The Settlement Ratio and Settled Area: Novel Indicators for Analyzing Land Use in Relation to Road Network Functions and Performance

Abstract

Land use significantly influences mobility dynamics, affecting both travel behavior and mode choice. Traditional indicators such as the Floor Area Ratio, Land-Use Mix Index, and Built-up Area Ratio are widely used to describe settlement patterns; yet, they often fail to capture their functional impacts on road networks. This study introduces two complementary indicators—Settlement Ratio (SR) and Settled Area (SA)—developed through a spatial analysis framework integrating GIS data and MATLAB processing. SR offers a continuous typological profile of built-up functions along the road axis, while SA measures the percentage of anthropized land within fixed analysis windows. Applied to two Italian state roads, SS14 and SS309, in the Veneto Region, the dual-indicator approach reveals how the intensity (SR) and extent (SA) of settlement vary across different territorial contexts. In suburban segments, SR values exceeding 15–20, together with SA levels between 10% and 15%, highlight the significant spatial impact of isolated development clusters—often not evident from macro-scale observations. These findings demonstrate that the SR–SA framework provides a robust tool for analyzing land use in relation to road function. Although the study focuses on spatial structure and indicator design, future developments will explore correlations with traffic flow, speed, and crash data to support road safety analyses.

1. Introduction

The road guidance system encompasses various elements, including human factors, vehicles, infrastructure, environment, and traffic. The environmental aspect extends beyond naturalistic features, such as terrain types, to include the degree of land anthropization [1,2]. The interplay between the transport network and settlement patterns is intrinsically complex and shaped by various land-use types—residential, commercial, industrial, recreational, agricultural, and forested areas—which significantly influence travel behavior, mode choice, and operating speeds [3]. High-density residential areas typically encourage walking and cycling, whereas commercial and industrial zones increase vehicular traffic [4,5]. Accessibility, closely tied to land use, is a key factor in shaping transport mode choices. Higher accessibility reduces travel distances and encourages active modes, such as walking and cycling, while discouraging car use [6,7]. Well-connected areas with efficient public transport show higher usage rates than less accessible zones [8]. The proximity of destinations also contributes to more sustainable mobility patterns by minimizing travel time and supporting non-motorized transport [9,10]. Urban development models that prioritize accessibility and integrate sustainable transport strategies are linked to lower greenhouse gas emissions and improved urban quality of life [11]. User speed is strongly influenced by the degree of anthropization, land-use density, and road classification. While high-category extra-urban roads support higher speeds, dense urban areas—with frequent intersections and pedestrian flows—tend to reduce them [12]. Road design features, including intersection layouts and speed regulations, shape user behavior and modal choices [13,14]. Urban policies that promote mixed-use development and transit-oriented designs directly influence average speeds and traffic patterns [15,16]. Such policies also enhance pedestrian and cycling infrastructure [17,18]. Effective road network planning requires detailed classification of land use to guide resource allocation and sustainable development. The spatial distribution of residential, commercial, industrial, and agricultural functions directly affects mobility demand, accessibility, and transport system efficiency [19,20]. Integrated planning approaches highlight how compact and polycentric urban development can mitigate environmental impacts while enhancing urban quality of life [21]. Furthermore, coordinated land-use and transport strategies contribute to more coherent planning processes, reinforcing the sustainability and resilience of urban systems [22]. Within this framework, comprehensive land-use analysis is essential to align infrastructure with evolving socio-spatial dynamics.

Over time, various indices have been developed to classify land use and measure the degree of anthropization. The Floor Area Ratio (FAR), calculated as the ratio between a building’s total floor area and the area of the plot it occupies, is widely used to represent development intensity. FAR is a key parameter in zoning regulations, as it influences building density, population capacity, and urban form [23]. The Land-Use Mix Index evaluates the diversity of land uses in each area, where a high index indicates a balanced mix of residential, commercial, and industrial activities [24]. The Accessibility Index reflects how easily residents can reach essential services, providing an indication of spatial efficiency and the quality of residential areas [25]. Closely related to this index is the Street Connectivity Index, which measures the density and integration of the road network, offering insights into mobility and route efficiency [26]. On the other hand, other indicators focus on physical land coverage. The Built-up Area Ratio expresses the percentage of land covered by buildings, quantifying settlement concentration [27], while the Impervious Surface Area (ISA) measures the extent of sealed surfaces such as roads and rooftops—key elements in assessing runoff and environmental impact [28]. Finally, the Urban Green Space Index quantifies the share of green areas relative to the total urban surface, supporting evaluations of ecological sustainability and urban livability [29]. Together, these indices support detailed spatial analysis, providing essential inputs for urban planning, environmental assessment, and sustainable development strategies. A summary of the main indices is presented in

