Social Force Model for Pedestrian Dynamics

Dirk Helbing; Peter Molnar

Social Force Model for Pedestrian Dynamics

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arxiv cond-mat/9805244 v1 pith:437CMULM submitted 1998-05-20 cond-mat.stat-mech nlin.PSpatt-sol

Social Force Model for Pedestrian Dynamics

Dirk Helbing , Peter Molnar This is my paper

classification cond-mat.stat-mech nlin.PSpatt-sol

keywords forcepedestrianpedestriansmodelmotionsocialbehaviorcertain

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It is suggested that the motion of pedestrians can be described as if they would be subject to `social forces'. These `forces' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.

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Cited by 2 Pith papers

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Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
cs.LG 2026-05 unverdicted novelty 5.0

Co-training an SDC and 12 pedestrians with MAPPO in a MARL setup yields 78% goal success and 14% collisions versus 35% goals and 33% for the best rule-based baseline, with jaywalking linked to 62% of collisions despit...
Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
cs.LG 2026-05 unverdicted novelty 4.0

Co-training an SDC and pedestrians with MAPPO yields 78% goal success and 14% collisions versus 35%/33% for rule-based baselines, with jaywalking causing 62% of collisions and evidence of poor anticipation via speed d...