Suchergebnisse

The goal of this paper is the estimation of the effect of accidents on traffic congestion and vice versa. In order to do this, I use ?big data? of highway traffic and accidents in England for the period 2007-2013. The data exhibit some remarkably stable cyclical pattern of highway traffic which is used as a research setting that enables the identification of the causal effect of accidents on traffic congestion and vice versa. The estimation draws on panel data methods that have previously been used to analyse the bahaviour of electricity day-ahead mar- ket prices. A positive relationship between traffic congestion and road accidents would yield multiplicative benefits for policies that aim at reducing either of them. In addition, we will explore potential heterogeneous effects around different metropolitan areas and regions. This is one of the few studies that makes use of the increasing volume of big datasets, which are publicly available from governments and local authorities.

Commercial vehicle accidents impose very significant costs on industry and society but for a variety of reasons the full costs are often poorly understood. Advocates that vehicle operators should undertake a full and systematic analysis of accident levels, causes and costs. Introduces the CCSM model of vehicle accident reduction. By undertaking analysis based on this approach, most vehicle operators should be able to identify measures to reduce accidents substantially. Typical measures include the implementation of vehicle accident monitoring systems, driver age and experience policies, reviews of vehicle speed policy, driver training schemes, more systematic driver recruitment and better vehicle specification.

Fragen zu eigenen Unfällen und zu unfallträchtigen Situationen sowie
abgeschlossenen Versicherungen.

Themen: Eigener Unfall im Haushalt; Art des Unfalls; vermutete
Verteilung der Unfallquoten auf Haushalt, Arbeitsstätte und Verkehr;
perzipierte unfallträchtige Situationen im eigenen Tagesablauf; eigene
Stressbelastung; Charakterisierung der ´meisten Menschen´ (semantisches
Differential); Kenntnis von Aktionen gegen Unfallgefahren; Abgrenzung
von Konsum-, Kredit- und Berufsgenossenschaften; Kenntnis der Aufgabe
von Berufsgenossenschaften; abgeschlossene Versicherungen;
Mitgliedschaft in einer Berufsgenossenschaft; Entfernung zur
Arbeitsstelle; Verkehrsmittelbenutzung.

Demographie: Alter (klassiert); Geschlecht; Familienstand;
Schulbildung; Berufsausbildung; Beruf; berufliche Position;
Berufstätigkeit; Haushaltseinkommen; Haushaltsgröße; Besitz langlebiger
Wirtschaftsgüter.

Interviewerrating: Schichtzugehörigkeit des Befragten; Ortsgröße.