This page provides a list of publications of the research group and associated researchers about microscopic pedestrian simulations and the underlying models. The list of publications in BibTeX format can be downloaded here.
Simulations
Simulations with Vadere
If you use Vadere for your research, please cite:
Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding. In: Collective Dynamics, vol. 4, 2019.
Simulations in General
Bridging the gap: From cellular automata to differential equation models for pedestrian dynamics. In: Journal of Computational Science, vol. 5, no. 5, pp. 841–846, 2014.
Simulating pedestrian dynamics: Towards natural locomotion and psychological decision making. Technische Universität München, 2016.
The superposition principle: A conceptual perspective on pedestrian stream simulations. In: Collective Dynamics, vol. 1, pp. A2, 2016.
Microscopic Locomotion Models
The webpage pedestriandynamics.org/models/ summarizes basic concepts of microscopic crowd models such as the Optimal Steps Model, the Gradient Navigation Model, or the Social Force Model. For detailed explanations, we refer to the scientific articles listed below.
Optimal Steps Model (OSM)
Natural discretization of pedestrian movement in continuous space. In: Physical Review E, vol. 86, no. 4, pp. 046108, 2012.
How update schemes influence crowd simulations. In: Journal of Statistical Mechanics: Theory and Experiment, vol. 2014, no. 7, pp. P07002, 2014.
The effect of stepping on pedestrian trajectories. In: Physica A: Statistical Mechanics and its Applications, vol. 421, pp. 594–604, 2015.
Dynamic Stride Length Adaptation According to Utility And Personal Space. In: Transportation Research Part B: Methodological, vol. 74, pp. 104–117, 2015.
Gradient Navigation Model (GNM)
Gradient navigation model for pedestrian dynamics. In: Physical Review E, vol. 89, no. 6, pp. 062801, 2014.
Social Force Model (SFM)
Social Force Model for pedestrian dynamics. In: Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995.
Avoiding numerical pitfalls in social force models. In: Physical Review E, vol. 87, no. 6, pp. 063305, 2013.
Cognitive Heuristics (BHM)
How cognitive heuristics can explain social interactions in spatial movement. In: Journal of the Royal Society Interface, vol. 13, no. 121, pp. 20160439, 2016.
Biomechanics Model (BMM)
Simulating pedestrian dynamics: Towards natural locomotion and psychological decision making. Technische Universität München, 2016.
Reynolds Steering
Steering Behaviors For Autonomous Characters. Game Developers Conference, Miller Freeman Game Group, San Francisco, CA, San Jose, CA, 1999.
Models including Psychology
Modelling social identification and helping in evacuation simulation. In: Safety Science, vol. 89, pp. 288–300, 2016, ISSN: 0925-7535.
Agent-Based Simulation of Collective Cooperation: From Experiment to Model. In: 0000.
Navigation Fields for Wayfinding
Pedestrian traffic: on the quickest path. In: Journal of Statistical Mechanics: Theory and Experiment, vol. 2009, no. 03, pp. P03012, 2009.
Adaptive pedestrian dynamics based on geodesics. In: New Journal of Physics, vol. 12, pp. 043032, 2010.
Navigation around pedestrian groups and queueing using a dynamic adaption of traveling. Hochschule München, 2013.
Towards faster navigation algorithms on floor fields. In: Hamdar, Samer H (Ed.): In Traffic and Granular Flow '17, pp. 307–315, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-11440-4.
Misc Models
Small Group Coherence
On modelling the influence of group formations in a crowd. In: Contemporary Social Science, vol. 6, no. 3, pp. 397–414, 2011.
Pedestrian Group Behavior in a Cellular Automaton. In: Weidmann, Ulrich; Kirsch, Uwe; Schreckenberg, Michael (Ed.): Pedestrian and Evacuation Dynamics 2012, pp. 807–814, Springer International Publishing, 2014.
Seating Behavior
Modelling passengers' seating behavior for simulations of pedestrian dynamics. Munich University of Applied Sciences, 2016.