## 8 Experimental Scenarios and Results
### Universal Mode Detection with Smartphone with Mode Availability GIS (MAGIS)  
Figure 5 shows the designs of ANNs for the universal mode detection for all modes of transportation based on their physical characteristics. Auto mode would typically experience the highest maximum speed, while transit buses would frequently accelerate and decelerate even during free-flow periods for serving transit stops. As Table 2 suggests, a boat mode would have a clearer view of the sky and \"see\" a greater number of satellites. In all of the above cases, observing a single particular set of data is not sufficient for reliably detecting the mode, as [PERSON] et al. [24] suggest. By making observations for a longer duration of time and sampling the data more frequently, it would give more opportunities for ANNs to capture particular sets of patterns for each mode and make more reliable mode estimations. In this scenario, 10 min of monitoring duration with five data samplings, also known as \"pings\", are used for all three approaches of DL, SP, and MAGIS.  
Table 3 and Figure 6 show the results from the universal mode detection using DL, SP, and MAGIS approaches. All three results show the highest values in its main diagonals, meaning that the proposed ANNs are detecting the correct modes the most among other modes. However, the mode detection rates of DL are the lowest among the three methods, probably due to its estimated acceleration values and lacking newly-available emerging input values from smartphones. The MAGIS performs significantly better than DL and SP methods due to its additional input factors from smartphone sensors and the binary input variables prepared from MA layers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**A. Universal Mode Detection with Conventional GPS Data Logger**} \\ \hline \multirow{2}{*}{**Actual Modes**} & \multicolumn{6}{c}{**Predicted Modes**} \\ \cline{2-7}  & **Auto** & **Bus** & **Truck** & **Tram** & **Walk** & **Bike** & **Boat** \\ \hline Auto & 42 & 12 & 18 & 11 & 4 & 5 & 8 \\ Bus & 8 & 48 & 10 & 26 & 3 & 2 & 3 \\ Truck & 25 & 13 & 42 & 11 & 4 & 4 & 1 \\ Tram & 11 & 12 & 10 & 42 & 4 & 11 & 10 \\ Walk & 4 & 3 & 2 & 4 & 52 & 23 & 12 \\ Bike & 7 & 8 & 5 & 7 & 15 & 53 & 5 \\ Boat & 2 & 5 & 8 & 17 & 14 & 9 & 45 \\ \hline \hline \multicolumn{7}{c}{**B. Universal Mode Detection with Smartphone**} \\ \hline \hline \multicolumn{7}{c}{**Predicted Modes**} \\ \cline{2-7}  & **Auto** & **Bus** & **Truck** & **Tram** & **Walk** & **Bike** & **Boat** \\ \hline Auto & 61 & 9 & 13 & 7 & 3 & 4 & 3 \\ Bus & 7 & 63 & 9 & 14 & 4 & 2 & 1 \\ Truck & 18 & 9 & 57 & 10 & 3 & 2 & 1 \\ Tram & 9 & 11 & 9 & 53 & 3 & 7 & 8 \\ Walk & 1 & 2 & 3 & 4 & 74 & 11 & 5 \\ Bike & 5 & 6 & 4 & 5 & 11 & 67 & 2 \\ Boat & 1 & 3 & 4 & 13 & 11 & 7 & 61 \\ \hline \hline \multicolumn{7}{c}{**C. Universal Mode Detection with Mode Availability Layer**} \\ \hline \hline \multicolumn{7}{c}{**Predicted Modes**} \\ \cline{2-7}  & **Auto** & **Bus** & **Truck** & **Tram** & **Walk** & **Bike** & **Boat** \\ \hline Auto & 78 & 5 & 7 & 4 & 2 & 3 & 1 \\ Bus & 4 & 82 & 4 & 7 & 2 & 1 & 0 \\ Truck & 8 & 2 & 82 & 4 & 2 & 1 & 1 \\ Tram & 4 & 7 & 5 & 76 & 2 & 5 & 1 \\ Walk & 2 & 3 & 4 & 5 & 74 & 7 & 5 \\ Bike & 3 & 4 & 5 & 4 & 8 & 72 & 4 \\ Boat & 1 & 2 & 2 & 5 & 3 & 5 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of universal mode detection rates (%) with DL, SP, and MAGIS.