| id |
ecaade2024_261 |
| authors |
Zhong, Yuqin; Tan, Zhi Sheng; Mavros, Panagiotis; Hölscher, Christoph; Tunçer, Bige |
| year |
2024 |
| title |
Estimating Relative Pedestrian Crowd Distribution: A visibility-graph-based analysis workflow for malls during early design stage |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 433–442 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.2.433
|
| summary |
This paper introduces a visibility-graph-based workflow for early stages of architectural design, aimed at estimating relative pedestrian crowd distribution in shopping malls. Traditional methods like Agent-Based Modeling (ABM) and Space Syntax analysis face challenges in early design phases due to extensive data or configuration needs and lack of detail respectively. Our approach uses visibility graph as the foundation and generates visit probabilities and Chains of Activities (COAs) from empirical studies, balancing accuracy, accessibility and efficiency. The workflow's integration within designers’ familiar design interface allows for rapid prototyping and assessment of design iterations, making it a practical tool. Validation through a case study in a shopping mall in Singapore demonstrates the workflow's accuracy, with results showing strong similarity to both ABM and observed data, but with significantly less time and resource demands. This workflow offers a novel solution for early-stage design, providing a swift and accurate means to evaluate pedestrian dynamics and optimize design layouts. |
| keywords |
Pedestrian Crowd Analysis, Mixed-use Building, Shopping Mall Design, Visibility Graph Analysis, Agent-based Modelling, Evidence-based Design |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,775,993 bytes) |
| references |
Content-type: text/plain
|
| last changed |
2024/11/17 22:05 |
|
