Distributed intelligence in pedestrian simulations

Cavens, Duncan; Gloor, Christian; Illenberger, Johannes; Lange, Eckart; Nagel, Kai; Schmid, Wolfgang

doi:10.1007/978-3-540-47064-9_18

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The multi-agent systems (MAS) offer the possibility to directly represent behaviors and interactions between individuals to explain and predict the evolution of complex phenomena. For instance, the simulation developed by Cavens et al model [39] use multi-agents based simulation. Simulation software is divided into distinct modules, which interact with each other via network messages.…”

Section: Pedestrian Simulationmentioning

confidence: 99%

Fuzzy Ant Colony Paradigm for Virtual Pedestrian Simulation

Boulmakoul¹

2011

TOORJ

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Pedestrian's studies are applied benefit and deployed in many areas; including public trip planning, and human factors analysis for building evacuation, or other situations where masses of people gather such as sporting events, etc. In this paper we describe a simulation system for virtual pedestrians. We mix the concept of fuzzy ant given by Ant Colony paradigm and associated cellular automaton model. We have adopted a fuzzy model for its particular ability to better represent uncertainty and imprecision about space perception. In our model we use it to represent virtual pedestrian's fuzzy pedestrian's desirability or visibility. A software solution was developed for this purpose. The Simulation results confirm predictions given by the first-order traffic flow theory. The proposed fuzzification is a preliminary step to consider other factors. The given system in its deployment phase will be integrated for pedestrians' accidents analysis in urban transportation networks. The strength of our model takes advantage in using explicit Ant Colony model and on projecting fuzzy theory to apprehend pedestrians' perceptions.

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Section: Pedestrian Simulationmentioning

confidence: 99%

Fuzzy Ant Colony Paradigm for Virtual Pedestrian Simulation

Boulmakoul¹

2011

TOORJ

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“…proactive tourism planning [12,14] • spatio-temporal zoning of the territory in relation to tourism (e.g., [8,12,14]), • monitoring, modelling and forecasting visitor flows, motivation, behaviour and impacts of visitors [2,12,[14][15][16][17][18][19][20][21][22][23][24] • concept of visiting limitations to the area [14] in the frame of tourism-carrying capacity [1,8,[25][26][27] and/or limits of acceptable changes (LAC; [1,8,12,28,29]), including various visitor management models [30] and considering different distributions of visitor pressure on the tourism carrying capacity of the territory [31] • preserving the visitor experience with an emphasis on its multi-component character [32]; this approach is the basis of some visitor management models [14] and a measure of the success of visitor management [33] • the establishment of visiting rules in connection with the appropriate development of the visitor infrastructure (type and conduct of trails, viewpoints, visitor centres, etc. ; [12,14]), and informing visitors and interpreting local heritage [34] • modelling and predicting tourism-activated processes and changes in ecosystems, cultural and "tourism visiting space" in protected areas [17,30,…”

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confidence: 99%

Agent-Based Modelling in Visitor Management of Protected Areas

2022

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The research was motivated by the growing importance of visitor management in protected areas, which can be based on knowledge management, system modelling of processes and phenomena, and a deeper knowledge of the experience of visitors in connection with the concept of psychological carrying capacity. The work builds on previous publications and research by the authors, focused on the optimization of tourism impacts, visitor management and the development of the theory and applicability of the concept of carrying capacity. It emphasizes the overview analysis of the possibilities of using agent-based modelling and visualization of visitor flows in protected areas. The analysis of suitable sources was based on the PRISMA method, which showed the main research directions for the use of the agent-based approach in visitor management. For the practical application of modelling, the NetLogo environment was chosen, in which the visitor flows of the model area were simulated. The visitor attendance was evaluated in relation to the psychological carrying capacity. Subsequently, visitor management measures were implemented in the model, and repeated simulations of visitor attendance, based on monitored flows, were run for a specific location around Oheb Castle (the Železné hory/Iron Mountains, Bohemia). The main result is the innovative use of agent-based modelling in visitor management in the context of visitor experience, visitor satisfaction and psychological carrying capacity. The contribution of the presented research is also the proposal of future research directions for more accurate use of psychological carrying capacity in visitor management.

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Pedestrian and Evacuation Dynamics 2005

Dynamics¹,

Waldau²

2007

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Distributed intelligence in pedestrian simulations

Cited by 3 publications

References 3 publications

Fuzzy Ant Colony Paradigm for Virtual Pedestrian Simulation

Fuzzy Ant Colony Paradigm for Virtual Pedestrian Simulation

Agent-Based Modelling in Visitor Management of Protected Areas

Pedestrian and Evacuation Dynamics 2005

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