Table 1. Indices for assessing land-use types and quantifying the degree of anthropization.

Index	Evaluation for Land-Use Functionality
Floor Area Ratio (FAR) [30]	Intensity of building development in an area
Land-Use Mix [11]	Diversity of land uses in a given area
Accessibility [31]	Accessibility score for each destination
Street Connectivity [32]	Density of the road network and the Connection between different destinations
Built-up Area [33]	levels of urbanization, such as the percentage of land covered by buildings
Surface Area (ISA) [34]	extent of impermeable surfaces
Urban Green Space [35]	urban green space relative to the total urban area of an urban, to assess environmental suit

.

Cadastral land-use data are primarily collected and processed through Geographic Information Systems (GIS) and remote sensing techniques. GIS enables the integration of spatial and attribute data for detailed land-use mapping and analysis [36]. Remote sensing, using satellite or aerial imagery, facilitates large-scale monitoring of urban expansion and land cover changes, particularly in rapidly developing areas [37]. Although these tools and indicators enable solid territorial analysis, they often lack the ability to evaluate the functional impacts of land use on mobility and transport dynamics.

Traditional land-use indicators employed in urban and territorial planning present notable limitations when applied to road mobility, safety, and performance analyses. They rarely establish direct correlations with the functional and technical attributes of road infrastructure, frequently overlooking both network hierarchy and continuity. Furthermore, their static, area-based representations fail to reflect the dynamic interactions between settlements and mobility systems, particularly along linear infrastructures such as roads. Consequently, these indicators are insufficient for comprehensive analyses aimed at managing road corridors and identifying localized risk patterns.

To address these limitations, this study proposes two novel spatial indicators: the Settlement Ratio (SR) and the Settled Area (SA) [38]. Unlike conventional metrics, SR provides a continuous typological profile along the entire road axis, while SA measures the proportion of anthropized land within predefined analysis windows. Combined, these indicators form a dynamic, infrastructure-oriented framework for quantifying both the intensity and extent of settlement. This approach enables a more accurate assessment of how the surrounding settlement context influences road infrastructure, traffic patterns, and accident incidence. Future research applying these indicators could support the development of targeted strategies to enhance road safety and operational performance.

2. Materials and Methods

This study examines the impact of settlement patterns and land use on the operational performance of road infrastructure by introducing a novel indicator, the SR. The proposed index is inspired by the well-established concept of Coverage Ratio (CR), a metric rooted in urban planning theory, developed to synthesize the physical and functional characteristics of the surrounding environment in which the road infrastructure is embedded. The CR is the ratio of Sc to Sf. Sc represents the ground projection of the Gross Usable Area (Sc), which is the sum of the surfaces of all floors of the building, calculated gross of the perimeter walls, expressed in square meters (sqm). Sf corresponds to the total area of the associated land parcel [39].

This study employs the SR as part of a quantitative mapping methodology aimed at evaluating land use. The SR is calculated as the ratio between the area covered by building footprints and the total lot area, providing an integrated, both quantitative and qualitative, measure of land-use intensity. The innovative aspect of this approach lies in its integration of the functional characteristics of the surrounding environment into the SR calculation by incorporating both geometric data and information related to land use and building activity.

The methodology is structured in two main phases. In the first phase, vector data are processed and managed using QGIS software, version 3.42. This is followed by a second phase involving more advanced analysis using specialized computational software.

