Improving Pedestrian Dynamics Predictions Using Neighboring Factors

Abstract

Predicting pedestrian dynamics is a complex task as pedestrian speed is influenced by various external factors. This study investigates neighboring factors that can be used to improve pedestrian walking speed prediction accuracy in both low- and high-density scenarios. Different factors are proposed, including Mean Distance, Time-to-Collision, and Front Effect, and data for each factor is extracted from different public datasets. The collected data at time t is used to train a neural network to predict the pedestrian walking speed at time t + ∆t. Predictions are evaluated using the Mean Absolute Error. Our results demonstrate that incorporating the Front Effect significantly improves prediction accuracy in both low- and high-density scenarios, whereas the Mean Distance factor only proves effective in high-density cases. On the other hand, no significant improvement is observed when considering the Time-to-Collision factor. These preliminary findings can be utilized to enhance the accuracy of pedestrian dynamics predictions by incorporating these factors as additional features within the model.