The movement and transport of people and goods is spatial by its very nature. Thus, geospatial fundamentals of transport systems need to be adequately considered in transport models. Until recently, this was not always the case. Instead, transport research and geography evolved widely independently in domain silos. However, driven by recent conceptual, methodological and technical developments, the need for an integrated approach is obvious. This paper attempts to outline the potential of Geographical Information Systems (GIS) for transport modeling. We identify three fields of transport modeling where the spatial perspective can significantly contribute to a more efficient modeling process and more reliable model results, namely, geospatial data, disaggregated transport models and the role of geo-visualization. For these three fields, available findings from various domains are compiled, before open aspects are formulated as research directions, with exemplary research questions. The overall aim of this paper is to strengthen the spatial perspective in transport modeling and to call for a further integration of GIS in the domain of transport modeling.

The recent attention given to automated vehicles suggests that self-driving vehicles or driverless cars are about to become reality in the near, if not very near, future. While technologically this impression may well be justified, the emerging discussion discovers other related aspects that needs to be addressed alongside the technological challenges. Examples are legal and ethical aspects (Gasser\ud et al. 2012; Beiker et al. 2012), user acceptance (Fraedrich, Lenz 2014) or the implications on the transport system that might result from the largescale uptake of self-driving vehicles (Zachariah et al. 2013; Spieser et al. 2014; Fagnant, Kockelman 2014). Somewhat less interest has been attached to the possible implications of this new ‘transport tool’ for people’s mobility decisions.\ud In this article, we focus on the possible implications\ud of automated vehicles for the mobility choices of people, in terms of both their everyday mobility, specifically the choice of transport mode, as well as their longer term residential location decisions. The question is: which influence can we expect from automated vehicles on the\ud choices we make for everyday mobility and also longer term residential location? This interest arises from the specific properties of automated vehicles.\ud In particular, the potential benefit of spending time in the vehicle with activities other than driving may lead to significant changes in how we make use of time while traveling and its resultant valuation (Continental 2013; Solokow 2013; Silberg et al. 2013; Silberg et al. 2012; Munsch 2014; Cyganski et al. 2015). As a result, people might reconsider their choice of transport mode, number\ud of trips and distance of travel. What is more, changes in the value of travel time may influence the choice of residential location. The idea, for example, that time in a vehicle does not have to be spent on driving-related tasks, but instead permits other activities, may provoke a complete reappraisal of the time factor (Silberg et al. 2012). This could then lead to users considering different\ud (further away) destinations for their daily journeys, or even changing their choice of home location, as the long commutes would then be seen more favorably. If we take these connected factors to their logical conclusion, in the end it would bepossible to dissolve the time factor as a limiting variable of urban planning. However, such changes\ud can be expected not only as a result of individual choices but also the type of automated mobility concepts. The use of a vehicle on demand as an alternative to privately-owned cars might lead to an increase in multimodal behavior, or contribute to an increase in carpooling. The use of a fully automated private car might instead increase car use and mileage covered given that time spent in the\ud vehicle is often viewed positively. As these examples show, there is a great need to specify and clarify what kind of automated vehicle or rather automation scenario is under consideration. \ud To acknowledge these considerations, the article starts with an outline of current scenarios for the deployment of automated vehicles in urban areas (section 2). It moves on to discuss the possible implications of these scenarios on mode choice (section 3), followed by considerations of the effects on longer term residential location choices (section\ud 4). The methodological basis of the discussion is a stated preference experiment and a systematic review of existing scenarios (for more information see Cyganski 2015 and Heinrichs 2015). It draws on the work by the authors in the recent Villa Ladenburg project of the Daimler and Benz Foundation, in which an interdisciplinary team explored a\ud wide range of challenges and frame conditions associated\ud with automated driving (see Maurer et al. 2015). The article concludes with some observations concerning the findings including the levels of uncertainty

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

In 2013 Willumsen, one of the most renowned researchers in transport modeling, stated, regarding automated vehicles: “We can no longer ignore them, if [the] planning horizon is 10+ years”.

Luis G. Willumsen, einer der renommiertesten Wissenschaftler für die Verkehrsmodellierung, stellte 2013 auf einer Fachkonferenz fest: „We can no longer ignore them [autonome Fahrzeuge], if planning horizon is 10+ years“ [37]. Doch Arbeiten, die versuchen, die Auswirkungen der autonomen Fahrzeuge auf die Alltagsmobilität der potenziellen Nutzer und konkret deren Verkehrsmittelwahl zu antizipieren, finden sich erst selten [11], [19], [37]. Allerdings erlaubt ein Blick auf die individuellen Treiber unseres täglichen Mobilitätsverhaltens bereits jetzt vorsichtige Aussagen zu etwaigen Verhaltensänderungen durch die Einführung autonomer Fahrzeuge. Analogieschlüsse zur Nutzung bekannter Verkehrsmittel und ihre Übertragung in Verkehrsnachfragemodelle lassen erste quantitative Aussagen über Auswirkungen auf die Gesamtverkehrsnachfrage zu. Die Nachfragemodellierung ermöglicht es hierbei, zwischen unterschiedlichen räumlichen Kontexten und Nutzergruppen zu unterscheiden und verschiedene Szenarien zum Einsatz solcher Systeme zu evaluieren.