2.1. Spatial Analysis

The first phase of the proposed methodology consists of a spatial analysis conducted using QGIS software, version 3.42. This phase is centered on the acquisition and management of three key geospatial and georeferenced datasets: Buildings, Land Cover Maps, and the Road Graph. During this phase, a series of mobile investigation windows is generated along the road infrastructure. Each window functions as a discrete unit of analysis, facilitating the systematic identification and classification of all buildings and land parcels contained within its boundaries. This approach yields a detailed dataset in which each building is associated with both the corresponding land parcel and a unique investigation window identifier.

The objectives of this phase are twofold: first, to ensure a systematic and organized collection of spatially relevant data; second, to establish an accurate spatial correspondence between each building and its surrounding territorial context. This detailed spatial correlation is fundamental for producing accurate thematic maps and is essential for the subsequent analytical phases of the methodology.

Intersection in QGIS

The proposed methodology aims to continuously and progressively quantify the degree of anthropization along a road network. The calculation of the SR index begins with mapping the road network in the QGIS environment, using a layer of densely georeferenced vertices based on the SR Gauss–Boaga FO reference system (EPSG:3003). At regular intervals of 20 m along the road axis, a circular buffer with a radius of 500 m is generated, centered on each vertex. The ensemble of these overlapping buffers constitutes a moving window for an extensive spatial survey, enabling a detailed analysis of the surrounding area.

The 500 m radius was selected as it provides an appropriate spatial extent to capture adjacent anthropization potentially affecting the operability and safety of the infrastructure. This choice is supported by scientific evidence demonstrating that a 500 m buffer effectively correlates built-up areas with road traffic mortality rates, suggesting an accurate representation of the anthropogenic influence on the environment and, by extension, on operational and safety conditions of road infrastructure [40,41]. Each buffer thus functions as an analysis window to evaluate the settlement intensity along the network, systematically identifying land parcels and buildings, and linking each building to its corresponding parcel and buffer.

The subsequent phase involves performing two spatial intersection operations between the circular buffers and two cadastral layers: Buildings and Land Cover Map (LCM). The first intersection identifies the parcels contained within each buffer, with QGIS accurately accounting for only the portion of parcels falling inside the buffer boundaries when partial overlaps occur. This operation produces the “Lands500” dataset, encompassing the parcels identified across the sequence of buffers, enriched with geometric attributes (e.g., area), functional characteristics (e.g., land use), and buffer identification. The second intersection is conducted between the Lands500 dataset and the Buildings layer, resulting in the “Buildings500” dataset, which includes all buildings located within the selected parcels. Each building is associated with a unique identifier, geometric and functional properties, and its respective parcel and buffer.

QGIS enables efficient management of this spatial survey, allowing rapid and accurate data processing without loss of critical information. The resulting Lands500 and Buildings500 datasets are subsequently exported in .csv format for further analysis using MATLAB (MATrix LABoratory) software -R2023b version 23.2- ensuring robust validation of the derived results. Figure 1 illustrates the outcomes of the QGIS intersection operations, highlighting the circular buffer in yellow, land parcels in grey, and buildings included in the analysis in orange.

2.2. Calculation Analysis

The second phase of the analysis, which calculates the SR index, is conducted using MATLAB. The MATLAB scripts reverse the previous steps (Section 2.1), allowing the assignment of a unique SR value to each vertex of the road graph. This process leverages the datasets generated (Buildings500 and Lands500). The following paragraphs detail this phase, emphasizing the aggregation and synthesis of spatial data from QGIS to provide an accurate representation of the degree of anthropization along the road infrastructure.

2.2.1. Land-Use Aggregation

The “National Land Use Database” defines 14 land-use categories, which are too detailed for evaluating the impact on road infrastructure [3]. To streamline the analysis, these categories are aggregated into 4 macro-categories, as shown in Figure 2: agricultural, infrastructural, residential, and community. This reclassification simplifies the assessment, focusing on key land uses that significantly affect road infrastructure. By using these macro-categories, SR curves can be generated, providing insights into the overall SR index and identifying which land-use types have the most influence on road functionality and safety, enhancing the understanding of territorial dynamics. This aggregation avoids excessive fragmentation, ensuring the analysis remains clear and operationally relevant. It strikes a balance between preserving essential information and enabling meaningful interpretation of how land use affects infrastructure performance.

