# Analyzing Demand with Respect to Offer of Mobility
## 6 Scenarios Analysis and Results
### Alternative Scenario Analysis  
In order to make a decision multiple scenarios can be produced, and it is possible to analyze the impact of changes with respect to a reference scenario (e.g., the actual). Thus, to create a new scenario, the data of the mobility demand and the service offer are loaded in a new instance of the knowledge base. To this end, it is possible to use GTFS management tools (e.g., static GTFS manager) to generate (or update) the service offer and mobility \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Morning time & \(05:00-13:59\) \\ \hline Afternoon time & \(14:00-01:59\) (next day) \\ \hline Time interval size & \(59\) (min) \\ \hline Radius \(r\) around stop \(s\) & \(200\) (m) \\ \hline Total (morning) pick-ups \(pck(mng)\) & \(83\),\(125\) \\ \hline Total (afternoon) drop-offs \(pck(aft)\) & \(75\),\(234\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation setup for Actual Scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **TAF Dataset** & **Florence (Total)** \\ \hline Service time span & \(05:00\) to \(01:59\) (next day) & \(04:00\) to \(01:59\) (next day) \\ \hline Lines & \(25\) (\(21.92\%\)) & \(114\) \\ \hline Trips & \(1173\) (\(16.67\%\)) & \(7033\) \\ \hline Stops & \(942\) (\(62.26\%\)) & \(1513\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ATAF dataset description. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Pick-Ups** & **Drop-Offs** \\ \hline \multirow{6}{*}{**Whole ATAF dataset**} & **Max.** & \(59\) & \(59\) \\ \cline{2-4}  & **Avg.** & \(1.27\) & \(1.27\) \\ \cline{2-4}  & **RMSE** & \(2.74\) & \(2.74\) \\ \cline{2-4}  & **MAE** & \(1.36\) & \(1.44\) \\ \cline{2-4}  & **MASE** & \(0.89\) & \(0.94\) \\ \cline{2-4}  & **MSE** & \(-0.8\) & \(-0.7\) \\ \hline \multirow{6}{*}{**Removing 1\% of outliers in**} & **Max.** & \(10\) & \(10\) \\ \cline{2-4}  & **Avg.** & \(1.09\) & \(1.09\) \\ \cline{1-1} \cline{2-4}  & **RMSE** & \(1.98\) & \(2.05\) \\ \cline{1-1} \cline{2-4}  & **MAE** & \(1.17\) & \(1.25\) \\ \cline{1-1} \cline{2-4}  & **MASE** & \(0.91\) & \(0.97\) \\ \cline{1-1} \cline{2-4}  & **MSE** & \(-0.6\) & \(-0.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The values of features in ATAF dataset in the city of Florence. demand (e.g., changing the number of trips or vehicle lines passing a stop, adding a stop, changing the location of a stop). For example, Indipendenza XXVII Aprile stop, operated by ATAF, which is called p.Za Indipendenza by another mobility operator (Busitalia), is the most crowded stop in the city of Florence with for each day more than 377 pick-ups and 407 drop-offs, in accordance with the DORAM model. It indicates that, a considerable number of people wait at the stop during a working day. Such a situation could cause several consequences (e.g., high passenger destiny at the stop, increasing waiting time at the stop, decreasing operating speed, decreasing travel time reliability [46]). The solution has been to balance the number of waiting people at crowded stops and, to mitigate the problem, we created a scenario with a modified offer, called Alternative Scenario splitting the lines by (see Figure 1): (1) adding an alternative stop, (e.g., called Cosimo Ridolfi), in a suitable location (e.g., Indipendenza XXVII Aprile) stop; (2) transferring some line(s) to the newly added stop. For the sake of clearness, the mentioned bus-stop Indipendenza XXVII Aprile is located in the central area of Florence, and it represents a fundamental and crowded stop in the city. In the case, the alternative scenario with respect to the actual one is related to a new bus-stop creation near the bus-stop Indipendenza XXVII Aprile, for example in the nearest road which is called Cosimo Ridolfi.