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Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model
- Source :
- Applied Sciences, Vol 15, Iss 1, p 384 (2025)
- Publication Year :
- 2025
- Publisher :
- MDPI AG, 2025.
-
Abstract
- To effectively and objectively evaluate the spatial efficiency of rail transit station areas, seventeen 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.
Details
- Language :
- English
- ISSN :
- 15010384 and 20763417
- Volume :
- 15
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.106d835c1c404bcf96dcc962bed7d619
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/app15010384