2.2.2. Calculation of the SR Index

The calculation code elaborates the information from the Buildings500 dataset into the Lands500 dataset, using ID codes assigned to each spatial element in QGIS. The code performs a cross-referencing operation between the two datasets to identify all buildings within each plot. This enables the summation of building areas, thereby determining the built-up area for each plot. With both the built-up area and the total area of each plot known, a preliminary evaluation of the SR index can be made. The preliminary SR evaluation for each plot generates an index value ranging from 0 to 1, representing the ratio of built-up area to total plot area, without accounting for the initial conditions of the plots and buildings.

Following this preliminary SR index calculation, the methodology applies an area-based weighting to refine the results. A coefficient is introduced, representing the ratio between the area of the j-th plot and the area of the i-th circular window (Equation (1)). This multiplicative coefficient allows the final SR index value assigned to each vertex of the road graph to account for the varying plot sizes, ensuring an area-weighted evaluation.

$${\mathsf{\omega }}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{A}}_{{\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}}_{\mathrm{j}}}}{{\mathrm{A}}_{{\mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{w}}_{\mathrm{i}}}}},$$

(1)

By multiplying the initial SR value by the calculated coefficient, a weighted SR is obtained for each plot (Equation (2)). This approach gives greater significance to larger plots and less to smaller plots within each investigation window. The coefficient ensures that the final SR calculation is not merely a sum of individual SR values, but one that is appropriately weighted.

$${\mathrm{S}\mathrm{R}}_{\mathrm{i}}=\sum {\mathsf{\omega }}_{\mathrm{j}}·{\mathrm{S}\mathrm{R}}_{\mathrm{j}},$$

(2)

This refined method results in an SR index that accurately represents the degree of anthropization along the road infrastructure, considering the varying sizes and characteristics of the plots. It provides a more precise and meaningful assessment of the influence of human settlements on the operation and safety of the road infrastructure.

The final SR index calculation differentiates trends based on land-use macro categories. This approach results in four distinct SR value profiles for each circular investigation window. The code first identifies all plots within the same macro category for each window and sums their respective weighted SR values, resulting in four separate SR values per window. At the end of this phase, all data from the Lands500 and Buildings500 datasets are condensed and assigned to the vertices of the road graph. By reconstructing the road graph’s planimetric alignment and associating each vertex with its respective road progression, the methodology enables a graphical representation of the SR profiles along the road infrastructure. The analysis then proceeds with a global calculation of the total settled area surrounding the infrastructure, independent of land-use classification. The code sums the built-up areas within each window and divides this value by the circle’s total area, yielding a percentage value of the SA. A final check ensures that for each road vertex, the sum of the four SR values matches the calculated SA value. This result confirms the consistency of the calculations, demonstrating that the multiplicative coefficient properly weights larger plots without distorting the overall results.

Figure 3 summarizes the methodology proposed in this study using a flow chart, starting with the input data and then moving through the two phases of spatial analysis and calculation to arrive at the SR index assessment.

3. Case Study

The proposed methodology was validated using data from the road network in the Veneto Region, Italy, with the road graph provided by ANAS S.p.A., the road network manager. This graph includes georeferenced points, enabling the reconstruction of the road layout based on a methodology previously developed by the authors [42]. Two roads were selected for the application of this methodology: State Road 14 (SS14) “della Venezia Giulia” and State Road 309 (SS309) “Romea”. Figure 4 shows the location of the two roads within the Veneto Region.

The State Road 14 of Venezia Giulia (SS14) is an Italian state road connecting Venezia Mestre with Pese di Grozzana [43]. The section of road examined extends from the municipality of San Donà di Piave to the municipality of Concordia Sagittaria, both in the metropolitan city of Venice, for a length of 22.5 km.

State road 309 Romea (SS309) is an Italian state road, part of the European road E55, connecting Ravenna to Mestre (VE) [44]. The examined section of the SS 309 has a length of 71.04 km, stretching from the regional border with Emilia-Romagna to the city of Venice.

The input datasets and the processing steps undertaken to define the SR index are detailed below.

