An intelligence-based route choice model for pedestrian flow in a transportation station
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.
transportation artificial neural networks intelligent system crowd movement evacuation human behaviour