Modeling of pedestrian turning behavior and prediction of pedestrian density distribution

Abstract

As urban public spaces attract more pedestrians, it is essential to prevent excessive pedestrian aggregation. Use of a walking behavior model is crucial to predict the distribution of pedestrian density. In earlier studies, the modeling of walking behavior generally focused on straight walking or aimless strolling. In recent years, more complex models of walking behavior, including turning behavior, have increasingly gained attention. Because the majority of models are calibrated using data gathered from experimental settings, research on the prediction of turning behavior of multi-scale walking passages in life scenarios remains in its development.The aim of this paper is to develop a turning behavior model suitable for multi-scale pedestrian passages. The expected walking direction was predicted by combining the global path planning model with a gradually changing expected direction with the local path planning model based on the attention field. Then, it was integrated with social force model to predict the pedestrian walking behavior and density distribution. Settings in the turning behavior model such as adaptively changing the expected walking direction according to the particular circumstance, approaching the inner curve upstream of the passage intersecting space, and appropriate pedestrian heterogeneity traits were found to help make predictions more accurately. The proposed turning behavior model is applicable to physical environments with different widths without recalibrating parameters and is suitable for predicting the distribution of pedestrian density in urban life scenarios with a variety of passage widths, which helps in the effective design and renovation of pedestrian space.