# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 4 Demand Scenario Generation
### Temporal Disaggregation
#### 4.4.1 Derivation of Temporal Patterns
Also, there is a high utilization at night compared to weekdays. With regard to the observations of [PERSON] et al. [42] and [PERSON] et al. [34], this indicates that the Hamburg bike sharing system user structure primarily comprises commuter and weekend leisure users. To capture the day-to-day variability in hourly bookings within our model, we included the hour of the day and the day of the week as categorical input variables. In addition, as we could observe that the influence of daytime is different for specific days of the week, we included an interaction term between time and day of the week. In order to access annual patterns, we furthermore analyzed the year-to-year variability in daily bookings (Figure 7). Next to a slight upward trend due to the general growth of Hamburg's bikes sharing system [76], the figure indicates that there is a strong annual seasonal pattern (represented by the blue line). Coherent with the existing literature, this pattern consists of three periods with fewer bookings during winter, a high number of bookings during summer, and a transition phase during spring and autumn with growing and declining booking numbers, respectively. As the pattern is repeated each year, we assume that those three periods reflect the general pattern of annual bike sharing usage. However, neither the associated seasons nor the respective months provide a sufficient temporal granularity to also represent any short-term variations within the annual course. Hence, we included week numbers as a categorical variable in our model since this interval is short enough to represent fluctuations but long enough to keep the number of input variables used for the regression model within reasonable limits. In summary, the following explanatory variables were included in the model:  
* Week number (reference variable: calendar week 1);
* Weekday (reference variable: Monday);
* Time of day (reference variable: hour 0);  
Figure 7: Year-to-year variability in daily bookings from conventional BSS in Hamburg, Germany (black: observed bookings; green: smoothed conditional means). * Interaction term between weekday and time of day. In contrast to the uniform pattern of annual seasonality, Figure 7 also shows that the frequency of the daily seasonality is rather irregular for the different years. In order to reveal the key temporal patterns, we consolidated the three-year booking data into a dataset consisting of hourly booking figures for only one year. We performed this by averaging the hourly bookings of each corresponding hour over the three years. This dataset is called the representative bike sharing dataset as follows. The results of our negative binomial model applied to the representative bike sharing dataset are presented in Table 1 (Appendix A.1). As this is common in the recent literature regarding the influence of temporal factors on bike sharing rentals (e.g., Refs. [11; 19; 25; 41; 43; 51; 53]), we used [PERSON]'s Pseudo-R\({}^{2}\)[77] to measure goodness of fit. Table 1 shows that our model has a very high fit, as the Pseudo-R\({}^{2}\) value is 0.94443. This indicates that nearly 95% of the variation in hourly bike sharing rentals can be explained using the variables used within the model. As expected, the coefficients for week-related dummy variables show that the number of trips is increased for mid-year weeks compared to weeks at the beginning or end of the year. Furthermore, since we included the interaction between weekdays and the time of the day, the results demonstrate that the regression model enables the estimation of weekday and weekend peaks. Another finding is that the influence on hourly rentals relative to the reference category is mostly not significant for the interaction between weekdays and nighttime hours. We explain this by the fact that there is hardly any difference between the nightly booking numbers on weekdays.