An intelligence-based route choice model for pedestrian flow in a transportation station

Abstract

This study proposes a method that uses an artificial neural network (ANN) to mimic human decision-making about route choice in a crowded transportation station. Although ANN models have been developed rapidly and widely adopted in various fields in the last three decades, their application to predict human decision-making in pedestrian flows is limited, because the video clip technology used to collect pedestrian movement data in crowded conditions is still primitive. Data collection must be carried out manually or semi-manually, which requires extensive resources and is time consuming. This study adopts a semi-manual approach to extract data from video clips to capture the route choice behaviour of travellers, and then applies an ANN to mimic such decision-making. A prediction accuracy of 86% (ANN model with ensemble approach) is achieved, which demonstrates the feasibility of applying the ANN approach to decision-making in pedestrian flows.