An Immune Memory and Negative Selection Based Decision Support System to Monitor and Control Public Bus Transportation Systems

Mnif, Salima; Elkosantini, Sabeur; Darmoul, Saber; Saïd, Lamjed Ben

doi:10.1016/j.ifacol.2016.07.104

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…We design a control system where information is distributed and decisions are decentralized over a set of “intelligent buses.” Each bus is able to make its own control decisions in a reactive manner to keep as close as possible to its preestablished timetable. Our suggestions extend the works of Mnif et al by providing a detailed description of a multiagent system to control public bus transportation systems. We provide a detailed representation of immune concepts, implementation of immune mechanisms in terms of learning and decision‐making algorithms, and their implementation within a hybrid multiagent system architecture.…”

Section: Biological and Artificial Immune Systemssupporting

confidence: 54%

“…It is able to memorize pathogens (learning feature) and to reuse this knowledge during future encounters with similar or identical substances (adaptation feature). Our suggestions extend the works of Mnif et al 42,43 by providing a detailed description of a multiagent system to control public bus transportation systems. 26 Artificial immune systems are widely used in many domains, such as traffic control, 27,28 monitoring and control, 29-31 fault detection and anomaly recovery, 32-34 optimization, [35][36][37][38] and mobile robotics.…”

Section: Biological and Artificial Immune Systemsmentioning

confidence: 52%

“…The authors particularly developed a proof‐of‐concept prototype implementation on a single bus line to demonstrate the validity, pertinence, and usefulness of their suggestions. Mnif et al introduced a set of models to structure knowledge related to both disturbances and control decisions, as well as monitoring and control mechanisms based on immune memory and negative selection principles. These knowledge models and immune mechanisms were implemented as a multiagent decision support system to control disturbances in public bus transportation systems …”

Section: Biological and Artificial Immune Systemsmentioning

confidence: 99%

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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: Biological and Artificial Immune Systemssupporting

confidence: 54%

Section: Biological and Artificial Immune Systemsmentioning

confidence: 52%

Section: Biological and Artificial Immune Systemsmentioning

confidence: 99%

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

Mnif

Darmoul

Elkosantini

et al. 2018

Computational Intelligence

Self Cite

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Integration of Immune Features into a Belief-Desire-Intention Model for Multi-agent Control of Public Transportation Systems

Mnif¹,

Darmoul

Elkosantini

et al. 2017

Lecture Notes in Computer Science

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Simulation of Agent-Based Negative Selection Model (ABNSM) for Reservoir Water Level Monitoring

Doi

Norwawi

Ismail

et al. 2020

J. Phys.: Conf. Ser.

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Reservoir water level monitoring is an important process during heavy or light rainfall to determine the volume of reserved water. Mistakes in data recording by the dam operator can lead to disasters. Data from different gauging stations are collected to determine whether to release water in the dam or not. The decision to release water is critical because it can affect the volume of water left in the dam for both drought and flood seasons. Constant water level monitoring is difficult because of the changes in water level. To overcome this issue, intelligent agent-based architecture is proposed for reservoir water level monitoring by imitating the artificial immune system. This paper presents the agent technology where agents communicate with each other concurrently by sending online data from different gauging stations to the main reservoir. One of the techniques in the artificial immune system is known as negative selection and this technique has been chosen as a water level monitoring model.

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An Immune Memory and Negative Selection Based Decision Support System to Monitor and Control Public Bus Transportation Systems

Cited by 5 publications

References 7 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

Integration of Immune Features into a Belief-Desire-Intention Model for Multi-agent Control of Public Transportation Systems

Simulation of Agent-Based Negative Selection Model (ABNSM) for Reservoir Water Level Monitoring

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