# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 4 Description of Case Study--Palermo, Italy  
For the study, it is important to understand the particulars of the area. Palermo is located on the north-western coast of Sicily, Italy, covering an area of 158.88 km\({}^{2}\) with 663,401 inhabitants [64]. It is a part of southern Italy where municipalities have been shown to have a lower efficiency when considering the difference between the assessed spending needs given conditions and the actual spending [65]. This is an important factor as the efficiency level is associated with productivity levels. This is coupled with lower spending on information technology (IT) systems and services within the public sector [66] which limits the capacity of agencies to use highly technical systems. As a result of this, there is a great opportunity to use Palermo as a case study given the limited resources these authorities face and therefore the need to implement systems that do not require significant IT investment and resources.  
The municipality's road network consists of approximately 3800 road axes with a total surface area equal to approx. 9 million m\({}^{2}\). This is a significantly large network and therefore having a system in place that could pinpoint particular areas of interest for intervention would be very useful. Data on the commute from the last population census carried out by ISTAT (Italian National Institute of Statistics) [67] show that there are approximately 278,954 individuals who commute every morning, for work and study purposes, of which 96.3% of the movements are carried out within the municipality. It is also important to consider that car sharing is very low with the average coefficient of vehicle occupancy being 1.3 and the highest mode of transport for these trips being private cars (48.2%) [68]. This is an important consideration as it means that the number of workers within the city will have a significant impact on the traffic levels and in turn the roads with higher debilitating conditions. The area was previously divided into 25 neighborhoods but this was subsequently amended in 2009 to a division of eight circumscriptions [68] as shown in Figure 4. There are overlaps between the divisions and, as the data collected have factors from both divisions, both are considered. Circumscription no.1 is particularly important as it houses the historical district of the city and the ZTL (Limited Traffic Zone) area, which has a traffic congestion charge attached to it and additionally is where a lot of tourism is concentrated given the presence of historical buildings, museums and artifacts. Circumscription no. 8 is also important given its central location and proximity to the port.  
The circumscription and neighborhood divisions are very important as population and industrial data were collected and aggregated according to these divisions. It is critical for any model development to understand the regional and geographical divisions within a region to adequately collate the data collected and, therefore, this should be carried out in any study of this nature. For traffic analysis, the area is divided into 200 zones ('PUT' traffic zones) across its area. This is particularly important as commute and commercial activity are also recorded based on the interactions between these 'traffic zones'.