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1.8 Maintenance of roads and streets based on objective data


The municipal streets environment is different compa- red to state-owned roads, thus a different maintenance approach is required. There are many municipalities with different street network size, population density, climate, economy and maintenance capacities across the country. Finding a suitable solution for all municipa- lities is a huge challenge. Suitable objective methods are missing to prioritise maintenance: Which, when, where and how to maintain their street network?

Good quality data is needed for better decision-making. But which data, how to collect and analyse it? Use of sustainability and machine learning (ML) could be a solution in order to enhance the streetmaintenance approach at the municipal level.


By studying the current maintenance practices and challenges of the municipalities, it has been revealed that municipalities are facing both technical and administrative challenges. Ageing streets, budgetconstraints, and lack of data driven assessment are among the priority issues to address. The sustainability tool SUNRA has been developed to complement the maintenance decisionmaking at the municipal level. The tool was tested on maintenance projects in Skellefteå municipality, which can be adjusted to other municipalities’ capacities. Pavement deterioration models based on ML have been developed to prioritise maintenance at the network level. The models will be further evaluated with additional data.

Impact on society

  1. Improve sustainability awareness in street management at the municipality level.
  2. Improve data-driven decision-making approach in selection of maintenance alternatives.
  3. Effective utilisation of taxpayer money in urban street management.