Providing a sufficiently appropriate route environment is crucial to ensuring fair and safe biking, thus encouraging cycling as a sustainable mode of transport. At the same time, better understanding of cyclists’ preferences regarding the features of their routes and their infrastructure requirements is fundamental to evaluating improvement of the current infrastructure or the development of new infrastructure. The present study has two objectives. The first is to investigate cyclists’ route preferences by means of a choice experiment based on a stated preference survey. Subsequently, the second objective is to compare cyclist preferences in two countries with different cycling characteristics (both in infrastructure as well as cyclists’ behavior). For this purpose, a graphical online stated preferences survey was conducted in Greece and Germany. Within the framework of statistical analyses, multinomial mixed logit discrete choice models were developed that allow us to quantify the trade-offs of interest, while distinguishing between the preferences of different user groups. In addition, user requirements in Greece, as a country with a low cycling share and very little dedicated bike infrastructure, were compared to the requirements in Germany, where cycling is popular and the infrastructure is well developed. The results over the whole sample indicate that subgroups value infrastructure differently according to their specific needs. When looking at country specifics, users from Greece are significantly more willing to accept longer travel times in return for higher-quality facilities. The utility of low speed limits in mixed traffic is also different. In Germany, low speed limits offset the disturbance caused by motorized traffic, but in Greece they do not. Consequently, the results help to asses which types of infrastructure are most sustainable from a user perspective and help to set priorities when the aim is to adapt the road infrastructure efficiently in a stable strategy.

The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable interaction between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking facilities along main streets, street connectivity, the prevalence of neighborhood streets, green pathways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated workflow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.

Electric vehicles require sufficient public charging infrastructure. This in turn necessitates detailed information on charging demand. In this paper we present a four-step approach to estimating public charging demand of electric vehicles. Previous methods are limited in their ability to provide differentiated results and adapt to future developments. Therefore, we account for user groups (private, carsharing, commercial), technical developments (vehicles, infrastructure), infrastructure availability, and carsharing development (operational area, business models, autonomous vehicles). Our approach also considers the interactions between these factors and allows for scenario analysis yielding the quantity and spatial distribution of public charging demand. We demonstrate our approach for Berlin, Germany. We find that the majority of public charging demand results from carsharing. This demand is concentrated in the city center, even when carsharing is available citywide. Public charging demand for commercial users is relatively low and located outside the city center. For private users, public charging demand shifts to the city center with an increasing market penetration of electric vehicles and technological advancements (increased range, charging speed). Public demand from private users increases dramatically when private infrastructure is absent. Finally, public charging demand shifts to the city center when private users do not have private infrastructure.

Introduction: Many municipalities aim to support the uptake of cycling as an environmentally friendly and healthy mode of transport. It is therefore crucial to meet the demand of cyclists when adapting road infrastructure. Previous studies researching cyclists’ route choice behavior deliver valuable insights but are constrained by laboratory conditions, limitations in the number of observations, or the observation period or relay on specific use cases. Methods: The present study analyzes a dataset of over 450,000 observations of cyclists’ routing settings for the navigation of individual trips in Berlin, Germany. It therefore analyzes query data recorded in the bike-routing engine BBBike and clusters the many different user settings with regard to preferred route characteristics. Results and Conclusion: Results condense the large number of routing settings into characteristic preference clusters. Compared with earlier findings, the big data approach highlights the significance of short routes, side streets and the importance of high-quality surfaces for routing choices, while cycling on dedicated facilities seems a little less important. Consequentially, providing separated cycle facilities along main roads – often the main focal point of cycle plans – should be put into the context of an integrated strategy which fulfills distinct preferences to achieve greater success. It is therefore particularly important to provide a cycle network in calm residential streets as well as catering for short, direct cycle routes.

Electric vehicles offer a means to reduce greenhouse gas emissions in passenger transport. The availability of reliable charging infrastructure is crucial for the successful uptake of electric vehicles in dense urban areas. In a pilot project in the city of Hamburg, Germany, public charging infrastructure was equipped with a reservation option providing exclusive access for local residents and businesses. The present paper combines quantitative and qualitative methods to investigate the effects of the newly introduced neighborhood charging concept. We use a methodology combining a quantitative questionnaire survey and qualitative focus group discussions as well as analyses of charging infrastructure utilization data. Results show that inner-city charging and parking options are of key importance for (potential) users of electric vehicles. Hence, the neighborhood concept is rated very positively. Providing guaranteed charging and parking facilities is therefore likely to increase the stock of EVs. On the other hand, this could to a large extent lead to additional cars with consequential disadvantages. The study shows that openly accessible infrastructure is presently utilized much more intensely than the exclusive option. Consequentially, the concept evaluated should be part of an integrated approach managing parking and supporting efficient concepts like car sharing.