## 5 Computational Experiments
### Test Scenarios  
To illustrate the system's viability, three tests have been run on the previously described area, whose characteristics are shown in Table 6 below. All the tests assume that a majority of the requests are of the 'HN' type (80%), and only 20% of bookings take place. The first test assumes only requests demanding service exclusively by RP, and it has been included for reference. In contrast, tests 2 or 3 have little or no trips demanding exclusively RP (although the system might assign by itself this modal option in some cases). In all three tests, a number of 10,031 trip requests have been generated by the system during a period of time of 4 h (from 9 to 13) to account for the beginning and end of trips, which is equivalent to an approximate fraction of 20 % of the demand in public transport between the satellite municipalities and the central city. The simulations have used a fleet of 400 RP vehicles with a capacity of four passengers during that period, which is a capacity frequently used in ride-pooling scenarios such as [23]. The ride-pooling service's fare is based on the reported fare from equivalent enterprises already presently operating in Barcelona, set to a 0.5 E base fare, a 1.28 E fare per km and a 0.5 fare inflation (obtained from the average fare costs of Uber and Cabify in Barcelona (Spain) on 15 August 2022, when these experiments were planned. Source: [[https://www.uber.com/global/en/price-estimate/](https://www.uber.com/global/en/price-estimate/)]([https://www.uber.com/global/en/price-estimate/](https://www.uber.com/global/en/price-estimate/)) (accessed on 15 August 2022) and [[https://cabify.com/es/barcelona/lite/tarifas](https://cabify.com/es/barcelona/lite/tarifas)]([https://cabify.com/es/barcelona/lite/tarifas](https://cabify.com/es/barcelona/lite/tarifas)) (accessed on 15 August 2022)). A public \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Type of Requests**} & \multicolumn{3}{c}{**Intermodal Preference**} \\  & **B** & **HN** & **a** & **b** & **=** \\ \hline
**Experiment 1** & 0.2 & 0.8 & 1 & - & - \\
**Experiment 2** & 0.2 & 0.8 & 0.05 & 0.3 & 0.65 \\
**Experiment 3** & 0.2 & 0.8 & - & - & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental instances summary with the type of requests and intermodal preference proportions. Figure 6: Demand movements in Barcelona from 9 a.m. to 13 p.m. (**a**) Origins. (**b**) Destinations. transportation fare in the SIMMB of 1.14 E (obtained from the fare of one ticket from a 10-ticket travel card in Barcelona. Source: [[https://www.tmb.cat/es/tarifas-metro-bus-barcelona/sencillos-e-integrados/](https://www.tmb.cat/es/tarifas-metro-bus-barcelona/sencillos-e-integrados/)]([https://www.tmb.cat/es/tarifas-metro-bus-barcelona/sencillos-e-integrados/](https://www.tmb.cat/es/tarifas-metro-bus-barcelona/sencillos-e-integrados/)) (accessed on 15 August 2022)) has been assumed. In order to evaluate the performance indices of the system, a set of experiments have been carried out through which it has been possible to evaluate, on the one hand, the effect of the batch dispatching parameters and, on the other, it has been verified that the established anticipation times were appropriate. The parameters associated with batch dispatching are in turn related to the time to be able to respond to a set of requests, which can be critical in case of potential saturation. A batch dispatch period of 30 s could be assumed as used by many ride-pooling studies. For the batch size, however, a sensitivity analysis is carried out to establish a preliminary value. Table 7 depicts some results to illustrate its effect on the system performance. Experiments have been carried out on an Intel Core i7-10700 Machine (8-core, 16 threads, 2.90 GHz up to 4.80 GHz Turbo), with 32 GB of DDR4-2666 RAM. Column #**Req** shows the average number of requests (including LL requests) taken into account in each batch. The average simulation time gap between batch dispatchings appears in column **Freq**.