Microscopic travel demand models take the characteristics of every individual person of the modeled population into account for computing the travel demand for the modeled region. The real world mobility of individuals strongly depends on the specific available car, if any. However, mode choice models usually take a standard average car as reference. This paper shows an integrated approach to model the travel demand with respect to car specific attributes. The proposed work uses a synthetic population for the German capital of Berlin and simulates the travel demand for different examples that replicate car specific changes in fuel price, fleet distribution and entrance restriction. Some of these car-specific measures influence the travel behavior on a level that cannot be modeled when using an average car at all. Furthermore, the results show significant changes in usage of specific car segments, which would be difficult to model using an averaged car

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.