Maintenance and management of municipal street pavements in northern Sweden
Practices, challenges and performance models
Time: Fri 2025-11-21 13.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Video link: https://kth-se.zoom.us/j/67258935998
Language: English
Subject area: Civil and Architectural Engineering, Building Materials
Doctoral student: Muhammad Amjad Afridi , Byggnadsmaterial, Department of Streets and Roads, Skellefteå Municipality, 931 85 Skellefteå, Sweden
Opponent: Professor Pauli Kolisoja, Faculty of Built Environment, Tampere University, Finland
Supervisor: Adjunct Professor Sigurdur Erlingsson, Väg- och banteknik, Swedish National Road and Transport Research Institute (VTI), 58195 Linköping, Sweden ; Faculty of Civil & Environmental Engineering, University of Iceland, 108 Reykjavik, Iceland
Research funders:
Skellefteå municipality
Mistra InfraMaint Project 1.8 (DIA 2016/28)
QC 20251029
Abstract
An effective municipal street network is essential for regional development, supporting mobility and public utilities, and requires optimal maintenance strategies for efficient use of public funds. This research, focused on northern Sweden, aims to enhance municipal street maintenance by integrating sustainability frameworks, current practices, and pavement performance modelling through five complementary studies.
The Sustainability National Road Administrations (SUNRA) framework was adapted for both Swedish Transport Administration (STA) road projects and municipal street maintenance. Findings show it can be effectively applied during planning for investment, maintenance, and construction or reconstruction projects.
Insights from a survey of Swedish municipalities highlighted pavement maintenance practices and challenges. Common pavement distresses included potholes, uneven surfaces, and alligator cracking. These were mainly caused by pavement ageing, heavy traffic, and patching. Cold climate and population density were additional factors. Automated pavement data collection, commercial pavement management systems (PMS), and performance models were rarely used. The windshield method, however, remained common. Northern and densely populated municipalities allocated higher budgets to pavement maintenance and rehabilitation.
Two machine learning (ML) studies and one sigmoid deterioration modelling study predicted the pavement condition index (PCI) over time using manually collected data from Skellefteå Municipality (2014, 2018, 2022). Both ML studies tested linear regression (LR), random forest (RF), and neural network (NN) algorithms, with RF achieving the highest prediction accuracy. Pavement age was the most important variable in the first study. The second study, using extended datasets with maintenance treatment categories, slightly improved predictions. Key variables for predicting the 2022 PCI included previous status (2018) and weighted distress.
Sigmoid deterioration curves captured non-residential street deterioration effectively but were less accurate for residential streets, probably due to variable pavement age and frequent utility cuts. Similarly, curves for pavements treated with surface levelling (SL) and special treatments (ST) performed best, while milling and resurfacing (MR) provided a balanced cost-performance outcome.
These findings support data-driven decision-making and optimized municipal street maintenance. Further evaluation using data from multiple municipalities, including automated collection methods and climate factors, is recommended.