A unified pedestrian routing model for graph-based wayfinding built on cognitive principles

Kielar, Peter M.; Biedermann, Daniel H.; Kneidl, Angelika; Borrmann, André

doi:10.1080/23249935.2017.1309472

Cited by 19 publications

(13 citation statements)

References 41 publications

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“…A tactical hypothesis model has to provide an assumption of how the participants determine a walking target. We apply a graph-based model that includes a graph-generation schema and a routing method [18]. This provides sufficient detail for our purpose.…”

Section: Replay Phase: Hypothesis Modelsmentioning

confidence: 99%

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

Kielar

Borrmann

2020

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Movement behavior models of pedestrian agents form the basis of computational crowd simulations. In contemporary research, a large number of models exist. However, there is still no walking behavior model that can address the various influence factors of movement behavior holistically. Thus, we endorse the use of artificial neural networks to develop walking behavior models because machine learning methods can integrate behavioral factors efficiently, automatically, and data-driven. In this paper, we support this approach by providing a framework that describes how to include artificial neural networks into a pedestrian research context. The framework comprises 5 phases: data, replay, training, simulation, and validation. Furthermore, we describe and discuss a prototype of the framework.

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Section: Replay Phase: Hypothesis Modelsmentioning

confidence: 99%

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

Kielar

Borrmann

2020

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“…Their findings were supported by experimental results. Kielar et al proposed a pedestrian routing model for graph-based way finding, which is built on cognitive principles [13,14]. Therefore, a graph-based navigation strategy would be reasonable to be utilized in this work, while the Dijkstra shortest path algorithm is implemented to figure out possible evacuation routes.…”

Section: Introductionmentioning

confidence: 99%

A Memory-Based Evacuation Navigation Model in Complex High-Rise Buildings

Long

Song

2022

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In contemporary society, safety issues are the main focus in the field of pedestrian and evacuation dynamics. As for complex high-rise buildings, the navigation strategies of evacuees still need to be further studied. Previous types of research has contributed to the construction of evacuation navigation model in complex high-rise buildings, where pedestrians are regarded as having an omniscient view in most of these models. In reality, evacuees’ perception is always limited, especially when the scenario is complex. In this contribution, pedestrians’ perception procedure is considered by computing the visible space so that the occlusion of the visual field can be estimated. In addition, human memory progress is modeled. Not all parts of environmental information would be remembered. Driven by evacuees’ memory data, a proposed dynamical shortest path algorithm will be periodically implemented or suddenly triggered by incidents during the simulation. For the pedestrians who have no enough knowledge about the evacuation scenario, a communication system is utilized so that information can be obtained from well-knowledged pedestrian, and an autonomous way-finding system will be executed when useful information cannot be acquired through near evacuees. For the microscopic perspective, human following and avoidance behavior is modeled. Simulations in two different types of scenarios are conducted. The knowledge level of simulated agents is gradually evolved by in-room free exploration. Results in different conditions show that the proposed memory-based model can reproduce pedestrians’ observation, turn-back, communication, and searching behavior. The intense conflict caused by the bidirectional crowd is observed and analyzed. In addition, the effects of knowledge level are investigated. The presented model in this contribution can be promising and useful in safety engineering.

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“…This process relies on pedestrians having some form of global knowledge of their environment, and it has received wide attention in research (see e.g. Kielar et al (2018) for an overview). On the other hand, the route pedestrians take may emerge from a sequence of decisions, each of which is taken at a junction of roads.…”

Section: Introductionmentioning

confidence: 99%

The value pedestrians attribute to environmental information diminishes in route choice sequences

Tong

Bode

2021

Transportation Research Part C: Emerging Technologies

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A unified pedestrian routing model for graph-based wayfinding built on cognitive principles

Cited by 19 publications

References 41 publications

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

A Memory-Based Evacuation Navigation Model in Complex High-Rise Buildings

The value pedestrians attribute to environmental information diminishes in route choice sequences

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