Predictive maintenance for the road infrastructure in South Tyrol

Practical example and current challenges

• Road infrastructure is one of the most important and valuable assets of a country. It is a central element for the efficient growth of an economy and has to meet many requirements: On the one side, citizens demand a reliable infrastructure to reduce their car operating costs, and on the other side, public administrations try to manage their road budgets as efficiently as possible.
• In addition, the quality of the road infrastructure also has an impact on tourism, the transport and supply industry, urban development and the satisfaction of the citizens.
• At present, this budget planning is done according to outdated methods and follows the logic of "preventive maintenance", where a road maintenance intervention is planned manually by experts in the field, based on information such as date of the last intervention, available budget, manually determined quality of the road, characteristics of the road (geometry, height, gradient).
• Against the background of the challenging and important planning of road maintenance measures by public decision makers, the use case aims at implementing a model for "predictive maintenance" based on available data. Smart planning or smart maintenance of the road infrastructure is therefore important to optimise the quality of roads and the allocation of the available budget. Based on the data collected in South Tyrol by means of machine learning algorithms, the necessary budget for the proper maintenance of the road infrastructure in the autonomous province of Bolzano, can be predicted and allocated effectively. The system collects data from a variety of data sources, which provide indications on the quality level of the roads and the level of road use.
• Existing market solutions for predictive maintenance have been developed specifically for the individual needs of users and the type of data available. However, there are neither open standards nor open source projects or software for predictive maintenance of road infrastructure.

What added value does the "GAIA-X project" offer?

• GAIA-X acts as a data lake and scalable infrastructure for the storage and processing of heterogeneous data sources used by public administrations to make more well-founded decisions for road maintenance.
• GAIA-X aggregates heterogeneous data sources from different types of sensors and data sources that are useful for determining the quality of the road surface and potential maintenance issues.
• In addition, GAIA-X offers the ability to process this large amount of data in order to generate an evaluation with a response time that meets the public administration's budget planning requirements.
• Applying a predictive maintenance solution for road budgeting, leads to a better management of public funds, allowing the use of these funds for targeted maintenance work with maximum efficiency. In addition, other added values are improved road surface quality, increased infrastructural competitiveness of an region for investment, insights on travel and tourism patterns, reduced impact of road works on citizens in the form of downtime and congestion, savings in agency costs by the public administration and lower road user costs for citizens and infrastructure users.
• The cloud-based solution can potentially be used by other public administrations in Europe, since the problem described is common to all public administrations.

Use Case Team

• Department of Infrastructure and Mobility of the Autonomous Province of Bolzano supported by Südtiroler Informatik AG - Contact person: Stefan Gasslitter
• SAP Walldorf – Contact person: Fabian Biegel