# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## Appendix A
### Appendix a.2  
The subsequent Table 12 contains the estimated parameter values for the regression model described in Section 4.4.2. Instead of using individual week numbers, we assigned a cluster number \(i=1\ldots 8\) to each calendar week. We then utilized these cluster numbers as categorical input variables for our regression model. In summary, the following explanatory variables were included in the model:  
* Number of cluster (cluster1...cluster8);
* Weekday (Monday...Sunday);
* Time of day (hour00...hour23);
* Interaction term between weekday and time of day (Monday \(\times\) hour00...Sunday \(\times\) hour23). \begin{table}
\begin{tabular}{l l l l l} \hline
**Parameter** & **exp(Estimate)** & **2.5\%** & **97.5\%** & **z-Value** & _p-Value_ \\ \hline Intercept & 36.06807 & 33.66655 & 38.64089 & 101.98774 & 0 \\ cluster2 & 1.29344 & 1.26802 & 1.31937 & 25.407 & 0 \\ cluster3 & 1.56703 & 1.53731 & 1.59731 & 45.9887 & 0 \\ cluster4 & 1.94824 & 1.90712 & 1.99025 & 61.27461 & 0 \\ cluster5 & 2.2289 & 2.18418 & 2.27454 & 77.5041 & 0 \\ cluster6 & 2.49625 & 2.44392 & 2.54969 & 84.636 & 0 \\ cluster7 & 2.71512 & 2.66547 & 2.76569 & 106.08542 & 0 \\ cluster8 & 2.97104 & 2.91181 & 3.03147 & 105.98863 & 0 \\ Tuesday & 1.21146 & 1.10226 & 1.33147 & 3.98016 & 0.00007 \\ Wednesday & 1.47616 & 1.344476 & 1.62041 & 8.18713 & 0 \\ Thursday & 1.79838 & 1.63946 & 1.9727 & 12.43287 & 0 \\ Friday & 2.20146 & 2.00814 & 2.41339 & 16.82734 & 0 \\ Saturday & 3.15199 & 2.87758 & 3.45257 & 24.70337 & 0 \\ Sunday & 2.93018 & 2.67371 & 3.21125 & 23.00396 & 0 \\ hour1 & 0.63255 & 0.57312 & 0.69814 & \(-\)9.09839 & 0 \\ hour2 & 0.35251 & 0.31733 & 0.39159 & \(-\)19.43723 & 0 \\ hour3 & 0.24146 & 0.21601 & 0.26991 & \(-\)25.00651 & 0 \\ hour4 & 0.21356 & 0.19058 & 0.23931 & \(-\)26.57694 & 0 \\ hour5 & 0.35226 & 0.3171 & 0.39131 & \(-\)19.44892 & 0 \\ hour6 & 1.17162 & 1.06585 & 1.2879 & 3.28083 & 0.00104 \\ hour7 & 4.18853 & 3.82432 & 4.58742 & 30.86091 & 0 \\ hour8 & 8.03662 & 7.34303 & 8.79571 & 45.25545 & 0 \\ hour9 & 4.87561 & 4.45259 & 5.33882 & 34.21192 & 0 \\ hour10 & 2.96347 & 2.70415 & 3.24766 & 23.25162 & 0 \\ hour11 & 3.31302 & 0.32077 & 3.62994 & 25.69912 & 0 \\ hour12 & 4.43575 & 4.05038 & 4.85778 & 32.12553 & 0 \\ hour13 & 4.88128 & 4.45778 & 5.34503 & 34.23761 & 0 \\ hour14 & 4.82277 & 4.40427 & 5.28103 & 33.97139 & 0 \\ hour15 & 5.7931 & 5.29155 & 6.3422 & 38.02006 & 0 \\ hour16 & 7.31935 & 6.68716 & 8.01131 & 43.18873 & 0 \\ hour17 & 9.70314 & 8.86693 & 10.61822 & 49.42118 & 0 \\ hour18 & 9.89627 & 9.04353 & 10.82943 & 49.8568 & 0 \\ hour19 & 6.91728 & 6.3195 & 7.5716 & 41.93976 & 0 \\ hour20 & 4.52315 & 4.13034 & 4.95337 & 32.55599 & 0 \\ hour21 & 3.27109 & 2.98543 & 3.58408 & 25.41923 & 0 \\ hour22 & 2.55577 & 2.33136 & 2.80178 & 20.01237 & 0 \\ hour23 & 1.79846 & 1.63893 & 1.97352 & 12.38455 & 0 \\ Tuesday \(\times\) hour1 & 0.951 & 0.82837 & 1.09177 & \(-\)0.71338 & 0.47561 \\ Wednesday \(\times\) hour1 & 1.00093 & 0.87386 & 1.14646 & 0.01335 & 0.98935 \\ Thursday \(\times\) hour1 & 0.99778 & 0.8722 & 1.14144 & 0.03242 & 0.97414 \\ \hline \end{tabular}
\end{table} 
Table 12: Estimated parameter values for Negative Binomial model (Cluster Model (\(i=8\))).