Sustainable decision support based on relevant data

Maintenance of roads and streets based on objective data

Challenge

About state roads there are many methods to prioritise when, where and how a road should be maintained but not for city streets. City streets’ states can not be described as similar to rural roads.

Expected results

Digitalisation and connectivity, new technology and new sensors have resulted in a wealth of new data that can complement traditional. Deep Learning and Machine Learning are methods that are suitable for analysis of state data. The decision on maintenance will be based on selected limit values. Too high or too low values entails both financial and security risks.

Modern efficient maintenance should build on objective data and take into account functional goals: to be traffic-safe, user-friendly and accessible.

 

1.8

Timeplan

2018 – 2022

Project manager

Sigurdur Erlingsson, KTH
sigurdur.erlingsson@vti.se

Partners

Skellefteå, VTI Swedish National Road and Transport Research Institute, KTH Royal Institute of Technology, Ramböll, NCC, PEAB, Skanska, STA Swedish Transport Administration, RISE Research Institutes of Sweden, City of Gothenburg

Expected impacts:

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Improved sustainable efficiency by 20 percent

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Competence deficiencies will decrease by 50 percent

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Over 50 municipalities are role models for sustainable asset management

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At least ten companies have adopted the results from the programme and export service / products, competence and practice

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