Målet har varit att ta fram ett relevant komfortmått som beskriver cyklisters upplevda komfort vid färd, och som kan användas vid kravställning och reglering av asfaltsentreprenader. Måttet ska ersätta IRI (International Roughness Index) - som idag gäller vid kravställning på cykelvägar trots att det inte beskriver upplevelsen vid cykling särskilt väl, utan är baserat på hur en färd i bil upplevs.

Baserat på litteraturstudier och cyklistutvärderingar har vi sammanställt kunskap om betydelsen av vägytans jämnhet för cyklisters säkerhet och komfortupplevelse. Cyklistutvärderingen genomfördes i Lund där 59 rekryterade cyklister fick cykla över och göra en subjektiv bedömning på ett antal utvalda cykelbanor av varierande kvalitet – från nylagd och mycket jämn till äldre och mycket ojämn. Cyklisternas subjektiva bedömning jämfördes med objektiva vägytemätningar. Ett flertal olika mätystem användes, bland annat ett portabelt punktlasersystem monterat på mindre fordon samt skannande linjelasersystem monterat på en skåpbil eller på VTI:s cykelmätvagn.

Litteraturstudierna visar att vägytans jämnhet har betydelse för cyklisternas säkerhet och komfort vilket i sin tur kan påverka viljan att cykla. Ojämnheter i vägytan kan också ge negativa hälsoeffekter genom de vibrationer som cyklisten då utsätts för. Analyser av data från våra cyklistutvärderingar och vägytemätningar visar att det främst är ojämnheter i våglängdsområdet från 0 till 3 meter som skapar obehag hos cyklisterna. Det finns olika typer av mätmetoder för att bestämma jämnheten på en vägyta. Grovt sett kan de delas in i antingen responsmätning eller mätning av längsprofil. Den mätmetod som väljs hänger ihop med det uppföljningssystem och den mätteknik som används vilket också avgör vilka typer av mått som erhålls.

För kravställning av nylagda asfalt på cykelvägar föreslår vi ett jämnhetsmått (RMS0-3) samt ett texturmått (MPD) baserat på en millimeterprofil inmätt med lasersensor. Projektet slutförs under hösten 2024 då också gränsvärden bestäms för när en yta ska anses vara godkänd eller inte. 

En tillräckligt jämn och komfortabel cykelväg är nödvändig för ökad och säker cykling. Dagens kravställning för statliga cykelvägar är densamma som för bilvägar och baseras på komfortupplevelsen vid bilåkning, vilket inte stämmer överens med upplevelsen vid cykling. Därför behöver kraven för cykelvägar ändras så att de blir mer ändamålsenliga.  

I det här forskningsprojektet finansierat av Trafikverket, har VTI i samarbete med Ramboll RST tagitfram ett förslag till ett nytt jämnhetsmått och en mätmetod anpassad för cykelvägar som kan användas vid kontraktsuppföljning av nylagda ytor. Förslaget baseras på sammanställning av kunskap från tidigare studier, egna jämnhetsmätningar och cyklistutvärderingar i fält samt test och utvärdering i faktisk verksamhet i samband med objektsmätningar beställda av Region Skåne och Malmö stad. Föreslaget mått utgår ifrån en inmätt längsprofil med en upplösning på millimeternivå där det kvadratiska medelvärdet, RMS (Root Mean Square) beräknas över en steglängd på 25 mm och där våglängder över tre meter filtreras bort. Vi har valt att kalla måttet BCrms3 (BiCycle-rms, våglängder på max 3 meter). Måttet beskriver därmed cykelvägars jämnhet inom våglängdsområdet 0,05 till 3 meter, vilket relaterar till de ojämnheter som har störst betydelse för cyklisters åk-komfort genom de vibrationer och stötar de ger upphov till. Gränsvärdet för måttet föreslås till att initialt vara 1,0 men kan justeras efter syfte och behov. Då det finns risk att ojämnheter i det kortare ojämnhetsområdet, det vill säga brister i texturen, inte fångas till fullo av måttet, föreslår vi att det i samband med kontraktsuppföljning kompletteras med ett krav på MPD (Mean Profile Depth). MPD är ett standardiserat mått för makrotextur. Med hänsyn till cyklisters komfort och säkerhet (friktion) bör en godkänd beläggning ha krav på ett MPD mellan 0,45 och 1,3 mm. Båda måtten skulle även kunna användas för underhållsplanering, men då med andra lämpliga gränsvärden. 

