# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 7 Conclusions  
This study was designed to present a flexible framework towards utilizing easily available information on a city and its road network for planning road maintenance interventions. The purpose was to create a workflow that could be replicated by small and under-resourced road authorities who cannot create and/or access large databases but still have to create effective rehabilitation plans. The analyses and models were developed relying on data analytics tools, open source algorithms and deep learning models. For the workflow's validation, a case study in Palermo, Italy was utilized. Within the study, several open source datasets that are easily available were utilized from the census, road traffic and maintenance history. From the information, feature selection tools were developed to identify which of the features are the most important towards explaining the point in time at which intervention activities need to be done. The feature selection tools were based on gradient boosting algorithms that adequately can handle the presence of categorical variables, which commonly exist in these types of datasets. Once the feature selection tool was developed, it was used to pinpoint the most important features, which were then utilized in a deep learning model to help predict the intervention time for a particular road. The most important features were analyzed and validated based on analyses of feature distributions and the specific surroundings. Once the features were integrated into the deep learning model, a model with high accuracy was achieved. It provides a very good assessment of when interventions should occur. The model is also non-contact and does not require excessive physical surveys. This process, therefore, utilizes the strengths of gradient boosting algorithms and deep learning, leveraging their combined power to handle categorical variables and search out connections on a deeper level than traditional approaches. Future work will look at different cities and the use of different feature sets to further establish the accuracy of this data exploitation technique. The use of the process in other cities will further validate the workflow along with checks in the following years to verify the accuracy of the next sequence of predicted road interventions. The predicted models in the study can aptly be used to generate schedules of which roads and sections should be prioritized over time. Further to the results of the study, a pipeline was also identified as to how the models and techniques could be used with other low-cost techniques for detecting pavement distresses and planning pavement interventions. The workflow helps exploit several low-cost element techniques, which would be helpful for many agencies given their budgetary allocations. Finally, it is worth pointing out this study exploits both low-cost investigation techniques on road pavement conditions and indirect data analytics, to establish over time and space, the best network maintenance strategy. This would allow more budget allocation towards effective interventions rather than on complex and expensive systematic investigations. With the techniques combined, they offer another step towards low-cost automation of elements of the PMS. Conceptualization, R.R. ; methodology, R.R., and G.D.M. ; software, R.R. ; validation, R.R., L.I. and G.D.M. ; formal analysis, R.R. and G.D.M. ; investigation, R.R. and G.D.M. ; resources, G.D.M. ; data curation, G.D.M. ; writing--original draft preparation, R.R. ; writing--review and editing, R.R., L.I. and G.D.M. ; visualization, R.R. and G.D.M. All authors have read and agreed to the published version of the manuscript. The research presented in this paper was carried out as part of the SMARTI ETN project under the H2020-MSCA-ETN-2016. This project has received funding from the European Union's H2020 Programme for research, technological development and demonstration under grant agreement number 721493.