Application of multi-agent systems in traffic and transportation

Burmeister, Birgit; Haddadi, Afsaneh; Matylis, Guido

doi:10.1049/ip-sen:19971023

Cited by 150 publications

(65 citation statements)

References 2 publications

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“…This is because it can represent important phenomena resulting from the characteristics and behaviors of individual agents and their interactions (Railsback & Grimm 2012). (Burmeister et al 1997) discuss the benefits of using an agent-based approach in domains that are functionally or geographically distributed into autonomous subsystems, where the subsystems exist in a dynamic environment, and the subsystems have to interact more flexibly. According to Burmeister, agent-based modeling can be used to structure and appropriately combine the information into a comprehensible form.…”

Section: Abms and Complex Socio-technical Systemsmentioning

confidence: 99%

“…So agent-based modeling provide abstraction levels that make it simpler and more natural to deal with the scale and complexity of problems in these systems. Agent components can be described at a high level of abstraction, yet the resulting systems are very efficient (Burmeister et al 1997). (Burmeister et al 1997) conclude that agent-based modeling reduce the complexity in systems design by making available abstraction levels that lend themselves to a more natural way of modeling the problem domain.…”

Section: Abms and Complex Socio-technical Systemsmentioning

confidence: 99%

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Agent-based modeling and simulation of emergent behavior in air transportation

Bouarfa

Blom

Curran

et al. 2013

Complex Adapt Syst Model

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Purpose: Commercial aviation is feasible thanks to the complex socio-technical air transportation system, which involves interactions between human operators, technical systems, and procedures. In view of the expected growth in commercial aviation, significant changes in this socio-technical system are in development both in the USA and Europe. Such a complex socio-technical system may generate various types of emergent behavior, which may range from simple emergence, through weak emergence, up to strong emergence. The purpose of this paper is to demonstrate that agent-based modeling and simulation allows identifying changed and novel rare emergent behavior in this complex socio-technical system.Methods: An agent based model of a specific operation at an airport has been developed. The specific operation considered is the controlled crossing by a taxiing aircraft of a runway that is in use for controlled departures. The agent-based model includes all relevant human and technical agents, such as the aircraft, the pilots, the controllers and the decision support systems involved. This agent-based model is used to conduct rare event Monte Carlo (MC) simulations. Results: The MC simulation results obtained confirm that agent based modeling and simulation of a socio-technical air transportation system allows to identify rare emergent behavior that was not identified through earlier, non-agent-based simulations, including human-in-the-loop simulations of the same operation. A typical example of such emergent behavior is the finding that alerting systems do not really reduce the safety risk. Conclusions: Agent based MC simulations of commercial aviation operations has been demonstrated as a viable way to be evaluated regarding rare emergent behaviour. This rare emergent behaviour could not have been found through the more traditional simulation approaches.

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Section: Abms and Complex Socio-technical Systemsmentioning

confidence: 99%

Section: Abms and Complex Socio-technical Systemsmentioning

confidence: 99%

Agent-based modeling and simulation of emergent behavior in air transportation

Bouarfa

Blom

Curran

et al. 2013

Complex Adapt Syst Model

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“…One was an en-route driver choice model and the other was a forest fire propagation model. The en-route driver choice model simulates a drivers' route choice using real-time travel information, e.g., road conditions and transportation congestion [20,21]. This model was used to validate the "agent parallel" strategy in 4D-SAS.…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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“…Multi-agent systems are gaining more and more attention since they can be utilized in a wide range of applications, such as search and rescue missions [1], [2], space exploration [3], [4], security patrols [5], [6], tra c control [7], [8], agricultural vehicles [9], garbage collection [10], mapping and land mine removal [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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Abstract-In this article a distributed model predictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is being presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two di↵erent control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the e ciency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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Application of multi-agent systems in traffic and transportation

Cited by 150 publications

References 2 publications

Agent-based modeling and simulation of emergent behavior in air transportation

Agent-based modeling and simulation of emergent behavior in air transportation

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Distributed model predictive control for unmanned aerial vehicles

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