A sufficiently smooth and comfortable cycle path is necessary for increased and safe cycling. The current requirements for state cycle paths are the same as for car roads and are based on the comfort experience when driving a car, which does not correspond to the experience when cycling. Therefore, the requirements for cycle paths need to be changed to be more appropriate. 

In this research project funded by the Swedish Transport Administration, VTI, in collaboration with Ramboll RST, has developed a proposal for a new evenness indicator and a measuring method adapted for cycle paths that can be used in contract performance control of newly laid surfaces. The proposal is based on literature reviews, longitudinal profile measurements and cyclist assessments, as well as testsin practice in pavement condition assessments of cycle paths commissioned by Region Skåne and the City of Malmö. The proposed evenness indicator is based on a longitudinal profile measured with laser profiler where the root mean square (RMS) is calculated over a step length of 25 mm and where wavelengths over three meters are filtered out. We have chosen to call the evenness indicator BCrms3 (BiCycle-rms, maximum wavelengths of 3 meters). The indicator thus describes the evenness of cycle paths with wavelengths ranging from 0.05 to 3 meters, relating to what is the most important for cyclists' riding comfort through the vibrations and shocks they cause. The limit value for BCrms3 is proposed to be initially 1.0 but can be adjusted according to purpose and needs. Since there is a risk that irregularities in the shorter unevenness range, i.e. imperfections in the texture, are not fully captured by the indicator, we propose that it be supplemented with a requirement for MPD (Mean Profile Depth) in connection with contract performance control. MPD is a standardized measure for macrotexture. Considering cyclists' comfort and safety (friction), an approved pavement should have a requirement for an MPD between 0.45 and 1.3 mm. Both indicators could also be used for maintenance management, but with other suitable limit values. 

Machine learning (ML) models are increasingly getting attention in predicting pavement maintenance methods to improve decision-making. This study investigates the use of ML at the municipal level to predict the street pavement condition index (PCI) rating over a 4-year span. Several supervised learning models, namely linear regression (LR), random forest (RF), and neural network (NN), were applied to the visually assessed pavement condition data of Skellefteå municipality, Sweden. Pavement distress, pavement age, and traffic data were used in several combinations to evaluate and compare the performance of the models. The RF model was based on paired variables of pavement age and pavement distress data. The results were comparatively accurate with R²=0.59 and Spearman's coefficient=0.74 for residential streets in the model testing stage. Similarly, for main, collector, and industrial (MCI) streets, the RF model, based on pavement age and traffic variables, performed best with R²=0.79 and Spearman's coefficient=0.88 during the model testing stage. The importance of input variables varies with the level of the model's sophistication and pavement performance goal; however, pavement age is the dominant variable. The prediction models can be useful in effectively managing street networks among municipalities, even those with scarce resources.

Well maintained cycleways will encourage more people to cycle, as the condition of cycleways is important for the safety, accessibility and riding comfort of cyclists. Despite that, only a few models used to describe the quality of service for cyclists take the surface condition into account. Objective measuring methods are needed to enable reliable and effective assessment of surface conditions, and measurable performance criteria related to the needs of cyclists should be developed. The purpose of this study has been to test the reliability and validity of using accelerometers in smartphones to assess the riding comfort on cycleways. A smartphone application converting three-dimensional accelerometer measurements into a single indicator for cycleways has been used to assess road surfaces in two field studies, in Sweden and Norway, respectively. Both studies assessed test sections of varying quality. To relate the measurements to subjective riding comfort assessments by cyclists, recruited cyclists collected quantitative data using the app, whilst also rating their perceived riding comfort by completing a survey. Measurements were also related to standard road surface condition indicators, generated from a road surface tester equipped with 19 laser sensors: international roughness index (IRI), mega- and macrotexture. The results show that it is possible to describe the unevenness of a cycleway using the technology present in smartphones. A software application can be used to collect and analyse data from the acceleration sensors in the phone, which can then be used to describe the riding comfort of cyclists. It is mainly the unevenness in the 50–1000 mm size-range that create the greatest discomfort for cyclists, and intermittent vibrations are perceived as more uncomfortable than more evenly distributed vibrations. Therefore, IRI is not a relevant measurement for describing the riding comfort of cyclists.

Management of street networks is different from state roads due to its multifunctional role in society. This requires timely maintenance of the street network to improve socioeconomic development. Municipalities in Sweden are responsible for the management of about 42000 km of street network in their jurisdiction. Maintenance budget and resources vary from municipality to municipality depending on their network size, geographical location, and population density. 

