On density–flow relationships during crowd evacuation. (May 2017)

• Record Type: Journal Article
• Authors: Haworth, Brandon; Usman, Muhammad; Berseth, Glen; Kapadia, Mubbasir; Faloutsos, Petros
• Is Part Of: Computer animation and virtual worlds. Volume 28:Number 3/4(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-05
• Subjects: architectural optimization -- crowd flow -- crowd simulation; Computer animation -- Periodicals; Visualization -- Periodicals; 006.6
• DOI: 10.1002/cav.1783
• Languages: English
• ISSNs: 1546-4261

Abstract

Abstract: Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer‐assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks. Abstract : Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative Level of Service (LoS) classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.