Automated Vehicles – Game Changer For Urban Mode Choice?

The progressive automation in traffic up to the introduction of automated driving vehicles and associated new mobility services is capable of disrupting existing transport systems and is expected to have a significant impact on travel behavior, mode choice, car ownership and in the long run on residential location choice (e.g. Chapin et al., 2016; Milakis et al., 2017). Mobility needs, the resulting traffic as well as the physical shape of urban space are in close correlation (Cervero & Kockelmann, 1997). The city structure with its built environment and the location of residential and activity locations forms the basis for mobility decisions of households and companies and has a significant influence on the design of traffic (Heinrichs, 2015) as a compact urban structure with a high density and mix of uses promotes short distances, the use of active transport modes like walking or cycling, and provides a Basis for public transport through concentrated traffic demand. In return, the availability and use of transport means can influence the urban structure (e.g. Apel, 2003; Steierwald, 2005). Therefore it is expected, that the availability of automated vehicles will have an impact on traffic demand patterns and the built environment with the greatest impact expected on the highest level of automation, where the system can autonomously manage all aspects of driving the vehicle (Kornhauser, 2014). However, both the expected usage as well as the spatial impact are yet highly uncertain (Heinrichs & Cyganski, 2015) and will depend heavily on a variety of factors. Besides the level of automation and the share of such vehicles in the total stock, the regional context, the perception of these vehicles and the newly offered transport options, changes in travel time and their perception as well as user preferences in general are sure to play a crucial role when it comes to analyzing prospect changes (Fraedrich & Lenz, 2016). Recent studies on mode choices in the present of autonomous driving considering geographical characteristics suggest differences in the perception of the autonomous vehicle concepts depending on geographical context (Fraedrich et al., 2016).