Enhancing the Assessment of Winter Road Maintenance Levels with Respect to Road Safety in Lithuania

Abstract

Winter road maintenance levels are currently determined based on criteria with weights that have not yet been scientifically validated in Lithuania. This study aims to address this gap by analyzing global practices related to winter road maintenance levels and their determination methods. A survey of experts was conducted to expand and refine the list of criteria that significantly influence road winter maintenance decisions. Based on expert consensus, the weights for these criteria were calculated. Using this indicator system, an expert assessment of the road winter maintenance levels was performed for selected road sections of the road network. This study proposes a scientifically grounded methodology to determine winter road maintenance levels, which can be applied to all national road networks. Furthermore, the methodology incorporates elements of road safety, emphasizing the need for winter road maintenance not only to improve infrastructure but also to reduce accident risks. This study revealed that the proposed methodology, which was validated through a Lithuanian case study in the Raseiniai Northern District, effectively assesses winter road maintenance levels by combining road safety factors with a multi-criteria evaluation. This thorough approach not only increases road safety but also improves traffic flow, showcasing its potential for wider application in national road networks.

1. Introduction

According to the World Road Association [1], road infrastructure asset management employs systematic approaches to effectively maintain, upgrade, and operate infrastructure. It includes areas such as pavement management systems, routine maintenance operations, continuous condition monitoring, and strategic planning. To improve the efficiency of road infrastructure asset management, it is crucial to improve the process, ensuring their coordination. Efficient road management requires continuous data collection on road infrastructure parameters and their impact on external factors, which should then be incorporated into a long-term database [2,3,4]. This database could serve as the foundation for developing a road maintenance level evaluation model, which is essential for defining the order and requirements of both winter and summer maintenance activities. Based on the determined maintenance level, the extent and type of measures necessary to maintain roads year-round can be regulated.

In recent years, there has been a noticeable trend in the shift in road network infrastructure asset management from the traditional condition-based to a service-based approach [5]. The condition-based approach focuses on assessing the physical condition of infrastructure assets, with maintenance and interventions triggered by observed deterioration or traffic conditions on roads and related infrastructure. However, this method tends to be reactive, relying on regular inspections and condition assessments to determine the need for repairs or maintenance. In contrast, the service-based approach focuses on fulfilling the needs of road users, such as ensuring road safety, travel reliability, comfort, minimal environmental impact, and more. Some national road authorities, such as those in the Netherlands [6], have adopted infrastructure management and maintenance decisions based on performance indicators that define the level of service for road users. These indicators include reliability, accessibility, continuity, road safety, security, health, environmental protection, economics, and politics. Performance-based contracts incentivize contractors to maintain roads to a high standard while managing risks associated with fluctuating conditions or requirements.

The primary winter costs of daily road maintenance in northern countries are related to the upkeep of road surfaces (snow removal and the application of chemical and friction materials to reduce slipperiness). The most important quality measure for winter maintenance is time and responsiveness, referring to how quickly hazardous driving conditions are identified and how promptly appropriate actions (measures) are implemented. The more effective these measures are, the lower the costs incurred by society, particularly by reducing accident rates and delays [7,8,9]. Therefore, it is essential to continuously explore ways to optimize available road winter maintenance resources by using the latest technologies and the most effective materials that reduce road slipperiness. This approach helps minimize the duration of unfavorable and dangerous (icy and snowy) road conditions and can potentially improve road safety. Modern technologies and materials today can effectively prevent road surface icing when anti-icing strategies are applied. However, to implement these strategies successfully, reliable and responsive decision support systems are essential.

Globally, road maintenance is typically managed through one of three primary approaches:

• State-Managed Approach: Countries such as Latvia and Lithuania centralize road maintenance, entrusting all responsibilities to state-owned enterprises, ensuring government oversight and consistency;
• Regional Privatization Approach: Maintenance tasks are divided geographically (e.g., zones and regions), and contracts are awarded through public tenders, fostering competition among private companies;
• Hybrid Approach: Adopted in nations such as Sweden and Estonia, this model involves both public and private sectors, with state-owned and private companies competing in tenders to provide road maintenance services.

Based on a review of national and international documents (i.e., Latvian Road Maintenance Regulations, 2021; Estonian Ministry of Economic Affairs and Infrastructure, 2015; Lithuanian Road Maintenance Standards, 2019; PIARC, 2010) [10,11,12,13], these approaches highlight diverse ways to balance efficiency, competition, and public oversight.

Table 1. Road winter maintenance levels in selected countries worldwide.

Road Maintenance Levels	Estonia	Latvia	Austria	Germany	USA	Slovenia	Switzerland	Norway	Sweden	Finland
Highest	3+ 3	A	A	1	IA	1	A	1	1	Ise
					IB					Is
Average	2	B	B	2	II	2	B	2	2	Ib
		C	C			3		3	3	Ic
Lowest	1	D	D	3	IIIA	4	C	4	4	II
		E			IIIB	5			5	III
No maintenance	-	-	-	-	IV	6	D	-	-	-
No of levels	3	5	4	3	6	6	4	4	5	6

provides an overview of winter road maintenance levels in several countries, illustrating how winter road conditions are managed. Many developed countries classify winter road maintenance into different levels according to traffic intensity and road type (e.g., highways and regional roads).

