# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 5 Modeling Results  
Since the regression coefficients are linked with the model intercept, they could not be directly applied to the OSABS base demand. Therefore, we needed to develop a methodology for effectively transferring temporal patterns to the new system based on the following assumptions:  
The k8-regression model's intercept is 36.07, and the average value of observed hourly bookings within the data is 288.14. From this, we can derive an intercept-mean ratio of 0.125 for the model. Moreover, given that the OSABS base demand for a statistical day, i.e., the average value for daily bookings, is 8322 trips for the high scenario and 5435 for the low scenario, we can calculate average values for hourly OSABS bookings of 346.75 and 226.46 for the high and low scenarios, respectively. Hence, by implementing the model's intercept-mean ratio in OSABS, we derive intercept values of 43.34 and 28.31 for the high and low scenarios, respectively.  
Based on these assumptions, we calculate the hourly OSABS traffic flow in Magdeburg for each of the eight representative weeks. An extract of the results is pictured in Figures 10 and 11, which show predicted weekly OD-traffic flows for the inner-city area of Magdeburg as well as average hourly bookings for Monday to Thursday, Friday, and Saturday to Sunday.  
As expected, the figures indicate increased OSABS utilization in weeks C6 and C8, as they represent calendar weeks with higher average temperatures compared to the calendar weeks represented by weeks C2 and C4, hence replicating annual BSS usage patterns. Moreover, Figure 10 indicates that our approach enables the prediction of OSABS traffic flows with high spatial granularity. Further, Figure 11 shows that the approach enables accurate reproducibility of previously determined day-to-day variability in hourly bookings.  
A crucial aspect of our study was validating the model. We compared our model's predictions for OSABS in Magdeburg with characteristic data from existing bike sharing systems determined by [PERSON] et al. [34] (Table 4). This comparison served as an indirect validation method, providing a benchmark for assessing our model's viability in real-world scenarios. As Table 4 demonstrates, our model predicts higher OSABS utilization in Magdeburg compared to conventional BSS in similar-sized cities.  
Figure 9: Comparison of observed and predicted values for weekly bookings.  
However, the slightly higher predicted OSABS figures are justifiable given its distinct advantages over conventional BSS. Firstly, OSABS extends the reach of bike sharing services to less dense urban areas. While traditional BSS primarily operates in densely populated, central city locations, OSABS allows for effective operation throughout the entire city, including peripheral areas, significantly expanding the potential user base and thus contributing to the higher predicted usage figures. Secondly, OSABS's mobility-as-a-service feature, which enables users to summon vehicles directly to their location, greatly enhances user convenience and thus system utilization. Further, the ability to transport goods adds a valuable dimension to the service, attracting a wider range of users, including those who need to carry items that would be difficult with a standard bike. This feature is likely to appeal to a diverse demographic, further boosting the system's utilization.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**BSS** & **Population** & **Area (km2)** & **Weekday Journeys** \\ \hline Very large, high-use BSS & 4,299,517 & 441 & 68,660 \\ Large BSS in major cities & 1,786,311 & 168 & 24,903 \\ OSABS in Magdeburg & 239,364 & 201 & 8322 (high scenario), \\ Small to medium efficient BSS & 350,354 & 36 & 4448 \\ Medium BSS with extensive & 448,719 & 74 & 2708 \\ cycling infrastructure & 111,302 & 17 & 359 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic BSS data (based on [34]).  
Figure 10: Predicted weekly OSABS OD-traffic flows for Magdeburg inner-city area.