Research on Traffic Organisation Design Strategies for Commercial Buildings Based on Depthmap and MassMotion
Authors
Langjie Xiao1, Daina Zhang1, Wei Cheng1, *
1School of Urban Construction, Wuhan University of Science and Technology, Wuhan, China
*Corresponding author.
Email: pixel1549@outlook.com
Corresponding Author
Wei Cheng
Available Online 4 December 2023.
- DOI
- 10.2991/978-94-6463-304-7_19How to use a DOI?
- Keywords
- Pedestrian Clustering; Pedestrian Density; pedestrian simulation modelling; circulation space; commercial buildings
- Abstract
In order to quickly predict people distribution and traffic behavior in commercial buildings and further provide the quantitative basis for traffic organization design, this paper presents the analysis of Pedestrian Clustering and Pedestrian Density under selected circulation spaces of a shopping mall in Wuhan (China) by applying the software: Depthmap and MassMotion. To compare the correlation and difference between the visualization graphs of the above two software, results show that using Depthmap and MassMotion alone or in combination can meet the different needs of each design phase.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Langjie Xiao AU - Daina Zhang AU - Wei Cheng PY - 2023 DA - 2023/12/04 TI - Research on Traffic Organisation Design Strategies for Commercial Buildings Based on Depthmap and MassMotion BT - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023) PB - Atlantis Press SP - 169 EP - 182 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-304-7_19 DO - 10.2991/978-94-6463-304-7_19 ID - Xiao2023 ER -