Exploring the Relationship Between the Built Environment and Spatiotemporal Heterogeneity of Urban Traffic Congestion During Tourism Peaks
A Case Study of Harbin, China
Dados Bibliográficos
| ID | 22032461 |
|---|---|
| Autores | Renyue Cui (Northeast Forestry University), Jun Zhang (0000-0003-1706-1611, Northeast Forestry University, autor correspondente) |
| Ano | 2025 |
| Volume | 14 |
| Fascículo | 12 |
| Páginas | 470 |
| Data de publicação | 2025-11-29 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | ISPRS International Journal of Geo-Information (JOURNAL) |
| Identificadores do periódico | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Editora | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14120470 |
| OpenAlex | W4416958550 |
| Idioma | EN |
| Referências citadas | 35 |
Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We apply a Multiscale Geographically Weighted Regression (MGWR) model to geospatial data across four typical peak periods and benchmark the results against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The MGWR model demonstrates superior capability in capturing spatial non-stationarity and multiscale effects. The results reveal strong spatiotemporal heterogeneity in the effects of built environment factors on congestion. Intersection density demonstrates a stronger mitigating effect during weekday evening peaks. Catering facilities significantly exacerbate congestion in tourist hotspots. Tourism-related facilities such as hotels and attractions intensify congestion during weekend peaks. Parking availability shows dual impacts, with peripheral parking reducing pressure and central clustering worsening congestion. Our geospatially disaggregated results provide empirical evidence for location-sensitive and temporally adaptive traffic management and urban design strategies. This study highlights the value of MGWR-based spatial modeling in supporting geoinformation-driven urban mobility planning
Built environment · China · Cluster analysis · Geospatial analysis · Ordinary least squares · Tourism · Traffic congestion · Urban spatial structure · Urbanization · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Urban Transport and Accessibility
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| Velocidade de citação | historical |
|---|---|
| Altamente citado | Não |