The accident risk of motorcyclists

This report contains the findings of a study to explore and quantify the interacting influences which determine motorcyclist accident liabilities. It was conducted on behalf of Road Safety Division, Department for Transport. The study first reviewed existing data sources to investigate the trends in motorcycling accidents over the last decade or so. The main part of the study was to carry out a survey of nearly 30,000 current motorcyclists in order to explore the relationship between accident risk and variables such as annual mileage, age, experience, journey type, training, personal characteristics of the riders, and the self-reported behaviours and attitudes of the riders. The numbers of accidents reported by riders within the past 12-months of riding were modelled using generalised linear techniques to take into account factors such as mileage, age, experience, bike size and the conditions prevailing when they rode. Models of rider behaviour were developed using other statistical modelling techniques. These models investigated how attitudes/motivations/perceptions and rider style influence rider behaviour, and how rider behaviour influences the likelihood of accident involvement. The influence of age, sex and experience on attitudes and behaviours, and as direct or indirect influences on accidents were also investigated. Accident risk was also directly influenced by the number of miles ridden in the past 12-months. The report makes a number of recommendations for improving the safety of motorcycle riders

Accessible user interface support for multi-device ubiquitous applications: architectural modifiability considerations

The market for personal computing devices is rapidly expanding from PC, to mobile, home entertainment systems, and even the automotive industry. When developing software targeting such ubiquitous devices, the balance between development costs and market coverage has turned out to be a challenging issue. With the rise of Web technology and the Internet of things, ubiquitous applications have become a reality. Nonetheless, the diversity of presentation and interaction modalities still drastically limit the number of targetable devices and the accessibility toward end users. This paper presents webinos, a multi-device application middleware platform founded on the Future Internet infrastructure. Hereto, the platform's architectural modifiability considerations are described and evaluated as a generic enabler for supporting applications, which are executed in ubiquitous computing environments

An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Budgeted Reinforcement Learning in Continuous State Space

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an - adjustable - threshold. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving.Comment: N. Carrara and E. Leurent have equally contribute

A real-time recession indicator for the Euro area

In this paper, we propose a new coincident monthly indicator to detect in real-time the start and the end of an economic recession phase for the Euro area. In this respect, we use the methodology proposed in Anas and Ferrara (2002, 2004) as regards the recession indicator for the US, based on Markov-Switching processes popularized in economics by Hamilton (1989). By using a set of four monthly time series, we show that this start-end recession indicator (SERI) is able to reproduce all the recession phases experienced by the Euro area since 1970. Concerning the last low phase of the growth cycle in the Euro area, started in 2001, empirical results show that the Euro area experienced a « quasi-recession » phase, located between the end of the 2001 year and the beginning of 2002, without a global recession. This is due to a lack of diffusion of this phenomena among the main Eurozone countries, though it was synchronized.Recession; real-time; probabilistic indicator; Euro area