOR), Qiu, Zhiyue1 (AUTHOR), Gu, YuXin1 (AUTHOR), Yuan, Hong1 (AUTHOR) arcyuan@home.swjtu.edu.cn
Source: Journal of Urban Planning & Development. Jun2026, Vol. 152 Issue 2, p1-15. 15p.
Subjects: Transit-oriented development, Urban land use, Multiple criteria decision making, Public transit ridership, Multi-objective optimization, Urban planning, Genetic algorithms, Back propagation
Abstract: The land-use layout in the core area of a station under the transit-oriented development (TOD) model plays a crucial role in shaping urban development. Although many rail transit stations have been constructed, most do not exhibit the intensive, economical, efficient, and orderly land-use characteristics typical of TOD stations. The traditional backpropagation (BP) neural network model offers significant advantages in addressing the nonlinear aspects of land-use evaluation and its influencing factors. However, due to the limited number of mature TOD stations in operation, available training data are insufficient, leading to suboptimal model performance. To address the issue of model underfitting caused by limited training samples, a genetic algorithm (GA) is employed to optimize the BP neural network, forming a GA-BP model (GA-BP). In this study, we construct a GA-optimized BP neural network for land-use evaluation and a rail transit ridership prediction model. These models are then integrated into a multiobjective planning framework, and the optimal land-use scheme within a 500-m radius of a TOD station is identified using nondominated sorting genetic algorithm II. The empirical results indicate that (1) the GA-BP model is more capable of finding global optima and demonstrates low error, high efficiency, and strong stability, effectively mitigating the problem of underfitting due to limited data; and (2) the model shows good applicability and can offer valuable optimization recommendations. We apply this framework to predict and optimize TOD land use in a target area, providing strategic guidance for the coordinated development of land use and urban form in areas surrounding TOD stations. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Urban Planning & Development is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 193068997
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: An Optimization Method for the Land-Use Index in a Site Area: Improving the BP Neural Network Model Based on a Genetic Algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Xiang%22">Li, Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> boundarier@sina.com</i><br /><searchLink fieldCode="AR" term="%22Dai%2C+Runlong%22">Dai, Runlong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shu%2C+Xingchuan%22">Shu, Xingchuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Ziyi%22">Liu, Ziyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Yao%22">Wei, Yao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yanling%2C+Siqi%22">Yanling, Siqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Zhiyue%22">Qiu, Zhiyue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+YuXin%22">Gu, YuXin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Hong%22">Yuan, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> arcyuan@home.swjtu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Urban+Planning+%26+Development%22">Journal of Urban Planning & Development</searchLink>. Jun2026, Vol. 152 Issue 2, p1-15. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Transit-oriented+development%22">Transit-oriented development</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+land+use%22">Urban land use</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Public+transit+ridership%22">Public transit ridership</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+planning%22">Urban planning</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The land-use layout in the core area of a station under the transit-oriented development (TOD) model plays a crucial role in shaping urban development. Although many rail transit stations have been constructed, most do not exhibit the intensive, economical, efficient, and orderly land-use characteristics typical of TOD stations. The traditional backpropagation (BP) neural network model offers significant advantages in addressing the nonlinear aspects of land-use evaluation and its influencing factors. However, due to the limited number of mature TOD stations in operation, available training data are insufficient, leading to suboptimal model performance. To address the issue of model underfitting caused by limited training samples, a genetic algorithm (GA) is employed to optimize the BP neural network, forming a GA-BP model (GA-BP). In this study, we construct a GA-optimized BP neural network for land-use evaluation and a rail transit ridership prediction model. These models are then integrated into a multiobjective planning framework, and the optimal land-use scheme within a 500-m radius of a TOD station is identified using nondominated sorting genetic algorithm II. The empirical results indicate that (1) the GA-BP model is more capable of finding global optima and demonstrates low error, high efficiency, and strong stability, effectively mitigating the problem of underfitting due to limited data; and (2) the model shows good applicability and can offer valuable optimization recommendations. We apply this framework to predict and optimize TOD land use in a target area, providing strategic guidance for the coordinated development of land use and urban form in areas surrounding TOD stations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Urban Planning & Development is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193068997
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1061/JUPDDM.UPENG-6003
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1
    Subjects:
      – SubjectFull: Transit-oriented development
        Type: general
      – SubjectFull: Urban land use
        Type: general
      – SubjectFull: Multiple criteria decision making
        Type: general
      – SubjectFull: Public transit ridership
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Urban planning
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Back propagation
        Type: general
    Titles:
      – TitleFull: An Optimization Method for the Land-Use Index in a Site Area: Improving the BP Neural Network Model Based on a Genetic Algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Xiang
      – PersonEntity:
          Name:
            NameFull: Dai, Runlong
      – PersonEntity:
          Name:
            NameFull: Shu, Xingchuan
      – PersonEntity:
          Name:
            NameFull: Liu, Ziyi
      – PersonEntity:
          Name:
            NameFull: Wei, Yao
      – PersonEntity:
          Name:
            NameFull: Yanling, Siqi
      – PersonEntity:
          Name:
            NameFull: Qiu, Zhiyue
      – PersonEntity:
          Name:
            NameFull: Gu, YuXin
      – PersonEntity:
          Name:
            NameFull: Yuan, Hong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 07339488
          Numbering:
            – Type: volume
              Value: 152
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Urban Planning & Development
              Type: main
ResultId 1