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

We propose an automatic crowd control framework based on multi-objective optimisa- tion 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 opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows 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 quanti- tatively 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 (20%) congestion along critical segments of the path.

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