Guide them through: An automatic crowd control framework using multi-objective genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{HU201890,

• author = "Nan Hu and Jinghui Zhong and Joey Tianyi Zhou and Suiping Zhou and Wentong Cai and Christopher Monterola",
• title = "Guide them through: An automatic crowd control framework using multi-objective genetic programming",
• journal = "Applied Soft Computing",
• year = "2018",
• volume = "66",
• pages = "90--103",
• month = may,
• keywords = "genetic algorithms, genetic programming, Crowd modelling and simulation, Crowd control, Multi-objective optimisation",
• ISSN = "1568-4946",
• URL = "http://eprints.mdx.ac.uk/23685/",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494618300437",
• DOI = "doi:10.1016/j.asoc.2018.01.037",
• size = "14 pages",
• abstract = "We propose an automatic crowd control framework based on multi-objective optimisation of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for optimal overall crowd flow in real-time, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front allows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quantitatively measured better, but also well aligned with domain experts recommendations on effective crowd control such as slower is faster and asymmetric control. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20percent) congestion along critical segments of the path.",

}

Genetic Programming entries for Nan Hu Jinghui Zhong Joey Tianyi Zhou Suiping Zhou Wentong Cai Christopher Monterola

Citations