The road winter maintenance levels vary significantly across countries, categorized into high, medium, and low maintenance, along with instances where no maintenance is performed. Winter maintenance activities often include snow removal, salting for prevention, and response times after snowfall. Countries such as Latvia, Estonia, Austria, and Germany align maintenance priorities with road importance and traffic intensity. Others, such as the USA and Slovenia, add distinctions between highways and local roads. The number of winter maintenance levels ranges from 3 to 6, reflecting differences in climate, resources, and infrastructure priorities. The highest maintenance level (e.g., A, 1, and I) typically applies to major highways, with snow removal and de-icing completed within a few hours after snowfall. Medium-level roads (e.g., B, 2, and II) usually have snow cleared within 12–24 h, while low-priority roads (e.g., C, 3, and III) may face delays of over 24 h. Countries like Estonia, Latvia, and Austria have structured systems that balance urgency and regional needs. In contrast, the USA lacks a national standard, leaving decisions to state and local authorities, which can lead to regional disparities in winter maintenance practices.

In most cases worldwide, the budget allocated for road winter maintenance is limited and often insufficient to ensure the highest level of maintenance across all roads of national or local significance. Consequently, it is increasingly recognized that road winter maintenance planning must involve prioritizing and ranking roads to determine which should receive higher maintenance levels and which should be assigned lower or minimal maintenance standards.

Road winter maintenance criteria worldwide often depend on factors such as traffic intensity, road category, and environmental considerations, reflecting limited budgets and the need for prioritization [14,15,16,17,18,19]. Countries such as Latvia and Estonia use multi-level systems, categorizing roads by traffic intensity, functional importance, and seasonal demands, to allocate resources effectively. For example, Latvia applies five maintenance levels (A–E) based on traffic intensity, while Estonia uses a three-level system that considers both traffic and road type (please note that the highest maintenance level is further divided into two sub-levels: 3+ and 3). Austria and Slovenia incorporate additional parameters, such as public transport routes, tourist significance, climate, and geographic conditions, to determine maintenance standards. In the USA, road maintenance is influenced by funding availability, environmental requirements, safety, and service level expectations, ensuring that maintenance aligns with road management goals and user needs.

At the network level, a comprehensive evaluation of various criteria is essential, including engineering, economic, social, and environmental impacts. A multi-criteria analysis methodology for road maintenance levels is necessary to optimize the allocation of resources for road network maintenance [20]. Using a multi-criteria analysis approach, it is possible to determine the optimal winter maintenance levels based on criteria such as road category, road function, traffic intensity and composition, permissible driving speed, pavement type, public transport availability, accident rates, road safety indicators, societal needs, geographic conditions, and climatic conditions.

In Lithuania, state roads are managed by the state-owned company Via Lietuva, which classifies winter maintenance requirements into five levels, ranging from Level I (highest) to Level V (lowest). These levels are determined by factors such as average annual daily traffic, average annual daily truck traffic, maximum allowable speed, and road function. However, the weighting of these criteria within the methodology lacks scientific validation. Drawing on the best global practices, the methodology could be enhanced by incorporating additional criteria, such as accident rates, pavement type, and public transport usage, with scientifically substantiated weights assigned to these parameters.

In an international context, improving road maintenance methodologies is essential for enhancing road safety and reducing accidents, especially as traffic intensity continues to rise. Countries that implement scientifically validated methods can allocate resources more efficiently, ensuring that investments in road maintenance yield measurable safety benefits. Moreover, the dissemination of effective road management practices fosters greater collaboration and knowledge sharing, contributing to global advancements in road safety.

Based on the information provided, the research problem can be formulated as follows: Winter road maintenance levels are determined based on a limited set of criteria with weights that are not scientifically substantiated. Currently, the winter road maintenance level is based on specific weighted scores in Lithuania, including average annual daily traffic, average annual daily truck traffic, road function, and maximum allowed speed. However, the assigned weights (40%, 30%, 15%, and 15%, respectively) lack a scientifically validated basis, highlighting the need for a more comprehensive and justified methodology.

The research focuses on developing a methodology for determining the maintenance levels of state roads. This study looks at this methodology from a road safety angle, looking at national, regional, and local roads while also adding to the overall understanding of winter road maintenance. By addressing the gaps in current Lithuanian methods, it brings in globally recognized best practices to make better operability and use of resources and improve safety across various types of roads. To validate the proposed methodology, a case study was conducted, which demonstrated its practical applicability and effectiveness in real-world conditions. The findings highlight the potential for broader implementation and refinement based on empirical data. This research aims to evaluate this methodology from a safety perspective (road safety and public transport reliability), focusing on national, regional, and local roads while contributing to a broader knowledge of winter road maintenance. Overall, this study contributes to three key areas:

-

Refinement of criteria: The study refines and expands the list of criteria that significantly influence winter road maintenance decisions through expert surveys.

-

Road Safety Insights: This metric provides valuable information on the relationship between winter road maintenance levels and road safety.

-

Methodological Framework: This study presents a methodological framework to assess winter road maintenance levels.

2. Materials and Methods

In this study, the scientific investigation will be based on data collected through expert evaluations. As noted by researchers [21,22], the essence of the expert assessment method lies in the rational and organized examination of a problem, which includes the evaluation of opinions and the analysis of processed results. The aggregated opinion of the expert group is presented as the solution to the problem (that is, the outcome of the assessment). Once the expert group survey is completed, the collected data are processed. The initial information for processing consists of numerical data reflecting the experts’ preferences, along with detailed justifications for these preferences. The goal of the processing is to obtain aggregated data and extract new information embedded within the expert evaluations in latent form. Based on the results of the processing, a solution to the problem is formulated.

