Ontologies and agents for a bus fleet management system☆

Belmonte, María‐Victoria; Pérez-de-la-Cruz, José-Luís; Triguero, Francisco Herrera

doi:10.1016/j.eswa.2007.01.003

Cited by 25 publications

(13 citation statements)

References 14 publications

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“…6 Hierarchical architectures use some kind of organizational structure where a hierarchy of authority exists. 2,7,[12][13][14][15] Agents at lower levels send information to agents at upper levels, which make decisions to coordinate and synchronize activities and decisions of lower-level agents. 2,7,[12][13][14][15] Agents at lower levels send information to agents at upper levels, which make decisions to coordinate and synchronize activities and decisions of lower-level agents.…”

Section: Distributed Control Of Disturbed Public Bus Transportation Smentioning

confidence: 99%

An immune multiagent system to monitor and control public bus transportation systems

Mnif

Darmoul

Elkosantini

et al. 2018

Computational Intelligence

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In public bus transportation systems, several types of disturbances, such as accidents and congestion, may affect preestablished timetables and visiting hours at stations. Disturbances result in detrimental consequences, degraded performance, and poor quality of service in terms of extended delays and waiting times, punctuality, frequency, and efficiency of shuttles. Despite numerous research on the monitoring and control of public transportation systems by means of buses, distributed monitoring and control architectures that confer intelligence and decision-making autonomy to buses to react to disturbances are still missing. This article addresses this gap by designing and developing a distributed architecture to monitor and control public transportation buses using multiagent systems. The design relies on biological immunity as a methodological framework that guides the development of knowledge models and decision-making mechanisms. Knowledge models structure knowledge about disturbances and control decisions, whereas decision-making mechanisms implement control and reaction strategies. Through experimental validation based on simulation, we show that the suggested immune multiagent distributed control architecture is not only able to maintain performance (average delay/earliness, average total time in simulated network) at acceptable levels but also to improve quality of service in terms of number of served passengers and stations by at least 15% in case of disturbances. KEYWORDS artificial immune system, biological immune system, immune memory, multiagent system, negative selection theory, public transport control system

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Section: Distributed Control Of Disturbed Public Bus Transportation Smentioning

confidence: 99%

An immune multiagent system to monitor and control public bus transportation systems

Mnif

Darmoul

Elkosantini

et al. 2018

Computational Intelligence

View full text Add to dashboard Cite

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“…Multi-Agent Systems have been used in various ITS applications such as urban traffic control [16]- [19], fleet management [21], [22], and route guidance systems [24] [25] In these approaches, different multi-agent frameworks have been used as the software tools to implement the multi-agent environments amongst which Matsim [26] and Emme/2 [27] are the two well-known frameworks.…”

Section: B Computational Modelsmentioning

confidence: 99%

Carbon Efficient Transport Management Using Multi-agent System

Namoun

Akhlaghinia

Wajid

2014

2014 IEEE 23rd International WETICE Conference

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This paper presents a carbon efficient transport management system that uses agent-based technology to simulate the existing transport infrastructure of a city. By utilizing realtime information about the current use of transport infrastructure from different data sources (e.g. road-side sensors) the system is capable of providing guidance to everyday commuters. The guidance is primarily in the form of multi-mode route suggestions with the aim of reducing overall carbon-dioxide emissions. This paper describes the architecture of the transport management system along with the design details of multi-agent system. The initial results of the simulations validate the feasibility of the multi-agent system in providing carbon-efficient transport management for an urban city.

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“…Thus the ontology allows agents to infer and understand knowledge of other agents [22]. The common ontology defines the vocabulary with which queries and assertions are exchanged among agents [3].…”

Section: Ontologies and Multi Agent Systemsmentioning

confidence: 99%

A multi agent model to support the knowledge management process inside professional activities

Monticolo

Hilaire

Koukam

et al. 2007

2007 2nd International Conference on Digital Information Management

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This article describes the design of a multi agent software model to identify, synthesize and reuse knowledge all along engineering design projects. As a matter of fact, these projects require that engineers, with different specialities, collaborate to carry out the same goal. Inside professional processes, they use their know-how and knowledge in order to achieve the laid down goals. Our approach consists in using an organisational approach to identify the competences and knowledge of professional actors which allows the design and the description of agents' know-how. Furthermore, the paper describes the design of our agent model based on an organisational approach and the role of a domain ontology called OntoDesign to manage heterogeneous and distributed knowledge.

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Ontologies and agents for a bus fleet management system☆

Cited by 25 publications

References 14 publications

An immune multiagent system to monitor and control public bus transportation systems

An immune multiagent system to monitor and control public bus transportation systems

Carbon Efficient Transport Management Using Multi-agent System

A multi agent model to support the knowledge management process inside professional activities

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