Response to automatic speed control in urban areas: A simulator study.

Speed affects both the likelihood and severity of an accident. Attempts to reduce speed have centred around road design and traffic calming, enforcement and feedback techniques and public awareness campaigns. However, although these techniques have met with some success, they can be both costly and context specific. No single measure has proved to be a generic countermeasure effective in reducing speed, leading to the suggestion that speed needs to be controlled at the source, i.e. within the vehicle. An experiment carried out on the University of Leeds Advanced Driving Simulator evaluated the effects of speed limiters on driver behavionr. Safety was measured using following behaviour, gap acceptance and traffic violations, whilst subjective mental workload was recorded using the NASA RTLX. It was found that although safety benefits were observed in terms of lower speeds, longer headways and fewer traffic light violations, drivers compensated for loss of time by exhibiting riskier gap acceptance behaviour and delayed braking behaviour. When speed limited, drivers' self-reports indicated that their driving performance improved and less physical effort was required, but that they also experienced increases in feelings of frustration and time pressure. It is discussed that there is a need for a total integrated assessment of the long term effects of speed limiters on safety, costs, energy, pollution, noise, in addition to investigation of issues of acceptability by users and car manufacturers

Adaptive estimation of linear functionals in the convolution model and applications

We consider the model $Z_i=X_i+\varepsilon_i$, for i.i.d. $X_i$'s and $\varepsilon_i$'s and independent sequences $(X_i)_{i\in{\mathbb{N}}}$ and $(\varepsilon_i)_{i\in{\mathbb{N}}}$. The density $f_{\varepsilon}$ of $\varepsilon_1$ is assumed to be known, whereas the one of $X_1$, denoted by $g$, is unknown. Our aim is to estimate linear functionals of $g$, for a known function $\psi$. We propose a general estimator of and study the rate of convergence of its quadratic risk as a function of the smoothness of $g$, $f_{\varepsilon}$ and $\psi$. Different contexts with dependent data, such as stochastic volatility and AutoRegressive Conditionally Heteroskedastic models, are also considered. An estimator which is adaptive to the smoothness of unknown $g$ is then proposed, following a method studied by Laurent et al. (Preprint (2006)) in the Gaussian white noise model. We give upper bounds and asymptotic lower bounds of the quadratic risk of this estimator. The results are applied to adaptive pointwise deconvolution, in which context losses in the adaptive rates are shown to be optimal in the minimax sense. They are also applied in the context of the stochastic volatility model.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ146 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Density deconvolution from repeated measurements without symmetry assumption on the errors

We consider deconvolution from repeated observations with unknown error distribution. So far, this model has mostly been studied under the additional assumption that the errors are symmetric. We construct an estimator for the non-symmetric error case and study its theoretical properties and practical performance. It is interesting to note that we can improve substantially upon the rates of convergence which have so far been presented in the literature and, at the same time, dispose of most of the extremely restrictive assumptions which have been imposed so far

Grothendieck ring of semialgebraic formulas and motivic real Milnor fibres

We define a Grothendieck ring for basic real semialgebraic formulas, that is for systems of real algebraic equations and inequalities. In this ring the class of a formula takes into consideration the algebraic nature of the set of points satisfying this formula and contains as a ring the usual Grothendieck ring of real algebraic formulas. We give a realization of our ring that allows to express a class as a Z[1/2]- linear combination of classes of real algebraic formulas, so this realization gives rise to a notion of virtual Poincar\'e polynomial for basic semialgebraic formulas. We then define zeta functions with coefficients in our ring, built on semialgebraic formulas in arc spaces. We show that they are rational and relate them to the topology of real Milnor fibres.Comment: 30 pages, 1 figur

Cumulative distribution function estimation under interval censoring case 1

We consider projection methods for the estimation of the cumulative distribution function under interval censoring, case 1. Such censored data also known as current status data, arise when the only information available on the variable of interest is whether it is greater or less than an observed random time. Two types of adaptive estimators are investigated. The first one is a two-step estimator built as a quotient estimator. The second estimator results from a mean square regression contrast. Both estimators are proved to achieve automatically the standard optimal rate associated with the unknown regularity of the function, but with some restriction for the quotient estimator. Simulation experiments are presented to illustrate and compare the methods.Comment: Published in at http://dx.doi.org/10.1214/08-EJS209 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org