Given the aim of the study, the participants are individuals directly involved in the maintenance of state-level roads or those overseeing and managing such activities. They hold senior positions and possess higher education qualifications. Following these criteria, experts should be selected to participate in the study. According to the research, when the participants’ knowledge is relatively similar and their professional experience in the relevant field is at least five years, a sample size of 5 to 7 respondents is typically sufficient to ensure the reliability of study results [23]. Increasing the number of respondents beyond this range is unlikely to affect the consistency of the responses significantly.

The data from the expert survey can be recorded in a pre-prepared questionnaire. Along with the questionnaire, an explanatory note was provided to the experts. This note outlined the objectives and tasks of the evaluation, provided the necessary information, and included instructions for completing the questionnaire.

The purpose of the questions is to gain a deeper understanding of the phenomenon under study and to collect detailed information about the nature of the processes. The following types of surveys are commonly distinguished: questionnaire surveys, interviews, postal surveys, telephone surveys, surveys conducted through mass communication channels, and others. Among these, questionnaire surveys are widely used in international research practice due to their efficiency and ability to collect standardized data [24].

Currently applied methods for determining the weights of multi-criteria evaluation factors are often based on expert opinions. This remains a subjective process, meaning that the results are influenced by numerous factors, such as the qualifications of the experts, the range of the evaluation scale, and other contextual elements. The accuracy of determining factor weights is particularly dependent on the number of factors and the approach used for weight determination. These two conditions are interconnected.

The most straightforward method is direct evaluation, in which experts instantly assign weights to factors as fractions of the whole. This method tends to provide the most reliable results when the number of factors is small. It is simple, easy to understand, and convenient to apply. However, as the number of factors increases, the approach becomes increasingly problematic. This occurs because it becomes more difficult for experts to determine the correct interrelationships between a growing number of factors within the context of the phenomenon being studied. Consequently, discrepancies in expert opinions tend to increase, often exceeding acceptable thresholds, making the results of expert surveys unsuitable for further calculations [25].

Multi-criteria decision analysis (MCDA) methodologies often include the Analytic Hierarchy Process (AHP), one of the simplest yet widely used techniques. AHP systematically transforms the evaluation of competing objectives into a series of pairwise comparisons, making it particularly effective for addressing multi-objective optimization problems under budget constraints. Other methods, such as COPRAS, TOPSIS, VIKOR, and Kendall’s Concordance, are also commonly used, each offering distinct advantages in decision-making for infrastructure management. For further research, Kendall’s Concordance and COPRAS are prioritized due to their applicability in road maintenance and investment planning. Kendall’s Concordance and COPRAS were chosen for their suitability because the methods are well suited to gauge expert consensus and prioritize road maintenance tasks. These methods make it easy to evaluate multiple criteria without the hassle of complicated pairwise comparisons or heavy data normalization. Plus, they fit right in with our goal of adjusting resource allocation while being flexible enough to handle different data availability and expert insights. These techniques help optimize resource allocation and improve decision-making processes in the context of infrastructure development [26,27,28].

2.1. Kendall’s Concordance

The results of an expert survey should be assessed to determine the consistency of the experts’ opinions. The correlation coefficient can be calculated to evaluate whether the opinions of two experts are aligned. However, when the number of experts is larger, the consistency of opinions is measured using the concordance coefficient $W$[29,30].

First, the sum of the ranks for each evaluation criterion is determined using the following equation:

$$P_j={\sum }_{k=1}^r{P}_{jk}$$

(1)

where is $P_{jk}$ is the score assigned by the $k_{th}$ expert to the $j_{th}$ evaluation criterion.

The average value of the evaluation criterion is calculated using the following equation:

$$\overline{P_j}={\displaystyle \frac{\sum _{k=1}^r{P}_{jk}}{r}}$$

(2)

where $r$ is the number of experts.

The sum of the squared deviations of each evaluation criterion is calculated using the following equation:

$$S={\sum }_{j=1}^m{(P_j-\overline{P})}^2$$

(3)

where $m$ is the number of evaluation criteria.

The average sum of all evaluation criteria’s ranks is calculated using the following equation:

$$\overline{P}={\displaystyle \frac{{\sum }_{k=1}^r{\sum }_{j=1}^m{P}_{jk}}{m}}$$

(4)

The statistical distribution ${\chi }^2$ is calculated using the concordance coefficient $W$ based on the following equation:

$${\chi }^2=W·r·(m-1)={\displaystyle \frac{12S}{r·m·(m+1)}}$$

(5)

If the calculated ${\chi }^2$ value from Equation (5) is greater than the critical value ${\chi }_{crit}^2$ obtained from the ${\chi }^2$ distribution table for a given degree of freedom $\nu =m-1$ at a significance level of $\alpha =0.05$ (commonly used in practice), the experts’ opinions are considered consistent.

To calculate the significance of evaluation criteria, the highest numerical expression is considered the most important [31]. The significance of each evaluation criterion is calculated using the following equation:

$${\overline{q}}_j={\displaystyle \frac{{\overline{P}}_j}{{\sum }_{i=1}^m{P}_j}}$$

(6)

The sum of the calculated significance values for the evaluation criteria equals 1. The significance value indicates how many times one evaluation criterion is more important than another. Based on these values, further selection of evaluation criteria can be performed by choosing only those with the highest significance in each predefined group.

