Towards Safer Pedestrian Traffic: Investigation of the Impact of Social Field Characteristic on Crowd Dynamics
The objective of this paper is to investigate pedestrian safety on vehicle-free platforms. Towards realizing such objective, a microscopic pedestrian movement model is expanded to analyze the implications of different pedestrian behavioral characteristics on pedestrian movement under different density levels. This microscopic modeling approach is flexible and may be efficiently implemented while accounting for different traffic dynamics caused by complex geometric and operational features, such as those observed in transit stations, football stadiums, and rallies. The integrated modeling framework is built based on the Social Field method (i.e., the social force model): the surrounding stimulus is considered, while adding a stopping/vibration module and a tangential force module to the basic Social Field method to account for additional behavioral dimensions based on atomistic interactions between particles. C++ was used to build a simulator to obtain the trajectory of pedestrians in the system. The research has been already translated to two simulated scenarios: the bottleneck scenario and the bi-direction flow scenario. Realistic flow patterns have been produced with a triangular fundamental diagram (flow-density curve) as observed in real-life conditions. When the density goes up, the flow will go up then go down to meet the capacity of the system. The expanded social force model provided improved (lower) error levels once the simulated pedestrian trajectories are compared to the observed pedestrian trajectories. Results show the expanded model produces high-density congestion dynamics that are not captured by the traditional social force model. The dynamics represented the higher clustering of flow-density data points at low flow and high pedestrian levels represent these dynamics.
The research team would like to thank the Delft University of Technology Transportation Research Team, especially Dr. Winnie Daamen, for providing the trajectory data needed to test the proposed modeling framework.
- 1.Antonini, G., Bierlaire, M., Weber, M.: Discrete choice models of pedestrian behavior. Tech. rep. (2004)Google Scholar
- 2.Campanella, M., Hoogendoorn, S., Daamen, W.: Calibrating walker models: variations of parameter due to traffic regimes. In: European Conference on Mathematical and Theoretical Biology, Kraków, Poland, June 28–July 2 (2011)Google Scholar
- 4.Ewing, R., Dumbaugh, E.: The built environment and traffic safety: a review of empirical evidence. CPL Bibliogr. 23(4), 347–367 (2009)Google Scholar
- 8.Hoogendoorn, S., Daamen, W.: Microscopic calibration and validation of pedestrian models: cross-comparison of models using experimental data. In: Traffic and Granular Flow ’05, pp. 329–340 (2007)Google Scholar
- 10.Kuhn, M.H.: Lewin, kurt. field theory of social science: selected theoretical papers. In: Cartwright, D. (ed.), pp. xx, 346. New York: Harper & Brothers (1951). $5.00. Ann. Am. Acad. Pol. Soc. Sci. 276(1), 146–147 (1951)Google Scholar
- 13.Porter, E., Hamdar, S.H., Daamen, W.: Pedestrian dynamics at transit stations: an integrated pedestrian flow modeling approach. Transp. A: Transp. Sci. 14, 1–16 (2017)Google Scholar