Abstract
This study proposes a novel framework integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS) with real-time crowd analytics from Closed-Circuit Television (CCTV) for quantitative walkability assessment. The framework extends open data standards (IFC and CityGML) to model infrastructural and pedestrian flow attributes comprehensively. A walkability scoring mechanism quantifies route quality based on accessibility, efficiency, and physical comfort, differentiating among pedestrian groups, such as individuals sensitive to weather conditions or carrying belongings. Implemented at the Hong Kong University of Science and Technology (HKUST), results indicate that the framework effectively captures variations in walkability scores due to directional differences (uphill vs. downhill), crowd conditions, and operational constraints like facility closures. Statistical tests confirm significant differences in walking costs across these scenarios with variations of up to 30%, demonstrating the framework’s robustness and practical utility for real-time, human-centric urban infrastructure planning. © 2025 by the authors.
| Original language | English |
|---|---|
| Article number | 3637 |
| Journal | Sensors |
| Volume | 25 |
| Issue number | 12 |
| Online published | 10 Jun 2025 |
| DOIs | |
| Publication status | Published - Jun 2025 |
Funding
This research was funded by City University of Hong Kong grant number 9610704.
Research Keywords
- BIM-GIS integration
- CCTV analytics
- computer vision
- real-time crowd monitoring
- walkability assessment
- data schema
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/