Open AccessDecentralised intelligent transport system with distributed intelligence based on classification techniques
Author(s) -
Osaba Eneko,
Onieva Enrique,
Moreno Asier,
LopezGarcia Pedro,
Perallos Asier,
Bringas Pablo G.
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0047
Subject(s) - scalability , intelligent transportation system , computer science , architecture , process (computing) , distributed computing , systems architecture , intelligent decision support system , advanced traffic management system , work (physics) , artificial intelligence , engineering , transport engineering , database , mechanical engineering , art , visual arts , operating system
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.