McCluskey, T.L., Kotsialos, A., Müller, J.P., Klugl, F. and Schumann, R. (2016) Autonomic Road Transport Support Systems. Autonomic Systems . Springer, London, UK. ISBN 978-3-319-25806-5

The work on Autonomic Road Transport Support (ARTS) presented here aims at
meeting the challenge of engineering autonomic behavior in Intelligent Transportation
Systems (ITS) by fusing research from the disciplines of traffic engineering
and autonomic computing. Ideas and techniques from leading edge artificial intelligence
research have been adapted for ITS over the last years. Examples include
adaptive control embedded in real time traffic control systems, heuristic algorithms
(e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated
surveillance interpretation). Autonomic computing which is inspired from the
biological example of the body’s autonomic nervous system is a more recent development.
It allows for a more efficient management of heterogeneous distributed
computing systems. In the area of computing, autonomic systems are endowed
with a number of properties that are generally referred to as self-X properties,
including self-configuration, self-healing, self-optimization, self-protection and more
generally self-management. Some isolated examples of autonomic properties such
as self-adaptation have found their way into ITS technology and have already proved
beneficial. This edited volume provides a comprehensive introduction to Autonomic
Road Transport Support (ARTS) and describes the development of ARTS systems. It
starts out with the visions, opportunities and challenges, then presents the foundations
of ARTS and the platforms and methods used and it closes with experiences
from real-world applications and prototypes of emerging applications. This makes
it suitable for researchers and practitioners in the fields of autonomic computing,
traffic and transport management and engineering, AI, and software engineering.
Graduate students will benefit from state-of-the-art description, the study of novel
methods and the case studies provided.

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