Presentaciones a Congresos

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Data-driven simulation for pedestrian avoiding a fixed obstacle
(2019-07) Martin, Rafael F.; Parisi, Daniel
"Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which does not have to deal with a huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian—one obstacle problem. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction."
Pedestrian collision avoidance with a local dynamic goal
(2018-08-21) Martin, Rafael F.; Parisi, Daniel
"We present here a general formalism for equipping simulated pedestrians with an avoidance mechanism. The central idea is to use a short-range target which is adjusted dynamically depending on the environment and thus modulating the desired velocity of the agent. This formulation can be implemented over any type of existing pedestrian model, being force-based or rule-based. As an example, we implement a simple instance of the formulation which is adjusted to reproduce previous reported and available experimental data of collision avoidance in scenarios of low density. The proposed minimal model shows good agreement with the real trajectories and other macroscopic observables."