# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 4 Description of Case Study--Palermo, Italy
#### Characteristics of Available Data  
As previously mentioned, the data collected were obtained from government authorities, both locally and nationally. The data compiled from these sources resulted in a dataset of 1099 maintenance occurrences over 10 years with the recordings made in six time period groups across the ten years. The local company legally required to carry out these maintenance activities is currently Risorse Ambiente Palermo S.p.A. (RAP) [70]. The data sources are summarized in Table 3. It must be noted whilst the data were obtained  
Figure 4: Circumscription divisions within Palermo (screenshot taken from Google Earth [69]). from several open source systems, it did involve multiple stakeholders and the research team had to collate the items across matching points for the final dataset. However, this is an unavoidable task to ensure that the characteristics of the area are understood and additionally it is a step that would only be needed to be done at the beginning of the process in a practical implementation scenario. According to the legal requirements of the company, it has to carry out a minimum of 1.8% surface area intervention on the road network per year. For the planning of interventions, the company employs the application of filtering criterion that produces a list of roads to be maintained that are in the worst deterioration state and the process favors those roads considered critical with higher traffic intensity. To create this list of roads with grave severities, road inspectors carry out laborious monitoring activities throughout the year to update road conditions. They manually observe conditions, using video and physical surveys [74], and generate reports on the road conditions. Whilst this can be an effective method to collect data, it can also be expensive considering the number of surveys and personnel involved. Therefore, having a model that could assist in predicting which roads are likely to need rehabilitation would reduce costs. Additionally should physical surveys not be possible, the model could be utilized to produce a non-subjective indication of where interventions should be done. The instances of intervention were recorded for six time intervals over the period and therefore within the data, the year group is recorded with numerical values with a range of 1-6 where the interventions occurring at year group 6, represent interventions at 10 years beyond the first recording. The recorded six groups are equally spaced out across the ten years meaning that each group represents one-sixth of 10 years, i.e., approximately 20 months. The roads within the list are those having a high severity rating and therefore requiring full surface depth repair over the area specified in the data. The characteristics of the roads on which these interventions were carried out were compiled and this represented the list of features to be analyzed. Unlike large databases like the LTPP database, where detailed technical information on the road condition and monitoring, such as PCI and IRI values, are available, the dataset focused on physical road characteristics, movements of cars and people and economic activities carried out on and near the roads. This issue is evident in many small countries and cities where there is a lack of funds to have extensive databases and therefore the challenge exists to produce reputable data analyses with limited data. The features produced are given in Table 4 and do provide a good representation of the specific situation considering the typical overview feature groups previously discussed in Section 1.2, allowing for an accurate portrayal of the area. The features produced were linked to the roads identified in the historical data by careful data matching.