The Comparison of Stigmergy Strategies for Decentralized Traffic Congestion Control: Preliminary Results

Ito, Takayuki; Kanamori, Ryo; Takahashi, Jun; Iv,; Maestre, n Marsa; Hoz, Enrique de la

doi:10.1007/978-3-642-32695-0_15

Cited by 13 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some models using stigmergy have reversed it to indicate future rather than past actions and interpreting them for predictions, for example in vehicle routing [13] or traffic congestion management [4,10]. None of these models explicitly consider collision avoidance, but lack of collisions is an implicit goal.…”

Section: Related Worksupporting

confidence: 39%

“…ASCA agents use an anticipatory stigmergy-based solution [10] for collision avoidance. Unlike standard stigmergybased models, in which agents deposit pheromones indicating where they have been, anticipatory stigmergy uses the principle of stigmergy in a different way.…”

Section: Overview Of Modelmentioning

confidence: 45%

“…Anticipatory stigmergy solutions for traffic congestion solutions [10] use pheromones to "reserve" a space, thus preventing other agents from passing over this space at the time it is reserved by another agent. In highly-congested environments, this can lead to less efficient outcomes as an agent cannot find space to navigate towards its goal.…”

Section: Overview Of Modelmentioning

confidence: 46%

“…This approach uses the notion of anticipatory stigmergy [10] for agents to coordinate with each other to avoid collisions. Unlike standard stigmergy-based models, in which agents deposit pheromones to inform other agents what actions they have taken, anticipatory stigmergy deposits pheromones to inform others of intended future actions.…”

Section: Introductionmentioning

confidence: 47%

See 3 more Smart Citations

Anticipatory stigmergic collision avoidance under noise

Osten

Kirley

Miller

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Reactive path planning to avoid collisions with moving obstacles enables more robust agent systems. However, many solutions assume that moving objects are passive; that is, they do not consider that the moving objects are themselves re-planning to avoid collisions, and thus may change their trajectory. In this paper we present a model, Anticipatory Stigmergic Collision Avoidance (ASCA) for reciprocal collision avoidance using anticipatory stigmergy. Unlike standard stigmergy, in which agents leave pheromones to indicate a trace of previous actions, anticipatory stigmergy deposits pheromones on intended future paths. By sharing their intended future paths with each other at regular intervals, agents can re-plan to attempt to avoid collisions. We experimentally evaluate ASCA over three scenarios, and compare with a state of art approach, Reciprocal Velocity Obstacles (RVO). Our evaluation showed that ASCA is consistently more robust in noisy environments in which transmitted information can be lost or degraded. Further, using ASCA without noise results in fewer collisions than RVO when agents are in formation, but more collisions when formed randomly.

show abstract

Section: Related Worksupporting

confidence: 39%

Section: Overview Of Modelmentioning

confidence: 45%

Section: Overview Of Modelmentioning

confidence: 46%

Section: Introductionmentioning

confidence: 47%

See 2 more Smart Citations

Anticipatory stigmergic collision avoidance under noise

Osten

Kirley

Miller

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…This research tries to enable to construct a globally-optimized social systems, which have not been realized yet, by using new multi agent algorithms that employ the pricing mechanisms, the matching mechanisms, and the scoring mechanisms with the computing power and network infrastructures. The method ology developed by this research will be able to be applied to smart grids [28], [29], [30], congestion management [31], [32], [33], sensor networks with the computing power and network infrastructures. Further, we can anticipate that the result will be applicable to recovering planning systems against huge scale disasters.…”

Section: Introductionmentioning

confidence: 99%

Innovating multiagent algorithms for smart city: An overview

Ito

Chakraborty

Kanamori

et al. 2012

2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA)

Self Cite

View full text Add to dashboard Cite

This research tackles the grand challenge on establishing multi-agent based computational social mechanism design theories, in which we focus on developing novel social systems and mechanisms that are globally optimized by using computers and networks, and apply them to society simulations and real world applications. This research tries to enable to construct a globally-optimized social systems, which have not been realized yet, by using new multi-agent algorithms that employ pricing mechanisms, matching mechanisms, and scoring mechanisms with the computing power and network infrastructures. In this paper, we introduce the overall vision of this project and present some of the current research results.

show abstract

A Study of Route Assignment Strategy Based on Anticipatory Stigmergy

Kanamori

Takahashi

Ito

2016

Elect Comm in Japan

Self Cite

View full text Add to dashboard Cite

SUMMARY In this paper, we evaluate how to provide or control traffic information based on anticipatory stigmergy. Managing traffic congestion is one of the main issues for smart cities, and many efforts have been made to address it from the perspective of IT and transportation research. Recently, dynamic coordination methods have been made possible by using shorter term traffic information that can be gathered by probe vehicles or smartphones. Some attempts have been made to handle short‐term traffic information in which a stigmergy‐based approach is employed as an indirect communication method for cooperation among distributed agents and for managing traffic congestion. One drawback of these approaches is that handling near‐future congestion remains difficult because stigmergies basically represent past information. Therefore, we propose anticipatory stigmergy for sharing information about the near future and adequate driver allocation. All vehicles submit their near‐future intentions as anticipatory stigmergy in order to search for routes. Preliminary results demonstrate that anticipatory stigmergy with an assignment strategy functions well.

show abstract

The Comparison of Stigmergy Strategies for Decentralized Traffic Congestion Control: Preliminary Results

Cited by 13 publications

References 11 publications

Anticipatory stigmergic collision avoidance under noise

Anticipatory stigmergic collision avoidance under noise

Innovating multiagent algorithms for smart city: An overview

A Study of Route Assignment Strategy Based on Anticipatory Stigmergy

Contact Info

Product

Resources

About