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

• Published in: Applied soft computing 2014-11, Vol.24, p.31-39
• Main Authors: Yuen, J.K.K., Lee, E.W.M., Lam, W.W.H.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Crowd movement; Evacuation; Human behaviour; Intelligent system; Transportation
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.05.031

•We developed an artificial neural network (ANN) model to mimic route choice behaviour in crowds which achieved a prediction accuracy of 86%.•We demonstrated the feasibility of applying the ANN approach to decision-making in pedestrian flows. Both safety and comfort level inside the stations are potentially improved.•This model is useful for both station design and daily operation, as escalators are a critical transportation facility in transportation stations.•This ANN approach provides a rapid method for engineers to estimate the loadings of escalators, even for new stations, so that they can optimise their utilisation to achieve maximum efficiency. 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.