Autonomic Road Transport Support Systems

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

Testing demand responsive shared transport services via agent-based simulations

Demand Responsive Shared Transport DRST services take advantage of Information and Communication Technologies ICT, to provide on demand transport services booking in real time a ride on a shared vehicle. In this paper, an agent-based model ABM is presented to test different the feasibility of different service configurations in a real context. First results show the impact of route choice strategy on the system performance

Sample representation in a psychological treatment study after single event paediatric trauma

Children and their families who attended an emergency department following a single traumatic incident and who agreed to participate in a psychological treatment study (N = 211) were compared with nonparticipants (N = 2333) on several measures of trauma and injury severity: duration of admission and heart rate in the emergency department, emergency transport and admission to hospital, injury severity score, and triage code. Within the nonparticipant population, those who requested further information about the study (N = 573) were exposed to more severe trauma or injury than other nonparticipants (N = 1760). In addition, participants were exposed to more severe trauma or injury than either group of nonparticipants. These observations indicate that those exposed to more severe trauma or injury do not avoid participation in psychological treatment studies. Findings can therefore be generalized to those with more severe exposure, but not to the population as a whole

Issues arising from the HASTE experiments

[FIRST PARAGRAPH] The HASTE project work discussed in the foregoing papers can be depicted as being aimed at answering two questions: “Does greater secondary task load from an In-Vehicle Information System (IVIS) lead to an identifiably worse performance in the primary task of driving?” and “How much distraction is too much?”. There is, of course, a huge amount of literature examining the effect of distraction on driving. Some of this concerns visual distraction (e.g. Holohan, Culler & Wilcox, 1978; Dingus, Antin, Hulse & Wierwille, 1989; Wierwille & Tijerina, 1996; Wallace, 2003), while other parts cover distraction from cognitive (auditory) tasks such as mobile phone use (e.g. Stevens & Paulo, 1999; Svenson & Patten, 2003). But, in spite of this large background of research, it can be argued that the HASTE work was pioneering in the sense that it attempted to differentiate between the effects of visual and cognitive distraction and at the same time it attempted to carefully control the “dose” of distraction administered at any one time. These dose-response studies were carried out in three common but quite different experimental settings, a laboratory set-up, advanced driving simulators, and in instrumented vehicles in the field. The project also examined the reliability of the evaluation, with for example six replications of the rural road studies across a variety of driving simulators in five different countries

COST : matchmaking for researchers

As many researchers are aware, while the EU offers a huge variety of funding possibilities, many of the funding mechanisms are tied to projects which require consortia made up of partners from different countries. Many researchers based in Malta, especially younger researchers, query on how one gets invited to participate in such consortia. In addition, the complex funding and administrative procedures involved in many of these EU projects, may put off several local scientists and researchers from applying for such funds. COST, Cooperation in Science and Technology, offers a facilitated mechanism which tackles and overcomes these hurdles.peer-reviewe

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199

This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

Self-Management in Urban Traffic Control – an Automated Planning Perspective

Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated