# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 5 Results and Discussion  
### Examination of Data  
The first step was the uploading and examination of the data utilizing a pandas data frame. The first analytical step was a descriptive analysis of the features of the dataset. Within the preprocessing step, it was shown that there were no missing data entries, which is important to ensure there are no model inconsistencies later in the process. As previously mentioned there were 21 feature categories present in the dataset each carrying 1099 values corresponding to the maintenance activities carried out over the previous 10 years (2001-2019). This timeline thereby represents a significant period for analysis to ensure credibility in the process. To understand the distributions within the data, histograms were generated for the features to analyze the distributions of the features across the period. Of particular interest were the distributions of the features corresponding to the physical road characteristics (length and area) and the distributions for the occurrences of activities within the different zones, circumscriptions and neighborhoods over the 10 years. The length and area are directly related to each other with their distributions showing a direct similarity. The distribution for the length feature is shown in Figure 5. This showed that the majority of interventions were of a road length of less than 500 m. \begin{table}
\begin{tabular}{c c} \hline
**Data Group.** & **Description of Data** \\ \hline neighborhood & neighborhood where the road is located \\  circ & Circumscription (circ.) where the road is located \\  circ\_pop & population of circ. where the road is located \\  street\_category & road category classification (labelled 1 or 2; where 1 represents the higher trafficked option) \\  length & road length (measured in meters) \\  area & road area covered (measured in sq. meters) \\  zone & zone where road is located (1-historic center, 2-main city, 3-peripheries) \\  year & year group of maintenance \\  pop\_den & density of population in circ. \\  public\_buildings & The presence or lack of public buildings near the road (labelled 1 for yes and 0 for no) \\  commercial\_activities & The presence or lack of commercial activities along the road (labelled 1 for yes and 0 for no) \\  traffic\_rate & rate of activities in traffic zone where the road is located \\  t\_pop & population of traffic zone where the road is located \\  t\_pop\_den & population density of traffic zone where the road is located \\  t\_workers & number of workers in the traffic zone where the road is located \\  unemployment & percentage of unemployment in circ. where road is located \\  industrial\_jobs & percentage of industrial jobs in circ. \\  circ\_road\_den & road density within circ. where road is located \\  t\_arrivals & number of trip arrivals in the traffic zone where the road is located \\  t\_z\_departures & number of departures in the traffic zone where the road is located \\  t\_z\_perdays\_rt & number of total trips made within the traffic zone where the road is located \\ \hline \end{tabular}
\end{table} 
Table 4: Description of features within the dataset. Subsequently, the distributions corresponding to the geographical divisions of Palermo were examined. The distribution corresponding to the zoning is shown in Figure 6 (where the zones are numbered as described in Table 4 with 1 representing the historical center, 2--the main city and 3 the peripheries). This highlights that most of the interventions did not take place within the historical center and instead were concentrated outside of the center and the central districts of the city. This was expected given the traffic levels within the center and a higher level of foot traffic that occurs within the historical district due to restrictions and congestion charging being in place. Furthermore, zone 3 is larger and therefore has more roads than the other two zones. This provides another key to understanding necessary intervention scheduling in the municipality.