Explaining car drivers' intention to prevent road-traffic noise: An application of the Norm Activation Model

Strengthening car drivers’ intention to prevent road-traffic noise is a first step toward noise abatement through voluntary change of behavior. We analyzed predictors of this intention based on the norm activation model (i.e., personal norm, problem awareness, awareness of consequences, social norm, and value orientations). Moreover, we studied the effects of noise exposure, noise sensitivity, and noise annoyance on problem awareness. Data came from 1,002 car drivers who participated in a two-wave longitudinal survey over 4 months. Personal norm had a large prospective effect on intention, even when the previous level of intention was controlled for, and mediated the effect of all other variables on intention. Almost 60% of variance in personal norm was explained by problem awareness, social norm, and biospheric value orientation. The effects of noise sensitivity and noise exposure on problem awareness were small and mediated by noise annoyance. We propose four communication strategies for strengthening the intention to prevent road-traffic noise in car drivers.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

Estimating the uptake of traffic-derived NO2 from 15N abundance in Norway spruce needles

The 15N ratio of nitrogen oxides (NOx) emitted from vehicles, measured in the air adjacent to a highway in the Swiss Middle Land, was very high [δ15N(NO2) = +5.7‰]. This high 15N abundance was used to estimate long-term NO2 dry deposition into a forest ecosystem by measuring δ15N in the needles and the soil of potted and autochthonous spruce trees [Picea abies (L.) Karst] exposed to NO2 in a transect orthogonal to the highway. δ15N in the current-year needles of potted trees was 2.0‰ higher than that of the control after 4 months of exposure close to the highway, suggesting a 25% contribution to the N-nutrition of these needles. Needle fall into the pots was prevented by grids placed above the soil, while the continuous decomposition of needle litter below the autochthonous trees over previous years has increased δ15N values in the soil, resulting in parallel gradients of δ15N in soil and needles with distance from the highway. Estimates of NO2 uptake into needles obtained from the δ15N data were significantly correlated with the inputs calculated with a shoot gas exchange model based on a parameterisation widely used in deposition modelling. Therefore, we provide an indication of estimated N inputs to forest ecosystems via dry deposition of NO2 at the receptor level under field conditions.