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Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling

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Published:05 July 2020Publication History
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Abstract

One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules. Furthermore, the proposed approach considers both static and dynamic features during modeling to generate a realistic crowd, based on the assumption that humans tend to consider different features with respect to their states. In the proposed method, the automatic behavior rule generation problem is formulated as a symbolic regression problem. Then, the problem is solved by multi-objective genetic programming. On one hand, to improve the interpretability of the behavior rules found, a new mechanism is proposed to guide the algorithm to find concise and dimensionally consistent solutions. On the other hand, decisions made by considering static and dynamic features respectively are combined to improve the generated crowd realism. To validate the effectiveness of the proposed method, three real-world datasets are utilized for training and testing. The simulation results demonstrate that the proposed method is able to find universal behavior rules that are competitive to previous work in accuracy while having better interpretability.

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          cover image ACM Transactions on Modeling and Computer Simulation
          ACM Transactions on Modeling and Computer Simulation  Volume 30, Issue 3
          July 2020
          127 pages
          ISSN:1049-3301
          EISSN:1558-1195
          DOI:10.1145/3403635
          Issue’s Table of Contents

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          Publication History

          • Published: 5 July 2020
          • Online AM: 7 May 2020
          • Accepted: 1 March 2020
          • Revised: 1 January 2020
          • Received: 1 April 2019
          Published in tomacs Volume 30, Issue 3

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