3.1. Buildings

The Geoportal of the Veneto Region is a valuable platform for consulting and downloading territorial and environmental datasets provided by the regional authorities. In the portal’s “data download” section, users can retrieve a wide range of data, including vector, raster, and alphanumeric formats [45]. Among the available resources, the “Buildings” layer, which covers all seven provinces of Veneto, was of relevance to this study. These spatial datasets are georeferenced using the SR Gauss–Boaga FO reference system (EPSG:3003). The handling and processing of the vector data were carried out using QGIS, an open-source software that facilitates the analysis and editing of spatial and cartographic data. QGIS supports a wide range of functionalities through built-in tools and custom calculation scripts. The first step in the process was to aggregate the building data from all provinces into a single dataset. This step aimed to optimize the calculation process and streamline operations within the software, reducing redundancy and enhancing efficiency.

Table 2. Number of buildings by province and total for the Veneto Region.

Province	Buildings
Verona	306,034.00
Vicenza	396,428.00
Venezia	475,504.00
Treviso	516,366.00
Rovigo	136,357.00
Padova	347,847.00
Belluno	160,875.00
Total	2,339,411.00

provides an overview of the number of buildings in each provincial layer, as well as the total number of buildings across the region.

Upon completion of the aggregation process, a single comprehensive layer was created, encompassing all buildings within the Veneto Region. This dataset includes both functional and physical information for each building, as outlined below:

• Identification code (ID);
• Height (m);
• Intended use;
• Number of floors.

Before conducting spatial analysis, the building dataset was validated to correct geometric errors like self-intersections, duplicate nodes, and polygons with too few vertices, ensuring data reliability. Duplicate buildings were removed, reducing the dataset from 2,339,411 to 2,337,465 entries. Overlapping buildings were addressed using tools like “Polygon Self-Intersection” and “Dissolve” to merge and eliminate overlaps, ultimately refining the dataset to 2,331,010 entries without duplicates or geometric issues. Following the validation of the Buildings dataset, additional geometric information essential for calculating the SR index, such as the area of each building, was derived. QGIS facilitated the extraction of these geometric properties through pre-installed tools and simple commands.

Table 3. Excerpt from the building table.

ID	Height (m)	Intended Use	Number of Floors	Area (m2)	Perimeter (m)	Ebaric	Nbaric	Land Use
100309	3.6	Office	1	126.54	53.95	1,757,152.10	5,093,854.96	10
10031	3.07	Shack	1	43.97	28.01	1,774,341.22	5,066,848.41	1
100310	9.7	Residential	3	354.00	82.97	1,719,291.39	4,994,369.78	7
100312	7.1	Residential	2	416.45	123.39	1,705,790.81	4,982,099.78	7
100314	7	Residential	2	925.49	143.31	1,722,388.40	4,995,712.86	7
100315	6	Residential	2	84.41	42.47	1,766,785.71	5,093,343.47	7
100316	6	Residential	2	19.98	21.09	1,766,612.91	5,093,329.25	7
100317	13	School	4	465.93	135.25	1,720,160.58	4,995,812.06	8

provides an excerpt from the Buildings dataset, showing additional fields such as area, perimeter, and centroid coordinates (E, N). Additionally, a field detailing the building’s use, classified according to the “National Land Use Database: Land Use and Land Cover Classification,” was appended.

3.2. Land Cover Map

The Directorate of Territorial Planning of the Veneto Region has made available the updated 2020 Land Cover Map (LCM) [46]. This dataset, provided in shapefile format, is easily integrated into a GIS environment, allowing for seamless data manipulation and analysis. The LCM offers a detailed segmentation of the Veneto Region’s territory, dividing it into 397,455 lots. Each lot is associated with comprehensive functional and physical information, including land-use coding, as well as the respective area and perimeter measurements. Like the Buildings dataset, the LCM layer was subjected to a rigorous validation process to ensure the geometric accuracy of its elements. This verification was performed using the “Check Validity” tool, which identified and rectified any geometric anomalies. Only elements displaying geometric inconsistencies were corrected, ensuring that the final dataset comprised entirely valid geometries. Once the dataset was validated, additional geometric and physical characteristics were calculated for each lot to facilitate the accurate computation of the SR index. These characteristics include the area (in square meters), perimeter (in meters), and the E and N coordinates of the centroid, along with a unique identification code for each lot. The Code Land Use refers to the official classification, while Land Use indicates its assigned macro-category. The extraction and calculation of this other data were carried out using specific tools and commands within the GIS environment, ensuring a thorough and precise analysis (

Table 4. Excerpt from the LCM table.