2.2. COPRAS Method

Multi-criteria decision-making models aim to determine a compromise solution, allowing for multiple modifications of the main model or method.

These models are used to solve problems with a finite number of decision alternatives, applying discrete mathematics in their development. In this case, the goal is not to identify the optimal solution but rather to establish a priority ranking of the comparative alternatives. The analysis focuses on comparing alternatives to determine which one is better based on the criteria considered, aiming to achieve an interaction between the available decision alternatives.

Such models are frequently applied in practice. Several methods have been developed, but assessing their quality and determining the best one remains challenging [23,27,32,33]. In both cases, the final decision is made by a human decision-maker who selects one of the available alternatives.

Zavadskas and Kaklauskas [34] developed the multi-criteria complex proportional assessment method (COPRAS) to align different objectives and establish a priority ranking of alternatives. This method determines the priority order of alternatives and assesses the degree to which one alternative is superior to another [35].

The essence of the methodology is as follows:

$$S_i={\sum }_{j=1}^n{b}_{ij}$$

(7)

where

• $b_{ij}$—the score given by the $j_{th}$ expert for the $i_{th}$ criterion;
• $S_i—$ the sum of scores given by all experts for the $i_{th}$ criterion.

The average sum of ranks is calculated according to the following equation:

$$S^{*}={\displaystyle \frac{\sum _{i=1}^m{S}_i}{m}}$$

(8)

where $m$ is the number of evaluation criteria.

Deviation from the average sum of ranks:

$$∆S_i=S_i-S^{*}$$

(9)

where

• $∆S_i$—the deviation of the $i_{th}$ criterion’s sum of scores from the average sum of ranks.

The significance of each criterion is given through the following equation:

$${\overline{q}}_i={\displaystyle \frac{S_i}{{\sum }_{i=1}^m{S}_i}}$$

(10)

2.3. Criteria Selection

To rationally determine the levels of winter road maintenance, it would be appropriate to incorporate additional criteria into the winter road safety process, taking into account road safety, traffic composition, and pavement type parameters. In addition, it is essential to establish and scientifically justify the weight coefficients of the criteria.

This study presents seven criteria, four of which are commonly used, while the remaining three, although rarely applied and more complex, are introduced within the scope of this research. The appropriateness of these criteria for determining the winter road maintenance levels of state roads will be assessed based on expert opinions. The scores shown in the Lithuanian case study are examples and may vary depending on local conditions and the calculation methods used. Various approaches and criteria can produce different outcomes, so it is important to understand these scores in light of the specific methods and assumptions that were applied in this research.

The criteria have been structured and presented for expert evaluation. To define the proposed criteria, an overview and a detailed explanation of each criterion were provided:

(1) 

AADT:

Average Annual Daily Traffic: This is one of the key indicators reflecting the intensity of road usage (see

Table 2. Weighted scores for AADT based on Lithuanian case study.

Average Annual Daily Traffic, veh/day	Weighted Score
>10,000	10
10,000–5000	8
5000–2500	7
2500–1000	6
1000–750	5
750–500	4
500–250	3
250–100	2
<100	1

). This criterion is commonly used in current winter road maintenance methodologies and is also included in the current Lithuanian methodology.

(2) 

AADTT:

Average Annual Daily Truck Traffic: This indicator represents the number of heavy vehicles traveling on the road (see

Table 3. Weighted scores for AADTT based on a Lithuanian case study.

Average Annual Daily Truck Traffic, veh/day	Weighted Score
>2000	10
2000–1500	9
1500–1000	8
1000–750	7
750–500	6
500–250	5
250–100	4
100–50	2
<50	1

). It provides insights into road load levels and traffic flow speeds. This criterion is also quite common in current winter road maintenance methodologies and is included in the current Lithuanian methodology.

(3) 

Road function (RF):

This criterion is also applied in some of the current methodologies (see

Table 4. Weighted scores for various Road functions based on a Lithuanian case study.

Road Function	Weighted Score
Transit Roads	10
Transit Connecting Roads	7
Connecting Roads	5
Distributive	3
Access Roads	1

) and are included in current Lithuanian methodology.

Transit Roads—Roads that coincide with designated European transport corridors, serving long-distance connections and transit functions at the EU scale.

Transit Connecting Roads—Other roads classified under the European Union (EU) “E” road network, linking neighboring EU capitals or important economic, cultural, and recreational areas.

Connecting Roads—Roads crucial for long-distance travel within the country. These typically connect the national capital with regional centers or key economic, cultural, and recreational sites of national significance. They may also link regional centers to neighboring countries, provided that such a connection is not of European regional importance.

Distributive-Access Roads—Roads that connect regional and district centers to one another or to regionally significant economic, cultural, and recreational areas. Access roads provide access to the national road network from residential, agricultural, cultural, or recreational areas.

(4) 

Maximum allowed speed (V_max):

Driving speed is a crucial factor directly influencing the risk of traffic accidents and their consequences (see

Table 5. Weighted scores for Maximum allowed speed based on a Lithuanian case study.

Maximum Allowed Speed, km/h	Weighted Score
>90	10
≤90	5

). Therefore, road maintenance should be prioritized in very high-speed sections. This criterion is also applied in certain existing methodologies and is included in the current Lithuanian methodology.

