A BIM-GIS Framework Integrated with CCTV Analytics for Urban Walkability Assessment

Area of Science:

• Urban Planning and Design
• Geospatial Information Science
• Computational Social Science

Background:

• Traditional walkability assessments often lack real-time data and granular detail.
• Integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS) offers potential for comprehensive urban modeling.
• Real-time crowd analytics can provide dynamic insights into pedestrian behavior and environmental conditions.

Purpose of the Study:

• To develop and validate a novel framework for quantitative walkability assessment.
• To integrate BIM, GIS, and real-time crowd analytics (CCTV) for enhanced urban analysis.
• To create a walkability scoring mechanism sensitive to diverse pedestrian needs and environmental factors.

Main Methods:

• Development of a framework extending open data standards (IFC, CityGML) for infrastructural and pedestrian flow attributes.
• Implementation of a walkability scoring mechanism considering accessibility, efficiency, and physical comfort for various pedestrian groups.
• Real-time data integration from Closed-Circuit Television (CCTV) for crowd analytics.

Main Results:

• The framework successfully quantified walkability variations based on direction (uphill/downhill), crowd density, and operational constraints.
• Statistical analysis confirmed significant differences (up to 30%) in walking costs across different scenarios.
• The system demonstrated robustness and practical utility in a real-world case study at HKUST.

Conclusions:

• The integrated BIM-GIS-CCTV framework provides a robust tool for real-time, human-centric walkability assessment.
• The scoring mechanism effectively differentiates walkability for diverse user groups and conditions.
• This approach offers significant potential for improving urban infrastructure planning and pedestrian experience.