Code Land Use	Legend	Area (m2)	Perimeter (m)	Ebaric	Nbaric	ID	Land Use
11110	Residential	33,450.89	1014.78	1,631,504.77	5,052,060.84	1752	7
11110	Residential	38,319.79	883.33	1,632,517.84	5,033,063.83	1292	7
11110	Residential	23,820.24	929.57	1,633,230.73	5,048,295.77	1710	7
11110	Residential	6825.32	397.87	1,633,505.93	5,048,143.77	1709	7
11110	Residential	12,971.33	783.76	1,633,516.68	5,048,020.35	1708	7
11110	Residential	18,762.83	629.09	1,635,812.70	5,023,795.81	1196	7
11110	Residential	8517.06	480.14	1,640,603.74	5,069,409.89	1888	7
11110	Residential	3655.86	234.93	1,640,673.79	5,069,443.60	1887	7

).

Following the validation and processing of the cadastral data, the proposed methodology was applied to the SS14 and SS309 roads, allowing for the quantification of the surrounding degree of anthropization. The final datasets, Lands500 and Buildings500, obtained through the intersection operations in QGIS, were subsequently subjected to the second phase of calculation in MATLAB. This process led to the definition of the various reference indices, SR and SA, which enable a thorough description and assessment of the anthropic impact on road infrastructures.

4. Results

The methodology provides a comprehensive output graph that synthesizes all the initial data regarding buildings and plots intercepted by the circular investigation windows, represented through the SR and SA values. The graph shows the trends of different SR indices and the SA along a road, with road progression (in meters) displayed on the x-axis. The SR is an indicator of the extent of built-up or developed areas surrounding the road: a high SR indicates a higher density of buildings and settlements, while a low SR reflects a less urbanized environment. The final graph presents the SR values on the left y-axis, categorized into four macro land-use types: agricultural, infrastructure, residential, and community. The right y-axis displays the percentage of built-up area, indicating the portion of the territory occupied by buildings. The main results for the two roads, SS14 and SS309, are illustrated below, with a focus on key sections that exhibit significant variations in SR and SA.

4.1. State Road n.14 Della Venezia Giulia (SS14)

Figure 5 illustrates the SR and SA trends along the first 10 km of the SS14. In the initial two kilometers, the road passes through the inhabited area of Calvecchia, where the residential SR fluctuates between two and five, indicating a moderate presence of housing. Other SR categories (agricultural, infrastructural, and community) remain very low or negligible, reflecting limited infrastructure, agricultural activity, and community facilities.

The notable peak in the residential SR suggests recent urbanization, marked by new housing developments, while the low community SR indicates that community services have not expanded at the same rate. The SA curve shows an initial peak of about 10% at the 20 m mark, signaling a significant presence of, particularly community structures, before the road enters the inhabited area. After Calvecchia, the SA percentage drops to zero but then rises sharply, peaking at over 15% around the 5000 m mark, suggesting the approach to a densely built area, such as the town of Ceggia. In this section, the community SR significantly increases between 4000 and 6000 m, reaching values over 15, indicating a suburban area with community-related activities such as industries, shopping centers, and warehouses. The residential SR also peaks around 6000 m, with values reaching approximately 11, indicating a high housing concentration. In the final portion of this segment, the SA percentage decreases and stabilizes around 2%, indicating a reduction in building density and a transition to less urbanized or rural areas. This transition suggests that the road moves into a more natural setting, away from densely populated areas. Figure 5 highlights how the SS14 crosses various types of terrain, transitioning from urban and densely developed areas to more rural and natural regions. The detailed SR and SA values provide valuable insights into the distribution of settlements along the road, helping to evaluate the impact of urbanization on the road’s functionality and safety.