(5) 

Risk of traffic accidents

The expected risk of traffic accidents represents the likelihood that accidents occur on a specific road section in a given time frame (see

Table 6. Weighted scores for Risk of traffic accidents based on a Lithuanian case study.

Risk of Traffic Accidents per Road km	Weighted Score
>0.08	10
≤0.08	8
≤0.06	6
≤0.04	4
≤0.02	2

). It is determined using national data derived from safety analyses of state roads. Since accident predictions are calculated separately for road sections and intersections, a derived indicator can be used to assess risk based on the frequency of accidents per km of road per year.

This criterion is quite rare and has been proposed as an addition to existing methodologies.

(6) 

Public transport traffic (PTT)

It is crucial to ensure the reliable and uninterrupted movement of public transport on state roads. Due to low maintenance levels, interruptions in public transport services may occur, leading to significant losses, such as people not being able to reach their workplaces, schools, or healthcare facilities. The weighted score should be determined using information about a number of local and long-distance routes (see

Table 7. Weighted scores for Public transport traffic based on a Lithuanian case study.

Public Transport Traffic, Number of Routes	Weighted Score
>20	10
≤20	8
≤15	6
≤10	4
≤5	2
No routes	0

). The number of routes assigned to a specific state road should be evaluated. This criterion is quite rare, and it is proposed as an addition to the methodology.

(7) 

Pavement type (PT)

When determining the level of road maintenance, it is essential to consider the type of pavement, as this influences the maintenance activities and the methods applied. Different types of pavement require different maintenance approaches, ultimately affecting the quality and cost-effectiveness of the service.

For example, asphalt concrete pavement typically requires more intensive and specific maintenance techniques, thus receiving a higher weight. On the other hand, unbound mixture pavements, which generally require less maintenance effort, are assigned a lower score (see

Table 8. Weighted scores for Pavement type based on a Lithuanian case study.

Pavement Type	Weighted Score
Asphalt concrete	10
Unbound mixture pavements	0

).

This criterion is quite rare and is proposed as an addition to the methodology.

2.4. Methodological Framework

The proposed methodology for determining winter road maintenance levels comprises multiple sequential stages for certain road stretches:

Data collection and update: In the initial stage, all relevant data required for determining the road maintenance level must be collected and updated to ensure accuracy and completeness. This includes data on AADT, AADTT, RF, V_MAX, RTA, PTT, and PT.

Data systematization and evaluation: the second stage involves structuring the collected data and assigning criterion scores based on the evaluation scales (i.e., equal intervals, quantile, standard deviation, etc.).

Calculation of winter road maintenance levels (WRML): in the third stage, a cumulative score, based on a developed formula, is computed for each road or road section by integrating the assigned criterion scores.

Assignment of the state road section maintenance level: At this stage, the appropriate winter maintenance level is assigned to each state road or its section. This classification helps prioritize which roads require higher maintenance efforts, ensuring efficient allocation of resources and improved road safety during winter conditions.

Approval and implementation of winter road maintenance: The compiled road maintenance levels are validated, formally approved, and subsequently implemented by the road owner. Geographic Information Systems (GISs) may be utilized to facilitate the implementation of this methodology, enhancing data analysis, visualization, and decision-making processes. Additionally, road stretches can be merged to ensure consistency in maintenance level assignments to the road or part of it.

A schematic representation of the methodological framework is provided in Figure 1.

3. Results of Evaluation

Twelve experts from various related fields were invited to answer the questions. However, one expert did not provide answers in the second stage, possibly due to time constraints or other commitments. This is quite natural in such processes, as availability and participation can vary between participants. The initial expert group consisted of twelve specialists and researchers in this field, all of whom have higher education and more than ten years of experience in road maintenance and road safety. Experts from all relevant Lithuanian institutions and companies have been invited to answer the questions, including two experts from the Ministry of Communications of the Republic of Lithuania, two experts from the state-owned company Via Lietuva, which is primarily responsible for road management, four experts from VilniusTech University directly involved in road maintenance and safety research, and four experts from the state-owned company Kelių priežiūra, which focuses on the direct maintenance of state roads of national importance, including highways, national, and regional roads.

The expert surveys were conducted in two stages. The first stage aimed to identify which criteria, in the opinion of experts, are truly significant in determining the level of road maintenance. In the second stage, experts were asked to rank the selected criteria by importance and assign them scores.

3.1. The First Stage of the Expert Survey: Initial Selection of Criteria

In the first stage of the survey, twelve experts were presented with the criteria for determining the levels of winter road maintenance of roads of national importance. Experts were asked to mark the criteria they considered important in determining the level of road maintenance. A criterion was considered important if at least 50% of experts identified it as relevant to determining the level of winter road maintenance.

The analysis of expert evaluations revealed that six criteria could influence the determination of road maintenance levels the most. According to experts, factors affecting road maintenance levels include AADT (100% of respondents answered positively), AADTT and road function (both with 75% positive responses), maximum allowed speed V_max (67% positive responses), public transport traffic (58% positive responses), and accident risk (50% positive responses). Experts also indicated that pavement type (33% positive responses) does not significantly influence the determination of road maintenance levels. A graphical representation of the responses is provided in Figure 2.

3.2. The Second Stage of the Expert Survey: Evaluation of Criteria

3.2.1. Ranking of Criteria

The second stage of the expert survey consists of two parts. In the first part, experts were asked to rank the six criteria selected in the first stage by assigning scores. The most important criterion was given a six-point score, while the least important criterion was assigned a one-point score. The assigned values cannot be repeated. In the second stage, eleven experts provided their opinions. The results of the ranking criteria are presented in

Table 9. Expert ranking of state road maintenance criteria using a six-point system.

No.	Criteria	Experts											Sum
		E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11
1	AADT	6	6	6	5	6	6	5	5	6	6	6	63
2	AADTT	2	1	5	6	4	5	1	2	2	4	5	37
3	Road function	5	5	4	3	5	3	6	6	4	5	2	48
4	Maximum allowed speed (Vmax)	1	3	2	4	2	4	3	3	5	3	4	34
5	Risk of traffic accidents	3	2	3	2	3	2	2	4	3	2	3	29
6	Public transport traffic	4	4	1	1	1	1	4	1	1	1	1	20

.

Data analysis revealed that experts assigned the highest ranking to AADT, followed by road function as the second most important criterion. AADTT was awarded third place, the fourth to maximum allowed speed, the fifth to risk of traffic accidents, and the lowest ranking was assigned to public transport traffic.

In the second part, experts were asked to assign 100 points among individual criteria. They were required to indicate how many points out of 100 should be assigned to each criterion when determining the level of maintenance of state roads. The total allocated percentage could not exceed 100 points. The results of the second part of the second stage are presented in

Table 10. Expert-assigned point distribution for state road maintenance criteria.

No.	Criteria	Experts											Sum
		E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11
1	AADT	30	33	29	10	30	40	24	20	30	45	40	331
2	AADTT	12	7	24	30	20	25	5	10	10	15	25	183
3	Road function	25	27	19	10	20	10	26	25	15	20	5	202
4	Maximum allowed speed (Vmax)	5	10	10	20	10	10	15	10	20	10	15	135
5	Risk of traffic accidents	10	8	13	15	15	10	10	20	15	5	10	131
6	Public transport traffic	18	15	5	15	5	5	20	15	10	5	5	118

.

Data analysis revealed that experts assigned the highest number of points to AADT, followed by road functional purpose in second place, AADTT in third, maximum allowed speed in fourth, traffic accident risk in fifth, and the lowest number of points to public transport traffic.

3.2.2. Consistency of Expert Opinions

The consistency of expert opinions is assessed using Kendall’s Concordance method based on the rankings assigned to criteria in the second survey stage.

In the first part, experts were asked to rank six criteria based on their perceived importance. Kendall’s W calculated for Table 9 is 0.541, indicating a moderate level of agreement between experts. The Chi-square test result (${\mathsf{\chi }}^2$ = 29.75) exceeds the critical value (${\mathsf{\chi }}_{crit}^2$(0.05;5) = 11.07), meaning the level of agreement is statistically significant. From the ranking data, experts consistently rated AADT as the most important criterion, followed by road function, while public transport traffic received the lowest ranking. This suggests that experts have a shared understanding of the relative importance of these criteria in determining road maintenance levels.

In the second part, experts were asked to distribute 100 points among the six criteria, reflecting their importance in determining road maintenance priorities. The Kendall’s $W$ for Table 10 is 0.467 (values of the table were approximated by normalizing the data), which is lower than in Table 2, indicating that experts had a slightly weaker agreement when assigning exact importance scores compared to ranking them. The Chi-square test (${\mathsf{\chi }}^2$ = 25.70) still exceeds the critical value (${\mathsf{\chi }}_{crit}^2$(0.05; 5) = 11.07), confirming that the observed agreement is statistically significant. However, the lower $W$ value suggests that while experts generally agreed on the relative ranking of criteria, they differed more in their exact weighting of importance.

3.2.3. Criteria Ranking and Weight Analysis

After detailed calculations, it was determined that the ranking of the criteria is the same using both approaches. This means that despite the differences in the evaluation systems, experts agree on the importance ranking of the criteria. The ranking distribution according to the approaches is presented in

Table 11. Results of the classification of criteria according to different approaches.

Criteria	Rank
	Kendall	COPRAS
AADT	1	1
AADTT	3	3
Road function	2	2
Maximum allowed speed (Vmax)	4	4
Risk of traffic accidents	5	5
Public transport traffic	6	6

.

Based on the data from Table 11, it is obvious that the most important criterion is AADT, followed by the road function as the second most important, AADTT in third place, the maximum allowed speed in fourth, the risk of traffic accidents in fifth, and public transport traffic as the least important in sixth.

A detailed comparison of weights according to different calculation methodologies is presented in

Table 12. Results of the weights of the criteria according to different methodologies.

Criteria	Rank
	Kendall	COPRAS
AADT	0.27	0.30
AADTT	0.16	0.17
Road function (RF)	0.21	0.18
Maximum allowed speed (Vmax)	0.15	0.12
Risk of traffic accidents (RTA)	0.12	0.12
Public transport traffic (PTT)	0.09	0.11

.

The multi-criteria analysis technique plays a key role in evaluating and prioritizing the WRLM. It is particularly useful when we need to combine different criteria to determine the overall maintenance level for various sections of the road. In this process, the weight assigned to each criterion reflects the importance attributed to it based on expert opinions. These scores are then brought together using the multi-criteria analysis technique to determine a final maintenance level. This approach ensures that each criterion is considered in relation to the others, leading to a well-rounded and objective decision-making process. The methodology for determining winter road maintenance levels, based on the criterion weights obtained using the Kendall method, is as follows:

WRML = 0.27 (AADT) + 0.16 (AADTT) + 0.21 (RF) + 0.15 (VMAX) + 0.12 (RTA) + 0.09 (PTT)

(11)

3.3. Assessment of Road Maintenance Levels Based on Existing and Proposed Methodologies: A Case Study of the Raseiniai Road Maintenance Service

The Raseiniai northern region (see Figure 3) of the state-owned company Kelių priežiūra was chosen to evaluate changes in road maintenance levels across its state road network based on both existing and proposed methodologies. The selection of this region for road maintenance was based on its responsibility for the roads at all maintenance levels currently in use.

After performing all the calculations according to the proposed methodology and comparing them with the existing road maintenance levels, it was found that the extent of roads at the Level I maintenance remained unchanged, the extent of roads at Level II maintenance increased by three percent, the extent at Level III maintenance increased by six percent, the extent at Level IV maintenance decreased by fifteen percent, and the extent at Level V maintenance decreased by one percent. A graphical comparison is presented in Figure 4.

After analyzing the road maintenance levels calculated according to the proposed methodology and the existing road maintenance levels, it is observed that the maintenance level of most of the surveyed roads does not change. The road maintenance level changed only in 38,291 km sections out of 1075,422 km of surveyed roads, which constitutes about 3.6 percent of the entire surveyed road network. On roads or sections where the maintenance level changes, it changes by only one level. According to the calculations, the road maintenance level would not only increase but there would also be roads where it would decrease. Detailed road sections and their changes are presented in Figure 5.

4. Discussion

The proposed methodology was crafted by bringing together experts’ opinions. The weights of the criteria are supported by solid expert evidence. This methodology aims to justify the need for more tailored road winter maintenance by establishing financial benefits prior to the event, such as lower costs for road users related to traffic accidents, travel time, fuel consumption, and environmental effects. It also allows for ongoing adjustments to the evolving conditions of national roads, ensuring effective management and appropriate maintenance where it is genuinely needed.

Analysis of the road network revealed that the maintenance levels set by this new methodology provide a more accurate assessment of all criteria, likely to align better with the actual needs of society, especially in terms of road safety and efficient public transport. In some areas, maintenance levels were raised, while in others, they were lowered. Although these changes may seem minor, they are crucial for road users as they more effectively address potential problems.

One of the limitations of this study is the possible interconnection between specific criteria, such as how accident risk relates to traffic intensity. Therefore, future research could benefit from using sensitivity analysis to gain a clearer understanding of these relationships and how they influence the suggested methodology.

This research was conducted to understand the concerns raised in international studies regarding the traffic intensity (AADT and AADTT) and the class of the road in question being the primary parameters for ranking winter road maintenance activities. It also outlines the need for the ranking methodology to be properly defined [9,14,15,16]. Usman et al. [14] stated that proactive maintenance reduces accident rates, greatly on arterial routes, which supports the classification of criteria. Abohassan et al. [9] also point out the relationship between maintenance activities, atmospheric conditions, and road safety by strengthening the need to mitigate circumstances or events that would pose a danger in the proposed model. Studies on the dynamics of winter crashes [15] and on the estimation of weather-sensitive traffic [16] suggest that the application of spatial and predictive modeling to maintenance works, in conjunction with GIS, can improve operational efficiency. This study, like all others, shows a deviation from the norm by a small percentage of weight differences for some of the regions. Some research, ranging from Huang et al. [8], has advocated the inclusion of a weather index, which could improve the accuracy of the model.

Although the study was extensive in issuing varying degrees of maintenance, it had one additional flaw. A potential limitation was the bias in expert selection since the study largely depended on national experts. Despite a strong methodological framework, context from other countries could be included to improve applicability in diverse road maintenance practices and climatic conditions.

Studies such as “The Planning for Winter Road Maintenance in the Context of Climate Change” by Matthews et al. [36] have developed a Winter Severity Index (WSI) to understand how winter weather affects road maintenance. The WSI takes into account factors like snowfall levels and potential icing events and uses these to project future climate scenarios for better winter road maintenance planning. This research supports the idea of using weather-related risks and road functions to determine maintenance needs.

Additionally, Ye et al.’s research [37] highlights that traffic intensity plays a significant role in how resources are allocated for winter road maintenance, which backs up the weight analysis in our study. The use of GIS for decision-making is also supported by international studies, which show that spatial analysis can help optimize winter road maintenance operations [38,39].

Many road authorities, including those in Sweden, Canada, and the USA, have been adopting performance-based and service-based maintenance models. They focus on things like predefined service levels, contractual agreements, and keeping an eye on real-time data to help manage winter road maintenance.

Sweden employs a hybrid service-based model. Basically, the maintenance contracts lay out what’s expected in terms of service levels. For instance, it specifies the maximum snow depth allowed and how quickly contractors need to respond. Contractors have to stick to these standards, which helps keep things consistent across the network [40].

Canada, on the other hand, takes a performance-driven approach. Approaches often mix in real-time weather data and use Maintenance Decision Support Systems (or MDSS) to help guide their maintenance operations. The goal is to be cost-effective, efficient and take preventive measures [41].