A questionnaire was sent to all 290 municipalities across the country to investigate the street network and pavement management practices at the municipal level. An in-depth interview of 14 municipalities took place afterwards. A total of 51% of responses were officially received to highlight the frequently and infrequently occurring pavement distresses and their causes. Furthermore, the study highlights the maintenance approaches and allocation of budget to manage the street network. Lastly, the study highlights the factors which need to be addressed to improve street network management.   

Visual assessment of pavements is common and the use of pavement management system (PMS) is not only limited but also lacks pavement performance prediction models. Common pavement distresses are excessive formation of potholes, surface unevenness and alligator distress among the municipalities. Frequent causes of distress include the ageing of the street network, heavy vehicles, patching and high traffic flow. Furthermore, cold climate and population density are important factors in the degradation process. Allocation of maintenance and reconstruction budget is relatively high in municipalities located in the north. Densely populated municipalities have higher taxpayers’ contributions to maintenance and reconstruction. Other major issues are limited resources and maintenance of budget, which adversely affect the maintenance backlog and the choice of maintenance alternatives, ultimately the decision-making. This restricts the use of preventive maintenance among the municipalities. 

Municipalities are required to improve the frequency and quality of pavement data collection, enhance the sophistication of PMS by using pavement deterioration models, and increase the maintenance budget and resources. An insight into the capabilities of municipalities would help in long-term strategic planning and effective utilisation of the maintenance budget to improve the street network across the country.  

Street pavements are subject to various types of distress which necessitate a cost-effective management approach. This paper presents the outcomes of a survey focusing on street pavement maintenance and the utilization of machine learning (ML) pavement performance models on a 320 km municipal street network in Skellefteå municipality, Sweden. The findings reveal that the most common types of distress on Swedish streets include potholes, surface unevenness and alligator cracking, while prevalent causes of these distress are pavement ageing, heavy traffic and pavement patches. The windshield method of assessment of street pavement is prevalent, but the use of pavement management systems (PMS) is limited and pavement performance models are rarely employed. The case study reveals that Random Forest (RF) models developed for non-residential streets perform better than residential street models. RF models based on the variables age (A) and traffic (T) emerged as the best models, with 84% prediction accuracy. However, the R-squared value for the RF model applied to residential streets was 0.53, slightly surpassing the values for all models applied to non-residential streets (0.31, 0.50, 0.49). Further evaluation of models is suggested by using additional data.  

The municipal street network acts as a multifunctional asset by providing people, vehicles and public services with a well-functioning infrastructure. To keep it in good condition, optimal maintenance measures are required which would result in an efficient use of taxpayers' money. This paper investigates the street network deterioration processes and the management practices that the municipal administrations have applied in Sweden. The study is based on a survey with Swedish municipalities using questionnaires and complementary interviews. The answers provide insight into a wide range of common pavement distresses and deterioration factors, along with pavement management practices. The study identifies that potholes, surface unevenness and alligator cracking are the most cited challenges, while pavement ageing, heavy traffic and patches are the most noted causes. Similarly, the cold climate and population density are influential factors in pavement deterioration. Allocation of the maintenance and rehabilitation and reconstruction budget is higher in the northern part of the country as well as in densely populated municipalities. Condition data collection and use of commercial Pavement Management Systems (PMS) are limited. Addressing the challenges effectively may be possible through the enhancement of the budget, feasible/clear guidelines from municipal councils/politicians, and reducing the gap between street network administrations and utility service providers.

Pavement surface characteristics have been gaining relevance with the changing mobility paradigm as they greatly contribute to the degree of safety, comfort, environmental quality and long lasting pavements. The surveillance methods of pavement surface characteristics have evolved from manual to semi- and automated methods and also to more cost-effective methods. Automated condition surveys provide objective high-quality data. At network level, pavement distresses, friction, evenness and texture are currently surveilled. More rarely surveyed and related to sustainability demands are tyre-road noise and rolling resistance. They rely on dedicated pavement data collection vehicles or trailers coupled to vehicles, equipped with high-speed digital cameras, laser systems, ultrasonic sensors, accelerometers, microphones, etc. As a complementary alternative to these methods, unmanned aerial vehicles equipped with cameras as well as smartphone-based data collection methods are arising. In a design framework, the laboratory methods to survey pavement surface characteristics often use the same technology as in the field tests adapted to a much smaller scale and lower testing speed. In this chapter, the latest advances in laboratory and field tests, and surveillance methods of pavement surface characteristics are described and their performance is analyzed.

