An immune multiagent system to monitor and control public bus transportation systems. (11th June 2018)

• Record Type: Journal Article
• Authors: Mnif, Salima; Darmoul, Saber; Elkosantini, Sabeur; Ben Said, Lamjed
• Is Part Of: Computational intelligence. Volume 34:Number 4(2018)
• Page Start: 1245
• Page End: 1276
• Publication Date: 2018-06-11
• Subjects: artificial immune system -- biological immune system -- immune memory -- multiagent system -- negative selection theory -- public transport control system; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12181
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.