# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 2 Literature Review
### Factors Influencing Bike Sharing Utilization  
According to [PERSON] et al. [37], bike sharing usage patterns are mainly impacted by two types of contextual factors. Those include common contextual factors that occur frequently and affect the whole system (e.g., weather and time-related variables). In addition, there are opportunistic contextual factors which happen irregularly and affect only a subset of the system (e.g., social and traffic events). While some studies investigate the effects of opportunistic contextual factors (e.g., calendar events [38] or transit disruption [39]), common contextual factors are more frequently the subject of the recent literature. To identify temporal usage patterns, [PERSON] et al. [40] analyzed trip data from five German bike sharing schemes. Their findings show that during weekdays bike sharing demand has a slight morning peak and a larger peak in the afternoon. Similar results were obtained by [PERSON] and [PERSON] [41] during their analysis of a bike sharing system in Montreal, Canada. On a larger scale, [PERSON] et al. [42] analyzed 38 global bike sharing systems and classified them based on temporal characteristics and associated user types. A large proportion of the considered systems also showed two weekday peaks (morning and afternoon) and a broad afternoon peak at weekends, with predicted user types being commuters and weekend leisure users. However, [PERSON] et al. also identified further systems that, for example, are primarily used on weekends (leisure users) or have more than two commuter peaks per day (commuters with some utility users) [42]. The temporal characteristics of an even larger number of 322 global bike sharing schemes can also be obtained from [PERSON] et al. [34]. [PERSON] and [PERSON] [43] analyzed the effect of different weather variables on bike sharing trips in Washington, DC, USA. Consistent with other studies (e.g., Refs. [38; 44; 45; 46]), their results found that adverse weather like cold temperatures, rain, and increased wind speeds decreases the number of bike sharing trips. In addition, [PERSON] et al. [47] claimed that not only the number of trips but also the travel time increase during good weather conditions. In contrast, several studies suggest that temperature has a negative impact on bike sharing demand when it exceeds a certain level [8; 10; 48]. While looking at temporal and weather effects, [PERSON] et al. [44] additionally considered natural and built environments influencing bike sharing trips in New York City, USA. However, their findings show that weather impacts bike trips more than topography, infrastructure, or land use mix. Furthermore, in correlation with previously presented effects of weather variables, several studies could show that the yearly trend in bike sharing usage can be classified into three periods. This includes an off season with low demand during winter months, a main season with high demand during summer months, and a transition phase with growing or falling demand in spring and fall months, respectively [28; 40]. This indicates that the long-term influence of weather variables on bike sharing usage behavior can be also expressed through temporal patterns (in terms of yearly profiles or seasons). In addition to weather conditions and time-related variables, there are many studies analyzing several other factors affecting bike sharing usage on a higher spatial granularity, such as sociodemographic and built environment characteristics (e.g., Refs. [45; 49; 50; 51; 52; 53]). However, those factors predominantly impact usage behavior on a station level rather than on a system level. In addition, analyzing them requires specific data which are mostly valid for the considered study area only and thus limit the broad and easy transferability of our approach. Hence, related publications are beyond the scope of this work and will not be discussed any further. For additional research in this regard, we recommend the work of [PERSON] and [PERSON] [54], [PERSON] et al. [55], and [PERSON] et al.