# Mode Choice Effects on Bike Sharing Systems
## 2 Literature Review
A study from Switzerland [18,19] compared several mode-specific effects on choices between PMT, PT, cycling, and walking under the inclusion of individuals' and area-specific characteristics, whereby for walking and cycling only the effect of travel time was considered as a mode-specific attribute; they found that car availability, younger age, low fuel, and parking costs, and low parking search times increase the utility of PMT, while low access and egress times, low ticket costs, and a low utilized capacity increase the utility of PT. An investigation on the Dutch mobility panel with almost 2000 participants focused on characteristics that affect choices between PMT, PT, cycling, and walking [20]. Among others, these characteristics include individual and household characteristics (such as socio-demographics and mobility ownership), weather, trip characteristics (such as distance and travel time), effects from the built environment, and work characteristics. Results indicate that, among other things, higher education, transit subscription, cycling to high school, weekdays, and certain trip purposes increase the utility of cycling, while owning a company car and travelling in larger groups decrease its utility. A recent study compared shared modes in Zurich, namely station-based BSS, e-bike sharing systems (e-BSS), and e-scooters [7]; they found that station density and morning hours increased the utility of BSS, while variation in topographical altitude and night hours decreased its utility. [PERSON] et al., (2021) undertook an extensive survey with more than 5000 participants from the Greater Jakarta area in which they also incorporated less-established modes such as urban air mobility, including currently developed electric-based aircraft or autonomous vertical take-off and landing vehicles, on-demand transport, and bus rapid transit [21]; they found that motorcycles have the highest baseline utility, while bikes have the second-lowest. In addition, numerous studies have focused on the route choices of cyclists. Although this paper is primarily focused on mode choices, it can be expected that route choice effects in general also affect mode choices. A recent study with 662 participants from Greece and Germany investigated the effects of cycling infrastructure, speed limit, surface, on-street parking, trees, and travel time on route choices [22]; they found that in both countries protected bike lanes are preferred over other forms of infrastructure. Furthermore, asphalt is preferred over cobblestone, while the utility of a speed limit depends on the country. Other attributes that were incorporated in older studies are the number of car lanes [23], stop signs and crowding of cyclists [24], number and type of crossings [25], the width of bike lanes and traffic volumes [26], as well as sharing space with pedestrians and the availability of secure parking and showers at the destination [27]. With reference to the presented studies, it can be assumed that relevant attributes for cycling with private bikes are relevant for BSS, too. Furthermore, additional attributes, such as renting costs, access and egress times, to renting stations are relevant in the case of BSS. Evaluating which effects are of relevance for BSS and quantifying their influence is the aim of our survey study. The employed survey design and choice experiment are introduced in the following section. The study aims to provide an overview of the most relevant effects to allow the implementation of BSS in multi-modal transport demand models, and, with that, to provide a basis for forecasts on urban transport, which consider BSS as an alternative transport mode.