# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 1 Introduction  
### The Need for Information to Support Pavement Management Decisions  
Roads are a critical component of any society from a small town scale to the highest levels as they allow for the essential movement of goods and services [1] with road transport being the widely used transport mode for both passenger and freight transport in Europe [2]. In many countries worldwide the public road network is the biggest publicly owned asset [3]. To this end, these networks must be kept in a suitable condition making the road agencies' job of maintaining them a critical component towards the development of any region. Road agencies have the responsibility to develop programs that guide rehabilitation and maintenance practices and they have to enact critical decisions in terms of which areas should be prioritized and when interventions should be made. Further complicating their decisions are steadily reducing budgets for these activities [4, 5] that will likely see greater depletions given the current global economic pandemic related situation [6].  
The decisions of these road agencies are generally based on the implementation of a pavement management system (PMS) which falls under the analysis stage of the pavement management process and can be executed at either a network or project level [7]. The PMS is the most typical analytical method and it attempts to optimize the use of financial resources based on the particular needs of the road network [8]. The PMS is a combined toolbox, which uses key road data to help decision-makers create and make the best decisions to allow for optimum system conditions over time [9]. Three critical basic requirements for the adequate application of a PMS [10] are:It serves different types of users within the organization.  
It allows for good decision making concerning the decided programs and projects and allows for the timely execution of projects.  
It makes good use of existing technologies and new ones once they are available.  
The system, while effective, relies heavily on the input data, to ensure appropriate strategies are established. The data used as inputs can generally be categorized as inventory road data and pavement condition data, which are used within the system using various functions and models to produce outputs about the overall network or project assessments and proposed maintenance strategies [9; 11]. This is visualized in Figure 1.  
However, acquiring this data can be problematic due to typically high costs, time, resources and high-level team expertise required to do so [12; 13]. In some authorities, the collection of pavement condition data is done manually in over 90% of the occurrences [14] which therefore allocates a tremendous amount of subjectivity into the process. This can lead to the planning of ineffective strategies. As a result, road asset databases need to be kept up to date and effective. There are significant needs in transportation systems, now and in the future to develop smart infrastructure, that can carry out'self-reporting and self-managing infrastructure' through the application of automated information flows [15]. It should also be noted that the mere presence of a road asset database does not necessarily lead to effective solutions and decision-making. The effective management of these databases should be guided by their relevance, reliability and affordability [16]. These factors remain important regardless of the development of new technologies and methods and should be integral to the design and introduction of any new technique or model. Data collection should also be done at the lowest level of detail satisfactory enough to make effective decisions [17]. Therefore, when designing a database or collection system, the networks' characteristics should be integral to the process along with the ability of the authority to maintain the system both financially and technically.