Figure 6 presents the SR and SA analysis for the SS14 from kilometers 10 to 20. This analysis is divided into two segments: up to kilometer 14 and from kilometer 14 to the end of the section.

In the first segment, the built-up area shows two peaks around kilometers 11 and 12.5, primarily influenced by the community SR. The community SR starts to rise gradually, reaching values around five, indicating a significant presence of community-oriented activities. It then increases significantly, peaking at 20 near kilometer 12, indicating a major concentration of community structures. The SA also reaches higher values due to a residential SR peak at kilometer 13, suggesting the proximity of a residential neighborhood. From kilometer 14 onwards, the SA percentage decreases and stabilizes around 5%, indicating a reduced building density and a transition to less urbanized or rural areas. The community SR drops sharply, indicating a transition to areas with fewer community structures. All other SR indices remain low and stable, suggesting a limited presence of built-up areas, infrastructure, residences, and community facilities. This trend suggests a transition towards more rural regions with minimal urban development. Unlike Figure 5, which captured urban segments, Figure 6 shows the SR and SA trends along an extra-urban stretch of the SS14 that does not pass through major inhabited centers. Notably, peaks in built-up areas and SR values occur not only in residential urban areas but also in rural zones. In these contexts, the presence of residential “islands” as well as industrial and commercial areas leads to significantly higher SR index values. Looking ahead, it will be particularly relevant to investigate how, given equal SA values, variations in SR across different land-use macro-categories may differently affect the functionality of the road network. Special attention should be given to how these differences impact average user speeds, safety levels, and the incidence of traffic accidents.

4.2. State Road n.309 Romea (SS309)

Figure 7 illustrates a section of the SS309 between kilometers 10 and 20. In the first six kilometers, the SA follows a sinusoidal pattern, with peaks up to 10%, indicating a moderate and alternating presence of buildings and structures. These peaks are primarily driven by the community SR, suggesting that this road section is characterized by community-oriented activities. The fluctuations in the community SR indicate a consistent presence of industrial, commercial, and community-related activities.

A progressive decrease in both the SA and SR indices suggests a transition towards less urbanized or rural areas. Low SR values in other categories suggest a limited presence of new infrastructure and residences, indicating a zone that maintains community development without significant growth in infrastructure and housing. At kilometer 16, as the road approaches Tessera, the SA percentage gradually increases, peaking at around 11% at the kilometer 15 mark, indicating a rise in building density. Both the community and residential SR indices significantly rise around kilometer 17, reaching values of approximately 5 and 10, respectively. The gradual SA increase coincides with a significant residential SR rise, marking the road’s passage through an inhabited area. There is also a community SR peak, suggesting a well-planned residential area with adequate community services. In summary, Figure 7 shows that the SS309 section from kilometers 10 to 20 displays a varied distribution of building and community activity, alternating between urbanized and rural zones. This pattern underscores the importance of spatial planning that integrates residential needs with community services for balanced and functional territorial development.

Figure 8 focuses on the SS309 section between kilometers 20 and 30. In the first four kilometers, the settled area remains relatively low, fluctuating between 0% and 5%, suggesting a low-density development zone. The residential SR exhibits some variations, with peaks around five, indicating notable residential neighborhoods in the peri-urban area. The infrastructure, agricultural, and community SR indices remain low, generally below 5%, indicating limited infrastructure and community facilities. As the road passes through Musile di Piave, the SA percentage progressively increases, peaking at approximately 12% around the kilometer 25 mark, indicating growing development density. The residential SR also peaks in this area, with values between 10 and 15, suggesting a densely built urban area. Although the community SR remains generally low, it shows a slight increase, indicating a gradual growth in community facilities.

From kilometer 26 onwards, the SA percentage decreases, stabilizing between 5% and 10%, indicating reduced development density and a transition to less urbanized or rural areas. The infrastructure, agricultural, and community SR indices remain low and stable, reflecting limited new infrastructure and community facilities. The decrease in both SA and SR indices clearly suggests a transition to a less urbanized zone. In the final kilometer, however, there is a significant increase in the settled area and both residential and community SR indices, indicating a high-density development zone. The rise in community SR suggests the development of community services, although at a slower pace than other categories.