The United States has adopted Output and Performance-Based Road Maintenance Contracts (OPBMC) since the 1990s, focusing on maintaining specific performance standards to ensure road quality and safety. However, the United States has a more decentralized system. Each state is free to set its own performance standards. A lot of agencies use MDSS platforms to pull in real-time weather and road condition data, and they prioritize maintenance actions based on those predefined measures [42].

The prepared Multi-Criteria Decision Analysis (MCDA)-based approach gives a more structured, data-driven way to prioritize maintenance. It does not just cope with performance thresholds. While those service-based models are all about hitting those contractually defined service levels, our model takes into account other important factors—accident risk, impact on public transport, and the overall function of the road. All in all, while Sweden, Canada, and the USA are all about being responsive in real-time and making sure contracts run smoothly, proposed model offers a framework that adds a more operational and tactical level and incorporates a broader range of criteria, offering a data-driven prioritization that can be especially beneficial in resource and time limited scenarios (i.e., choosing the route that decreases the number of potential accidents by considering factors such as traffic intensity, road category, speed limits, public transport intensity). Looking ahead, future research could really dive into how to integrate those real-time performance metrics into the MCDA framework and assess the internal correlations between criteria to further refine decision-making, ensuring a more adaptive and efficient approach to winter road maintenance. That would contribute to better adaptation to ever-changing winter conditions.

Overall, the study’s findings align with global research trends in winter road maintenance assessment while introducing a novel approach by enhancing the current methodology. This includes the incorporation of additional criteria, such as the risk of traffic accidents and public transport traffic, along with scientifically validated weightings for these criteria, an aspect often lacking in existing methodologies.

5. Conclusions

A survey of practices in road management among developed economies shows that it is prevalent in the majority of such nations to adopt a three-step classification system whereby roads are tiered into levels of high, medium, and low maintenance. Some nations have more sophisticated systems with even six tiers for some, based on regional characteristics, climate factors, and provision of funding.

Road maintenance budgets in the majority of parts of the globe are limited and often insufficient to offer the highest standards for all roads. As a result, the authorities must prioritize maintenance work by categorizing roads based on their priority. Priority roads are offered higher maintenance levels, whereas less important routes are offered lower levels. On the network level, several factors—engineering considerations, economic impacts, social significance, and environmental effects—are taken into account when determining maintenance priorities.

Winter road maintenance classification is typically based on parameters such as Average Annual Daily Traffic (AADT), Average Annual Daily Truck Traffic (AADTT), speed limits, and functional road classification. Current methods typically do not incorporate scientifically proven weightings of these parameters. In the hope of making the system improved, it is suggested that additional parameters—i.e., traffic accident risk, pavement type, and public transport traffic—be incorporated, and their weightings be supported with scientific studies.

This improved classification of winter road maintenance can be considered for a broader discussion and potential adoption nationwide by road authority management. This scheme is based on consensus of experts, scientifically based criteria, and multi-criteria analysis techniques. It is adaptable and can accept alternative scoring procedures and is therefore suitable for a variety of situations. It can be integrated into GIS-based systems to enhance the ability of spatial analysis to aid better decision-making.

An expert evaluation of the maintenance levels of state road networks in the Lithuanian Raseiniai northern region was organized based on a developed criteria system. After performing and comparing all the calculations with the level of existing road maintenance, it was determined that the scope of maintained roads at level I remained unchanged, level II roads increased by 3%, level III roads increased by 6%, level IV roads decreased by 15%, level IV roads decreased and level V roads reduced by 1%.

The level of maintenance of the roads only changed in sections that add up to 38.291 km out of the total 1075.422 km that were studied, which is about 3.6% of the entire road network examined. Although these changes may seem small, they are actually very important to those who use the roads. This trend is in line with what we see globally, where road maintenance classification systems are constantly being updated based on priorities, budget limitations, and new methods. The results emphasize how crucial it is to make evidence-based adjustments in maintenance levels to make the best use of resources and improve both road safety and efficiency.

By utilizing this method, countries would be able to finance winter road maintenance more effectively with the demonstration of real benefits, i.e., fewer accidents, reduced travel time, decreased fuel consumption, and less environmental load. The system also offers provision for on-going adjustments in accordance with the changing road conditions, therefore leading to better management and more science-driven maintenance planning. In the USA, winter road maintenance is largely decentralized, with each state and local authority adopting its own performance-based approach, often using service-level agreements and real-time monitoring systems. Many agencies utilize Maintenance Decision Support Systems (MDSS), integrating weather forecasting, road condition data, and automated treatment recommendations. Additionally, prioritization methods typically rely on budget constraints and predefined service levels rather than multi-criteria decision analysis (MCDA).

In contrast, our proposed methodology provides a structured MCDA-based framework that assigns scientifically weighted scores to multiple criteria, including road safety indicators, public transport needs, and accident risk, which are often not explicitly incorporated in US models. While the USA focuses on service-based contract efficiency, our model enhances decision-making by offering a more data-driven prioritization of road sections, particularly in resource-limited conditions. Future research could explore integrating elements of US performance-based approaches with the MCDA framework to improve adaptability and responsiveness to changing winter conditions.

Future research could benefit from incorporating sensitivity analysis. This would help to better grasp the links between accident risk and traffic intensity. In addition, bringing in a variety of international viewpoints could make road maintenance practices more relevant under different climate conditions.