Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model
To effectively and objectively evaluate the spatial efficiency of rail transit station areas, <b>seventeen</b> typical rail station areas in Xi’an were selected as the research object. An evaluation system for spatial efficiency was constructed based on data from field research, satellit...
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2025-01-01
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| author | Haiyan Tong Quanhua Hou Xiao Dong Yaqiong Duan Weiming Gao Kexin Lei |
| author_facet | Haiyan Tong Quanhua Hou Xiao Dong Yaqiong Duan Weiming Gao Kexin Lei |
| author_sort | Haiyan Tong |
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| description | To effectively and objectively evaluate the spatial efficiency of rail transit station areas, <b>seventeen</b> typical rail station areas in Xi’an were selected as the research object. An evaluation system for spatial efficiency was constructed based on data from field research, satellite images, Baidu heat maps, and station passenger flow statistics. Key factors such as land use, transportation systems, social aspects, and spatial efficiency are considered in the framework. A data envelopment analysis (DEA) method was used to evaluate the spatial efficiency of these sample station areas. The results are as follows. ① An incomplete symmetric relationship exists between the Constant Returns to Scale Technical Efficiency (Crste) and the Variable Returns to Scale Technical Efficiency (Vrste) of station area spatial efficiency. The keys to improving station area spatial efficiency include reducing redundant resource investments and establishing a rational resource allocation structure. ② For high-efficiency station areas, the Crste and Vrste are relatively high, with an overall increasing return to scale efficiency (Scale). In medium-efficiency station areas, the Crste is relatively high, but either Vrste or Scale is low. In low-efficiency station areas, the Crste is moderate, and both Vrste and Scale are low. The findings provide a reference for the intensive use of land around Xi’an rail stations, as well as support for the sustainable operation of rail transit. |
| format | Article |
| id | doaj-art-106d835c1c404bcf96dcc962bed7d619 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-106d835c1c404bcf96dcc962bed7d6192025-01-10T13:15:22ZengMDPI AGApplied Sciences2076-34172025-01-0115138410.3390/app15010384Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) ModelHaiyan Tong0Quanhua Hou1Xiao Dong2Yaqiong Duan3Weiming Gao4Kexin Lei5School of Architecture, Chang’an University, Xi’an 710061, ChinaSchool of Architecture, Chang’an University, Xi’an 710061, ChinaSchool of Architecture, Chang’an University, Xi’an 710061, ChinaSchool of Architecture, Chang’an University, Xi’an 710061, ChinaSchool of Architecture, Chang’an University, Xi’an 710061, ChinaSchool of Architecture, Chang’an University, Xi’an 710061, ChinaTo effectively and objectively evaluate the spatial efficiency of rail transit station areas, <b>seventeen</b> typical rail station areas in Xi’an were selected as the research object. An evaluation system for spatial efficiency was constructed based on data from field research, satellite images, Baidu heat maps, and station passenger flow statistics. Key factors such as land use, transportation systems, social aspects, and spatial efficiency are considered in the framework. A data envelopment analysis (DEA) method was used to evaluate the spatial efficiency of these sample station areas. The results are as follows. ① An incomplete symmetric relationship exists between the Constant Returns to Scale Technical Efficiency (Crste) and the Variable Returns to Scale Technical Efficiency (Vrste) of station area spatial efficiency. The keys to improving station area spatial efficiency include reducing redundant resource investments and establishing a rational resource allocation structure. ② For high-efficiency station areas, the Crste and Vrste are relatively high, with an overall increasing return to scale efficiency (Scale). In medium-efficiency station areas, the Crste is relatively high, but either Vrste or Scale is low. In low-efficiency station areas, the Crste is moderate, and both Vrste and Scale are low. The findings provide a reference for the intensive use of land around Xi’an rail stations, as well as support for the sustainable operation of rail transit.https://www.mdpi.com/2076-3417/15/1/384spatial efficiencyDEArail transit station area |
| spellingShingle | Haiyan Tong Quanhua Hou Xiao Dong Yaqiong Duan Weiming Gao Kexin Lei Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model Applied Sciences spatial efficiency DEA rail transit station area |
| title | Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model |
| title_full | Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model |
| title_fullStr | Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model |
| title_full_unstemmed | Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model |
| title_short | Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model |
| title_sort | assessing the spatial efficiency of xi an rail transit station areas using a data envelopment analysis dea model |
| topic | spatial efficiency DEA rail transit station area |
| url | https://www.mdpi.com/2076-3417/15/1/384 |
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