One in five serious or fatal road traffic accidents occur under severe weather conditions. Despite notable improvements in traffic safety, the Vision Zero approach of shared responsibility for eliminating fatalities and serious injuries remains a global challenge. As the vehicle fleet becomes increasingly connected and automated, vast amounts of data are generated for every kilometer traveled, data that can be used to enhance road safety. One promising application is the monitoring of tire–road friction to improve understanding of road surface conditions and the interaction between tire and road. Since 2018, the Swedish Transport Administration has obtained connected vehicle data, sometimes referred to as floating car data (FCD) or probed vehicle data, to follow up on tire-road friction. The focus of the administration has been on how connected vehicle data can be applied to support and improve winter road maintenance on Sweden’s public road network. Within the Digital Vinter project, connected vehicle data have been validated and analyzed alongside conventional tire-road friction estimation methods and in relation to Road Weather Information Systems (RWIS) and Mobile Reporting of Ploughing (MIP). Some of the results from the Digital Vinter project are presented within this thesis.

The thesis comprises seven papers: three journal articles and four peer-reviewed conference papers. Paper A demonstrates the proof of concept, investigating temporal and spatial coverage in relation to AADT and showing that three independent suppliers exhibit strong correlations, partly validating one another. Paper F continues this theme by analyzing coverage across different operational areas with varied road types and traffic intensities, as well as seasonal behavior. Together, these papers highlight the importance of understanding data characteristics for correct interpretation. Paper C compares two suppliers using confusion matrices and shows that, for one-hour measurements on the same 550-m road segments, friction estimates exhibit correlations of 85–93% during winter.

Paper E analyzes connected vehicle friction data in relation to RWIS and MIP. The results show that connected vehicles capture friction changes before, during, and after snowfall in near real time. The paper also discusses the potential of integrating these data into maintenance decision-support systems through machine learning.

Several field tests were also conducted. The first occurred in Björli, Norway (2020; Paper B), where three suppliers participated with individual vehicles, compared against a RoAR MK6 reference device. Paper G presents a similar test in Kiruna, Sweden (2024), involving two suppliers with two vehicles each, analyzed together with cloud-based fleet data and reference systems such as ViaFriction and Coralba-µ. In both campaigns, all vehicles and systems detected high and low friction on both homogeneous (proving ground) and inhomogeneous (public road) surfaces. Papers B and G show that conventional continuous systems such as ViaFriction generally report friction values 0.05–0.10 lower than connected vehicle fleets, although both capture the same relative fluctuations. This difference can be partly explained by tire type: connected vehicles used standard winter tires, while ViaFriction devices used stiff aircraft tires (Trelleborg T520).

Additional fleet-based validation was performed in Luleå (2021; Paper D), showing that even with a consistent offset, 80% of connected vehicle friction estimates aligned with conventional measurements within ±0.05. As noted in Paper B, conventional systems may intentionally report lower friction to maintain a safety margin for winter maintenance. However, if thresholds are set too conservatively, there is a risk of excessive maintenance, unnecessary cost, and increased environmental impact. Such offsets also pose challenges when fusing multiple sources of tire–road friction information, as inconsistent baselines can distort aggregated results.

Finally, the thesis proposes an initial aggregation and implementation framework for operational use of connected vehicle data in winter road maintenance. This includes hourly road-segment aggregation based on confidence levels and road-section summaries for performance follow-up. These methods, developed from the insights gained across all papers, provide a first foundation for large-scale monitoring of tire–road friction across the Swedish public road network.