Car use in modern cities with a well-developed public transit is more sophisticated to explain only through hard factors such as sociodemographic characteristics. In cities, it is especially important to consider motives for car use. Therefore, we examined two modern cities with a high modal share of non-motorized modes and public transit to answer the question: How do the affective and instrumental motives influence car use in such cities? The used data set was collected in Berlin and San Francisco. To investigate the role of motives, we applied an ordered hybrid choice model (OHCM) with a probit kernel. Based on the OHCM we explained more than 14% of the overall heterogeneity and gave further insights to the decision-making process. The affective motive had a strong influence on car use frequency, whereby the instrumental aspects did not matter. Furthermore, an effect resulting from age could not be determined for the affective motives in these cities. Results suggest people are more likely to use cars for affective motives despite the city’s adversities. For these people it is difficult to achieve a shift to alternative means of transport. The only way to intervene here is through regulatory intervention.

Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets.

To counteract climate change, electric vehicles are replacing vehicles with internal combustion engine on the automotive market. Therefore, electric vehicles must be accepted and used like conventional vehicles. This study aims to investigate to which extent electric vehicles are already being used like conventional vehicles. To do this, we present a supervised method where we combine usage data from conventional vehicles (from car use model based on survey data) and electric vehicles (from sensor data) in Germany and California. Based on conventional vehicles, eight car usage profiles were defined by hierarchical clustering in a previous study. Using a softmax regression, we estimate for each electric vehicle a probability of assignment for every car usage profile. Comparison of conventional and electric vehicles with a high probability reveals that electric vehicles are used similar for long-distance travel (>100 km) and different for short-distance travel (<10 km) to conventional vehicles. This implies that electric vehicles are indeed used for long-distance travel but are still not entirely used for everyday mobility. This could be because electric vehicles are not yet suitable for all trip purposes (e.g., transport of larger items).

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