A model for pedestrian walking behaviour, based on a discrete choice approach is proposed for modelling the short range behaviour in normal conditions, as a reaction to the surrounding environment and to the presence of other individuals. The model is specified as a discrete choice model, wherethe choice of the position of the next step is considered, based on an individual-specific discretisation of space, varying with walking speed and direction. The utility associated with each cell captures various behavioural patterns: willingness to reach the destination, stability of the direction, acceleration and interactions with other pedestrians. The latter pattern involves collision avoidance and leader-follower behaviours. The spatial correlation among the alternatives is taken into account defining a cross nested logit model. The model has been estimated by maximum likelihoodon a Japanese dataset, using Biogeme. The dataset consists of pedestrian trajectories manually tracked from video sequences. The subset was collected in Sendai, Japan, in August 2000. Two main pedestrian flows cross the street, giving rise to a large number of interactions. In this context, 190pedestrian trajectories have been manually tracked, for a total number of9281 position observations. The estimated coefficients are significant and their signs are consistent with the behavioural assumptions. The validation procedure consists in applying two models on two datasets. In additionto the model presented above, a simple model is considered where the utility of each alternative is represented only by an alternative specific constant (ASC). This model perfectly reproduces the observed shares in the sample. The objective is to illustrate the importance of the explanatory variables in the model. The two datasets are the Japanese one, and a dataset collected in the Netherlands. Volunteer pedestrians are called to perform specific walking tasks in a controlled experimental set-up, in order to create characteristic pedestrian motion patterns such as one-directional flow, bi-directional flow, walking through narrow and wide bottlenecks and crossing flows among the others. The predictions on the Japanese dataset of the estimated model have been compared to those of the ASC model developedon this same dataset. In addition a cross-validation was performed on this dataset. As for the Japanese dataset the predictions of the estimated model were compared to those of the ASC model on the Dutch dataset. This third part is the most powerful, because the Dutch dataset had not been involved in the model calibration. The validation procedure underlines a good stability of the model and a good generalisation performance. Few observations are badly predicted, mostly concentrated at the extreme of the choice set. Finally, the model has been embedded in a simulator, allowing also for visual validation. For the covering abstract see ITRD E145999
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