Ett sätt för Trafikverket att följa upp effekterna av underhållet på de statliga vägarna är genom att använda sig av indikatorer. De indikatorer som beskrivs i denna rapport benämns som andra ordningens indikatorer och byggs upp av underliggande data. Indikatorerna som vi valt att arbeta med ska på ett allsidigt och transparent sätt beskriva hur Trafikverket uppfyller leveranskvaliteten för punktlighet, kapacitet, robusthet, användarbarhet, säkerhet samt miljö och hälsa.

 I denna förstudie sammanfattar vi en kunskapsöversikt för valda indikatorer och förslag på hur de kan implementeras i Trafikverkets pavement management system. De indikatorer som vi arbetar med är Säkerhet, Komfort, Strukturellt tillstånd, Rullmotstånd, Buller, Luftkvalitet, Godsskador, Ytskador, Fordonsslitage och slutligen en indikator för Hälsa. Det finns många aspekter att ta hänsyn till då en indikator introduceras. Den absolut viktigaste är, bortsett från att den är validerad, att den får acceptans och används. Det finns många sätt att använda en indikator och att enbart titta på absoluta värden är svårt för en sammansatt indikator. Relativa skillnader vid årliga uppföljningar är att föredra för att avgöra om strategier får avsedd effekt.

One way for the Swedish Transport Administration (STA) to follow up the effects of maintenance on state roads is by using indicators. The indicators described in this report are defined as second-order indicators and are built up using underlying data. The indicators describe, in a comprehensive and transparent way, how the STA meets delivery quality parameters for punctuality, capacity, robustness, usability, safety, and environment and health. 

In this feasibility study, a knowledge overview of the selected indicators is presented. Suggestions are also given on how the indicators can be implemented in the STA's pavement management system. The indicators studied were Safety, Comfort, Structural Condition, Rolling Resistance, Noise, Air Quality, Goods Damage, Surface Damage, Vehicle Wear, and Health Risk. Many aspects must be considered when introducing an indicator. Along with validation, it is extremely important that the indicator will be accepted and used. There are many ways to use an indicator, however, it is not ideal to only consider absolute values for a composite indicator. Relative differences in annual follow-ups are preferable to determine whether strategies have the intended effect.

Pågående digitalisering av transportsystemet, inklusive automatiserade fordon, innebär ett paradigmskifte. Utvecklingen av fordon med avancerat förarstöd, automatiserade funktioner och självkörande fordon är nu en självklar användning i transportsystemet. Tack vare introduktionen av nya sensorer i fordonen och uppkoppling finns ett komplement som möjliggör automationsfunktioner som kan stödja den mänskliga föraren med köruppgiften under hela eller delar av resan. Å andra sidan bör det stå klart att dagens transportsystem och infrastruktur har utformats för att manövrera fordon med hjälp av mänskliga förare och deras tillhörande begränsningar och möjligheter.

Projektet syftar till, att ur ett väghållarperspektiv med fokus på det statliga vägnätet, redogöra för det aktuella kunskapsläget avseende befintliga och möjliga anpassningar av digital och fysisk infrastruktur för att ge stöd till fordon med automatiserade funktioner. Projektet syftar även till långsiktig kunskapsuppbyggnad.

Underlag har samlats in via litteraturstudier och framför allt hämtats från studier av pågående och utförda relevanta projekt såväl nationella som internationella. Vidare har mycket kunskap hämtats in genom författarnas nätverk och deras deltagande i relevanta internationella och nationella grupper. En workshop med berörda kommersiella aktörer har också genomförts inom projektet.

The ongoing digitalization of the transport system, including automated vehicles, entails a paradigm shift. The development of vehicles with advanced driver support, automated functions, and self[1]driving vehicles is now an obvious use in the transport system. Thanks to the introduction of new sensors in the vehicles and connectivity, there is a complement that allows automation functions that can support the human driver with the driving task in all or parts of the journey. On the other hand, it should be clear that today's transport system and infrastructure have been designed to maneuver vehicles with the help of human drivers and their associated limitations and capabilities. 

The project aims, from a road maintenance perspective with a focus on the state road network, to describe the current state of knowledge regarding existing and possible adaptations of the digital and physical infrastructure to provide support for vehicles with automated functions. The project also aims at long-term knowledge building. 

Data has been collected via literature studies and, above all, obtained from studies of ongoing and executed relevant projects, both national and international. Furthermore, much knowledge has been gathered through the authors' networks and their participation in relevant international and national groups. A workshop with relevant commercial actors has also been conducted within the project.