Advancing lane formation and high-density simulations in bidirectional flow: A humanoid pedestrian model incorporating gait dynamics and body rotation

Abstract

Current bidirectional pedestrian flow models face challenges in accurately simulating lane formation and high-density conditions. This study addresses these issues by developing an improved humanoid pedestrian model (HPM), which extends the applicability of the original HPM from one-dimensional to two-dimensional scenarios and offers a more realistic simulation of pedestrian behavior. The improved HPM incorporates two distinct gaits—walking while rotating and walking while turning, which capture the complex dynamics of human walking—and an innovative gait-planning process. Additionally, a novel energy-based heuristic rule that considers factors such as deviation from the target direction, body rotation to navigate gaps, and reduced walking velocity is introduced. The energy expression is designed according to the form of mechanical energy, with no parameters requiring calibration. This design enables our model to demonstrate, to some extent, that pedestrians determine their walking direction by minimizing mechanical energy consumption. Simulations are conducted under conditions replicating previous experiments to validate the improved HPM against both experimental results and two classic models, namely the heuristic-based model and the social force model. The improved HPM shows minimal trajectory deviation; effectively replicates body rotation that facilitates efficient lane formation; and transitions swiftly from a randomized flow to stable, well-ordered flow patterns. Moreover, the improved HPM achieves a maximum density of 7 ped/m², representing a significant advancement in modeling high-density scenarios. Overall, the improved HPM offers deep insights into the crowd dynamics of bidirectional flow and thereby improves the accuracy of simulations in high-density situations.