Ariane Kehlbacher

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.

Demand studies often use observable characteristics to proxy preference heterogeneity. It is likely, however, that some households with the same observable characteristics have quite different preferences. An alternative approach is to use a Gaussian mixture of Almost Ideal Demand Systems to capture the heterogeneity. We show how to estimate this with censored purchase data for 5 food categories using Bayesian inference. Using model outputs we infer four different preference classes; how distinct these classes are from one another and which food categories are driving the segmentation process.

The majority of the UK population is either overweight or obese. Health economists, nutritionists and doctors are calling for the UK to follow the example of other European countries and introduce a tax on soft drinks as a result of the perception that high intakes contribute to diet‐related disease. We use a demand model estimated with household‐level data on beverage purchases in the UK to investigate the effects of a tax on soft drink consumption. The model is a Quadratic Almost Ideal Demand System, and censoring is handled by applying a double hurdle. Separate models are estimated for low, moderate and high consumers to allow for a differential impact on consumption between these groups. Applying different hypothetical tax rates, we conclude that understanding the nature of substitute/complement relationships is crucial in designing an effective policy as these relationships differ between consumers depending on their consumption level. The overall impact of a soft drink tax on calorie consumption is likely to be small. Copyright © 2014 John Wiley & Sons, Ltd.