Dynamic Medium Scale Navigation Using Dynamic Floor Fields

Hartmann, Dirk; Mille, Jana; Pfaffinger, Alexander; Royer, Christian

doi:10.1007/978-3-319-02447-9_102

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Some of the most recent enhancements in crowd modeling rely on a floor field to steer pedestrians towards the target. Among them are steering around crowd clusters [4,31] and more sophisticated navigation on the tactical and strategic level [5]. These developments can be employed in the GNM without any change to the equations of motion.…”

Section: Discussionmentioning

confidence: 99%

“…Static properties of a geometry (for example rough terrain or an obstacle) can be modelled by modifying the speed function G : R 2 → (0, +∞). [31] include the pedestrian density in G. This enables pedestrians to locate congestions and then take a different exit route. [32] used the eikonal equation to steer very large virtual crowds.…”

Section: The Navigation Fieldmentioning

confidence: 99%

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Gradient navigation model for pedestrian dynamics

Dietrich

Köster

2014

Phys. Rev. E

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We present a new microscopic ODE-based model for pedestrian dynamics: the Gradient Navigation Model. The model uses a superposition of gradients of distance functions to directly change the direction of the velocity vector. The velocity is then integrated to obtain the location. The approach differs fundamentally from force based models needing only three equations to derive the ODE system, as opposed to four in, e.g., the Social Force Model. Also, as a result, pedestrians are no longer subject to inertia. Several other advantages ensue: Model induced oscillations are avoided completely since no actual forces are present. The derivatives in the equations of motion are smooth and therefore allow the use of fast and accurate high order numerical integrators. At the same time, existence and uniqueness of the solution to the ODE system follow almost directly from the smoothness properties. In addition, we introduce a method to calibrate parameters by theoretical arguments based on empirically validated assumptions rather than by numerical tests.These parameters, combined with the accurate integration, yield simulation results with no collisions of pedestrians. Several empirically observed system phenomena emerge without the need to recalibrate the parameter set for each scenario: obstacle avoidance, lane formation, stop-and-go waves and congestion at bottlenecks. The density evolution in the latter is shown to be quantitatively close to controlled experiments. Likewise, we observe a dependence of the crowd velocity on the local density that compares well with benchmark fundamental diagrams.

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Section: Discussionmentioning

confidence: 99%

Section: The Navigation Fieldmentioning

confidence: 99%

Gradient navigation model for pedestrian dynamics

Dietrich

Köster

2014

Phys. Rev. E

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“…Therefore, in Vadere, we only use the geodesic distance to a target. The minimal geodesic path can be the shortest as well as the fastest path [32,48]. Solving the eikonal equation is a widely used approach to obtain the geodesic distance between a destination and each position in the topography.…”

Section: Common Ground For All Locomotion Modelsmentioning

confidence: 99%

“…To achieve more realistic medium scale avoidance behavior, one can choose a speed function f for the wave front that depends on the pedestrian density. By lowering f at crowded locations, that is slowing the wave down, medium-scale avoidance of other pedestrians can be modeled [32,48]. In this case, the floor field becomes dynamic and must be recomputed regularly.…”

Section: Common Ground For All Locomotion Modelsmentioning

confidence: 99%

Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding

et al. 2019

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Pedestrian dynamics is an interdisciplinary field of research. Psychologists, sociologists, traffic engineers, physicists, mathematicians and computer scientists all strive to understand the dynamics of a moving crowd. In principle, computer simulations offer means to further this understanding. Yet, unlike for many classic dynamical systems in physics, there is no universally accepted locomotion model for crowd dynamics. On the contrary, a multitude of approaches, with very different characteristics, compete. Often only the experts in one special model type are able to assess the consequences these characteristics have on a simulation study. Therefore, scientists from all disciplines who wish to use simulations to analyze pedestrian dynamics need a tool to compare competing approaches. Developers, too, would profit from an easy way to get insight into an alternative modeling ansatz. Vadere meets this interdisciplinary demand by offering an open-source simulation framework that is lightweight in its approach and in its user interface while offering pre-implemented versions of the most widely spread models.

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“…Typically, F is chosen to be 1 outside obstacles making the arrival time independent of surface conditions. More difficult terrain can be mapped by varying F [22], as can be crowd avoidance [49,24,46] or, in fact, simple queuing. See [46] for queuing and a typical queue in Fig.…”

Section: Utility Functions and Parameter Choicesmentioning

confidence: 99%

Dynamic stride length adaptation according to utility and personal space

Sivers

Köster

2015

Transportation Research Part B: Methodological

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a b s t r a c tPedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They try to avoid collisions and to preserve their personal space. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the pedestrians could go. The stride length and its correct adaptation, however, are rarely considered. This leads to artefacts that impact macroscopic observation parameters such as densities in front of bottlenecks and, through this, flow. Hence modelling stride adaptation is important to increase the predictive power of pedestrian models. To achieve this we reformulate the problem as an optimisation problem on a disk around the pedestrian. Each pedestrian seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need for individual space and the need to keep a distance from obstacles. The need for space is modelled according to findings from psychology defining zones around a person that, when invaded, cause unease. The result is a fully automatic adjustment that allows calibration through meaningful social parameters and that gives visually natural results with an excellent fit to measured experimental data.

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Dynamic Medium Scale Navigation Using Dynamic Floor Fields

Cited by 8 publications

References 20 publications

Gradient navigation model for pedestrian dynamics

Gradient navigation model for pedestrian dynamics

Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding

Dynamic stride length adaptation according to utility and personal space

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