Decentralised intelligent transport system with distributed intelligence based on classification techniques. Issue 10 (5th October 2016)

• Record Type: Journal Article
• Authors: Osaba, Eneko; Onieva, Enrique; Moreno, Asier; Lopez‐Garcia, Pedro; Perallos, Asier; Bringas, Pablo G.
• Is Part Of: IET intelligent transport systems. Volume 10:Issue 10(2016)
• Page Start: 674
• Page End: 682
• Publication Date: 2016-10-05
• Subjects: traffic engineering computing -- distributed processing -- artificial intelligence -- pattern classification -- intelligent transportation systems; decentralised intelligent transport system -- distributed intelligence -- classification techniques -- available road data management -- available road data capture -- artificial intelligence techniques -- C4.5 algorithm -- traffic congestion prediction -- pollution level prediction; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2016.0047
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling the generation of services to promote transportation efficiency. Besides that, thanks to the embedded classification techniques, the system is capable of predicting and reacting to certain events, facing them in an appropriate way. The aim of this work is to demonstrate how the system works in two different real‐world use cases. To achieve this objective, how the architecture acts to deal with some incidences is proven. In addition, both use cases serve to show the effective communication between the different components of the system. Besides this, this work demonstrates the fundamental role played by the artificial intelligence techniques working in the system. The well‐known C4.5 algorithm has been used for the accurate prediction of traffic congestion and pollution level. The authors explain in this work the reasons for using this classification technique, and the previous experiments performed.