## 8 Experimental Scenarios and Results
### Auto Mode Detection with MAGIS  
A particular ITS application of traffic monitoring is considered in this scenario. The traffic conditions experienced by private auto mode vehicles are usually considered as the general traffic flow because other modes of transportation, such as public transit or trucks, behave differently due to their specific roles, such as serving passengers at bus stops or carrying heavy loads of goods. Therefore, in the case of traffic monitoring, the mode detection among auto vs. non-auto modes are sufficient. This implies that all modes, other than the auto modes, are considered as non-auto modes altogether. In this section, raw data from non-auto modes are randomly shrunk down in size to match the size of the data from the auto mode with 50/50 ratio, in order to truly see the classifying power of the ANNs. Figure 7 shows the design layout of the ANNs for auto mode detection with DL, SP, and MAGIS approaches.  
Table 4 and Figure 8 show the results of auto mode detection with DL, SP, and MAGIS approaches. When the actual mode of transportation is auto mode and its mode is correctly estimated as auto mode, it is denoted as an A-A detection rate. Similarly, if the actual mode is non-auto mode and the estimated mode is non-auto mode, it is denoted as an N-N detection rate. Ideally, if the A-A and N-N detection rates are both 100%, the mode detection is considered perfect.  
Figure 6: Bar graphs of the results from universal mode detection with DL, SP, and MAGIS.  
Figure 7: Artificial neural network layout for auto mode detection with DL, SP, and MAGIS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**\# of Pings**}} & \multicolumn{8}{c}{**Monitoring Duration**} \\ \cline{2-11}  & \multicolumn{2}{c}{**5 min**} & \multicolumn{2}{c}{**10 min**} & \multicolumn{2}{c}{**15 min**} & \multicolumn{2}{c}{**20 min**} \\ \cline{2-11}  & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** \\ \hline
2 & 49 & 68 & 52 & 72 & 54 & 75 & 55 & 78 \\
5 & 53 & 70 & 59 & 72 & 60 & 77 & 67 & 80 \\
10 & 58 & 74 & 62 & 74 & 67 & 81 & 71 & 84 \\
15 & 63 & 79 & 65 & 78 & 71 & 83 & 74 & 86 \\
20 & 67 & 81 & 69 & 84 & 73 & 87 & 77 & 89 \\ \hline \multicolumn{1}{c}{**B. Auto Mode Detection Rates with Smartphone**} & & & & & & & \\ \cline{2-11}  & \multicolumn{2}{c}{**Monitoring Duration**} & & & & & & & \\ \cline{2-11}  & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** \\ \hline
2 & 54 & 77 & 53 & 77 & 56 & 79 & 58 & 81 \\
5 & 63 & 83 & 64 & 83 & 63 & 85 & 65 & 85 \\
10 & 66 & 82 & 69 & 86 & 73 & 88 & 77 & 89 \\
15 & 72 & 85 & 74 & 89 & 77 & 91 & 82 & 94 \\
20 & 75 & 88 & 76 & 90 & 84 & 94 & 87 & 92 \\ \hline \multicolumn{1}{c}{**C. Auto Mode Detection Rates with Mode Availability Layer**} & & & & & & & \\ \cline{2-11}  & \multicolumn{2}{c}{**Monitoring Duration**} & & & & & & & \\ \cline{2-11}  & \multicolumn{2}{c}{**5 min**} & \multicolumn{2}{c}{**10 min**} & \multicolumn{2}{c}{**15 min**} & \multicolumn{2}{c}{**20 min**} \\ \cline{2-11}  & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** & **A-A** & **N-N** \\ \hline
2 & 62 & 82 & 65 & 83 & 69 & 86 & 73 & 88 \\
5 & 68 & 85 & 70 & 86 & 78 & 89 & 80 & 90 \\
10 & 77 & 88 & 75 & 89 & 82 & 91 & 89 & 93 \\
15 & 82 & 91 & 84 & 92 & 89 & 92 & 92 & 94 \\
20 & 84 & 92 & 88 & 95 & 93 & 94 & 95 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of auto mode detection rates (%) with DL, SP, and MAGIS.  
Figure 8: A-A surface and N-N surface of auto mode detection with DL, SP, and MAGIS.  
The results show the DL approach, while showing the main diagonal with the highest values for both A-A and N-N are all lower than the SP or MAGIS-based results. The MAGIS approach shows the highest rates for both A-A and N-N. The results are significantly higher than DL or SP approaches and it shows that the mode availability binary input does improve the detection rates for both A-A and N-N rates.