ProactiveCrowd: modeling proactive steering behaviours for agent-based crowd simulation

How to realistically model an agent's steering behavior is a critical issue in agent-based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behavior-based modeling framework is first introduced to model the process of how humans select and execute a proactive steering strategies in crowded situations and execute the corresponding behavior accordingly. We then propose behavior models for two inter-related proactive steering behaviors, namely gap seeking and following. These behaviors can be frequently observed in real-life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real-world data and comparing the performance of our model with that of another state-of-the-art crowd model. The results show that the performance of our model is better or at least comparable to the compared model in terms of the realism at both individual and crowd level.

This is the peer reviewed version of the following article: Luo, L., Chai, C., Ma, J., Zhou, S. and Cai, W. (2018), ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent-Based Crowd Simulation. Computer Graphics Forum, 37: 375–388. doi:10.1111/cgf.13303, which has been published in final form at https://doi.org/10.1111/cgf.13303. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving