An approach for managing heterogeneous speed profiles in cellular automata pedestrian models

Pedestrian simulation models based on Cellular Automata are viable alternatives to particle based approaches, employing a continuous spatial representation. The effects of discretisation, however, also imply some difficulties in modelling phenomena observed in reality. This paper focuses on the possibility to manage heterogeneity in the walking speed of the simulated population of pedestrians by modifying an existing model extending the floor field approach. Whereas some discrete models allow pedestrians (or cars, when applied to traffic modelling) to move more than a single cell per time step, the present work proposes a maximum speed of one cell per step, but we model lower speeds by having pedestrians yielding their movement in some turns. Different classes of pedestrians are associated to different desired walking speeds and a stochastic mechanism is defined to ensure that they maintain an average speed close to the desired one. The paper formally describes the model and the results of its application in benchmark scenarios. Finally, the paper shows how this approach can also support the definition of slopes and stairs as elements reducing the walking speed of pedestrians climbing them in a simulated scenario.