Note on “A new bidirectional algorithm for shortest paths”

Pijls, Wim; Post, Henk

doi:10.1016/j.ejor.2010.06.003

Cited by 12 publications

(13 citation statements)

References 2 publications

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“…In order to optimize the spatial computation, and more particularly the spatial query, GAMA uses a dynamic Quadtree structure that is updated according to the movement of the agents. Regarding the computation of shortest paths on graphs, GAMA proposes different algorithms such as Dijkstra, Floyd Warshall, or NBA* [53]. At last, several algorithms are also available to compute shortest paths and distances on a grid (with the possibility to consider obstacles) such as Dijkstra, A*, or JPS [39].…”

Section: Spatial Component Of Modelsmentioning

confidence: 99%

Building, composing and experimenting complex spatial models with the GAMA platform

Taillandier¹,

et al. 2018

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Taillandier AbstractThe agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decisionmaking processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

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Section: Spatial Component Of Modelsmentioning

confidence: 99%

Building, composing and experimenting complex spatial models with the GAMA platform

Taillandier¹,

et al. 2018

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“…the time of departure is calculated based on expected travel time and time of appointment considering walking and motorized movement with constant speed. The path and modes to use are computed using behavioral rules and shortest path algorithm: agent will choose the most efficient mode in term of time, while computing shortest path using NBA* algorithm [19]. On the tactical level, each agent is able to update its path when changes in the environment does not let them move to their current destination, including the ability to change the mode of transportation.…”

Section: Mobile Entities: People and Vehicle Agentsmentioning

confidence: 99%

A Multi-modal Urban Traffic Agent-Based Framework to Study Individual Response to Catastrophic Events

Chapuis¹,

Taillandier²,

Gaudou³

et al. 2018

Lecture Notes in Computer Science

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Chapuis, KévinAbstract. Urban traffic is made of a variety of mobility modes that have to be taken into account to explore the impact of catastrophic event. From individual mobility behaviors to macroscopic traffic dynamics, agent-based modeling provides an interesting conceptual framework to study this question. Unfortunately, most proposals in the domain do not provide any simple way to model these multi-modal trajectories, and thus fell short at simulating in a credible way the outcomes of a catastrophic event, like natural or industrial hazards. This paper presents an agent-based framework implemented with the GAMA modeling platform that aims at overcoming this lack. An application of this model for the study of flood crisis in a district of Hanoi (Vietnam) is presented.

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“…Searching path process will be divided into two part, (1) from start node to goal node, and (2) from goal node to start node. They will stopped if they meet in the halfway [9] [10].…”

Section: Cs-2-1 Designing and Implementation Of Autonomous Hexarotor mentioning

confidence: 99%

Optimized A-Star algorithm in hexagon-based environment using parallel bidirectional search

Saian

Suyoto

Pranowo

2016

2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE)

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Note on “A new bidirectional algorithm for shortest paths”

Cited by 12 publications

References 2 publications

Building, composing and experimenting complex spatial models with the GAMA platform

Building, composing and experimenting complex spatial models with the GAMA platform

A Multi-modal Urban Traffic Agent-Based Framework to Study Individual Response to Catastrophic Events

Optimized A-Star algorithm in hexagon-based environment using parallel bidirectional search

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