## 5 Computational Experiments  
### Case Study: City of Barcelona  
The intermodal simulation model of this paper has been tested on the inner region of the metropolitan area of Barcelona, in Spain, as shown in Figure 5, comprising several municipalities, the most important of them being the city of Barcelona as a strong attractor of trips and activities from the surrounding cities. Those included in the _Test Area_ of this paper (referred to simply as TA in the following) have been Barcelona itself, L'Hospitalet del Llobregat (southwest), Sant Adria del Besos and a portion of Badalona and Santa Coloma de Gramanet. In all, the TA has an extension of nearly a 20 \(\times\) 15 km area of a very large urban density.  
The PT network under consideration in the TA has been made up of PT facilities in the Integrated System for Metropolitan Mobility of Barcelona (SIMMB), i.e., the bus, metro and tram lines, and the rapid transit systems operated by the Generalitat de Catalunya. Additionally to the SIMMB, the Spanish railways (RENFE) have also been included. All of them make up a large PT network with about 3000 stops, nearly 300 distinct lines and more than 6000 services during the operational horizon assumed in the tests (from 9 to 13). Moreover, the private traffic network is constituted of 114,000 links and 87,300 nodes. This contrasts significantly from other studies in the literature, which employ either a small test area or with a very tiny portion of the entire PT network.  
As a reference, Table 5 below shows the average trip travel times for each of the transportation options employed in the test area. This information has been obtained using the Virtual Mobility Lab network model developed in [16], being refined and adapted by the authors during the development of this intermodal simulation system.  
Like many other Spanish cities such as Madrid ([17; 18]), Seville ([19]) and similar large metropolitan areas in the world, Barcelona's metropolitan area has an extensive public transit network on which many people rely on a daily basis, a solid high-capacity private transit network, and the emergence of innovative shared mobility services such as ride-pooling (Uber, Cabify, etc.) and motorbike-sharing (eCooltra, SEAT MO, etc.), which have steadily gained popularity since their disruption ([20; 21]). A characteristic shared in common by all large metropolitan areas is the one depicted in Figure 6 for the city of Barcelona, where the majority of morning commutes originate from various locations throughout the area but are mainly headed to the city centre, which houses the majority of businesses and entertainment. Additionally, as evidenced by studies such as [22], movements inside the city of Barcelona are frequently undertaken using the public  
Figure 5: The test area showing the various types of public transportation lines.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **InterM** & **IntraM** \\ \hline
**Non-Motorised** & 30.3 & 14.5 \\
**Public Transport** & 47.2 & 25.9 \\
**Private Transport** & 26 & 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Trip travel times (min) in the SIMMB. InterM = between different municipalities, IntraM = internal to a municipality.  
transit network. Its usage may, however, diminish when the origin or destination is on the outskirts due to a more limited variety of options, which forces them to switch to private alternatives. As a result, the entire metropolitan region is an ideal scenario for testing the proposed intermodal approach to connect users in places where this transit offer is inefficient, which will use the ride-pooling service as a feeder for the public transport, reducing customers' dependency on private mobility.