Analysis and verification of the social forces model in pedestrian lane formation scenarios

Pedestrian behavior and dynamics modeling has greatly benefited from recent advances in our ability to process large amounts of data and to simulate complex scenarios. The importance of this area of study has recently been renewed in the current global scenario, when the prospect of new emerging pandemics introduces the challenge of controlling flow speed and density of people inside sport stadiums, subway and train stations, and in other urban spaces. In this work, we have selected the model of social forces, proposed by Helbing and Molnár in 1995, as the center of our research. We developed an implementation of this model in Python 3.8 using agent-based modeling concepts and object-oriented programming, and tested the ability of our implementation to reproduce the experiments carried out by Feliciani and Nishinari in 2016, analyzing the fine-scale resulting dynamics. We concluded that the model is capable of reproducing some microscopic aspects of the experiments, but that more advanced work on the calibration of its various parameters is necessary.