## 4 Self-Organizing Maps Applied to Driving-Behavior Classification
### SOM-Based Drivers Grouping Regarding Fuel-Consumption
#### 4.3.1 SOM Classification Results  
The above visualizations of the trained SOM can only be used to obtain qualitative information concerning driving behavior. Interesting groups of neurons (i.e., clusters) must be identified and labeled in order to develop meaningful quantitative descriptions of driving data, suitable for a real-time fuel consumption assessment. Although the clustering of the SOM can be performed by means of any unsupervised clustering method, such as K-means or hierarchical clustering, the U-matrix method was used in this work. First, we carried out a three-cluster grouping of the SOM neurons. Table 3 presents relevant statistical values of fuel consumption for each cluster: average value, variance, and maximum value. Taking these values into account, the clusters were labeled as Very Low, Low, and Medium-High. The classification results applied to the Uyanik dataset are shown in Figure 9, where three-dimensional views are provided. The three displayed clusters are compact, their contained data are contiguous, and they are clearly separated, as can be seen in Figure 9. Matching clusters with their associated consumption displayed in Table 4 by color, it is apparent that the green cluster corresponds to medium-high fuel consumption rides, the blue one to low consumption, and the red group represents very low fuel consumption rides. Additionally, by analyzing clusters' fuel consumption variances, it can be seen that the higher the average value, the higher the variance, with this correlation being a noticeable feature of the identified groups. Further analysis of the relationships of the identified groups with the driving features displayed in each sub-figure can be performed. Regarding Figure 9a,b, the DS groups look similar, since the GP variable of (a) and the XACC var of (b) are highly correlated as a measure of swift operation of the gas pedal. On the other hand, Figure 9c shows a different cluster distribution. In this graph, the Low \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Driver** & **Very Low (\%)** & **Low (\%)** & **Medium-High (\%)** \\ \hline D1 & 4.7 & 20.0 & **75.3** \\ D2 & 49.4 & 10.4 & 40.2 \\ D3 & 23.0 & 33.3 & 43.7 \\ D4 & 7.8 & 51.1 & 41.1 \\ D5 & 33.3 & 26.5 & 40.2 \\ D6 & **80.2** & 10.4 & 9.4 \\ D7 & 15.1 & 33.7 & 51.2 \\ D8 & 16.2 & 29.7 & 54.1 \\ D9 & 8.1 & 38.4 & 53.5 \\ D10 & 56.3 & 0 & 43.7 \\ D11 & **78.6** & 11.9 & 9.5 \\ D12 & 10.6 & 33.0 & 56.4 \\ D13 & 14.0 & 46.5 & 39.5 \\ D14 & 8.4 & 25.3 & **66.3** \\ D15 & 2.5 & 45.7 & 51.8 \\ D16 & 14.3 & 40.5 & 45.2 \\ D17 & 38.1 & 21.4 & 40.5 \\ D18 & 2.7 & 48.0 & 49.3 \\ D19 & 15.6 & 30.0 & 54.4 \\ D20 & 32.2 & 26.9 & 40.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of the route that each driver travels using different fuel-consumption driving styles (DSs) (three-cluster classification). and Very low consumption classes (blue and red, respectively) are interleaved. This happens because the correlation between XACC var and GP is strong, with GP vs. XACC var providing no additional meaningful information. In contrast, the PGP vs. GP and the PGP vs. XACC var planes show that the positioning of the clusters is interchanged with respect to Figure 9,b. Nevertheless, this interchange is coherent with the precedent figures, since the green cluster is placed at the upper range of the PGP axis, while the other ones are at the lower range, the blue cluster is related to low GP, and the red one is related to medium GP. The same clusters' position interchange phenomenon can be observed in Figure 9, according to the aforementioned characteristics. When considering the cluster distribution of Figure 9, and taking into account that aggressiveness and fuel consumption are well correlated, considering this figure as our baseline for further comparisons, we can assert that  
* Very low fuel consumption (red) corresponds to drivers who keep the car running at its lowest regime (low PGP, low ERPM, and medium GP).