The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Efficient multi-agent path planning
Proceedings of the Eurographic workshop on Computer animation and simulation
Planning Algorithms
Real-Time Path Planning in Dynamic Virtual Environments Using Multiagent Navigation Graphs
IEEE Transactions on Visualization and Computer Graphics
Creating crowd variation with the OCEAN personality model
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Towards the calibration of pedestrian stream models
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part II
Editorial: Advances in architectural, engineering and construction informatics
Advanced Engineering Informatics
| Hi-index | 0.00 |
For the spatial design of buildings as well as for the layout of large event areas, the crowd behaviour of the future users plays a significant role. The designing engineer has to make sure that potentially critical situations, such as high densities in pedestrian crowds, are avoided in order to guarantee the integrity, safety and comfort of the users. To this end, computational pedestrian dynamics simulations have been developed and are increasingly used in practice. However, most of the available simulation systems rely on rather simple pedestrian navigation models, which reflect human behaviour only in a limited manner. This paper contributes to enhancing pedestrian simulation models by extending a microscopic model by a navigation graph layer serving as a basis for different routing algorithms. The paper presents an advanced method for the automated generation of a spatially embedded graph which is on the one hand as sparse as possible and on the other hand detailed enough to be able to serve as a navigation basis. Three different pedestrian types were modelled: pedestrians with good local knowledge, pedestrians with partly local knowledge and those without any local knowledge. The corresponding algorithms are discussed in detail. To illustrate how this approach improves on simulation results, an example scenario is presented to demonstrate the difference between results with and without using a graph as constructed here. Another example shows the application of the extended simulation in a real-world engineering context. The article concludes with an outlook of further potential application areas for such navigation graphs.