Improving Pedestrian Dynamics Predictions Using Neighboring Factors

Huu-Tu Dang; Benoit Gaudou; Nicolas Verstaevel

doi:10.17815/CD.2024.178

Authors

Huu-Tu Dang UMR 5505 IRIT, Université Toulouse Capitole, France
Benoit Gaudou UMR 5505 IRIT, Université Toulouse Capitole, France https://orcid.org/0000-0002-9005-3004
Nicolas Verstaevel UMR 5505 IRIT, Université Toulouse Capitole, France https://orcid.org/0000-0002-7879-6681

DOI:

https://doi.org/10.17815/CD.2024.178

Keywords:

Pedestrian Dynamics, Speed Predictions, Neighboriing Effects, Neural Networks

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.

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