In summary, Figure 8 reveals that the SS309 section from kilometers 20 to 30 alternates between low-density development areas and more urbanized zones, with notable growth near Musile di Piave. These dynamics underscore the importance of urban planning that addresses both residential and community needs, promoting balanced and sustainable development along the road corridor.

5. Discussion

The proposed methodology, based on the introduction of the SR and SA indicators, enables a continuous and detailed representation of territorial anthropization along extra-urban road segments. Calculated along the road axis, these indicators differentiate the contributions of various land-use categories—residential, community, agricultural, and infrastructural—to the surrounding environment. Although the case studies analyzed are based on historical datasets, the approach is fully compatible with the integration of updated or real-time data, offering promising avenues for future development. This dynamic methodology overcomes the limitations of traditional static measures, which are often confined to discrete spatial units disconnected from the linear nature of road infrastructure.

In the case of SS14, the analysis revealed variable indicator behavior. In the first 10 km, the residential SR fluctuated between 2 and 5, corresponding to the settlement of Calvecchia. During this segment, SA was approximately 10%, partly driven by community structures. Between 4000 m and 6000 m, simultaneous increases in both residential and community SR indicated entry into a high-density suburban zone—likely the Ceggia periphery—with community SR values exceeding 15 and SA stabilizing around 15%. Beyond 6000 m, both indices declined, signaling a return to a rural, less anthropized context. Between chainage 10 km and 20 km, SS14 exhibited two notable SA peaks at approximately 11 km and 12.5 km, corresponding to surges in community SR, which reached values near 20. An additional residential SR peak occurred around 13 km, followed by SA stabilizing at 5% and a general decline in SR values—indicative of a transition to a scarcely anthropized extra-urban zone. These results confirm that isolated settlements, industrial clusters, or small centers can exert pronounced local impacts on territory configuration, even when urban continuity is absent.

The SS309 corridor from chainage 10 km to 30 km displayed similar trends, but with a more fragmented spatial distribution. Between 10 km and 20 km, SA followed a sinusoidal pattern, with values oscillating around 10%, primarily driven by elevated community SR—reflecting a widespread presence of services, industrial areas, and commercial facilities. A residential SR peak of 10 occurred near 17 km in proximity to Tessera, where SA reached 11%. The section from 20 km to 30 km showed a general reduction in anthropization until reaching Musile di Piave, where SA increased to 12% and residential SR exceeded 15, suggesting a significant settlement node. A further increase across the final kilometer highlights the expansion of another urban center.

A comparison of the two roads emphasizes how, despite similar SA values, SR configurations may vary substantially depending on the type of settlement. The type and distribution of land uses, not merely the extent of anthropization, affect road performance and safety. Residential islands, commercial centers, or industrial zones along extra-urban roads can cause functional discontinuities, speed variations, and safety concerns, particularly at private access points, intersections, or unregulated pedestrian crossings [47,48]. Previous studies have shown that land-use heterogeneity and fragmented development near roads significantly affect travel speeds and crash frequency, especially in suburban and peri-urban areas [32,49]. Functional land-use mix and built environment connectivity are also associated with crash rates, highlighting the importance of spatial context in road safety analysis [50].

Methodologically, the joint use of SR and SA facilitates a multi-dimensional and integrated assessment of territorial conditions, enriching traditional urban and transport analyses. While existing research has explored land use impacts on mobility and safety, these studies typically focus on urban areas and point-based metrics. The novelty of SR lies in providing a continuous and spatially distributed measure, enabling correlation with traffic flows, operational speeds, and incident data, thereby supporting evidence-based transport planning. The four-component structure of the SR offers an analytical framework to evaluate the differential influence of various land uses on vehicular flows. Differentiating between residential and industrial components enhances insight into origin-destination dynamics and network friction points. SA serves as a complementary metric for monitoring anthropization levels, aiding infrastructure design, maintenance, and safety interventions.

The introduction of SR and SA opens up significant prospects for infrastructure planning and management. These indicators could be incorporated into predictive traffic models, decision-support systems for infrastructure upgrades, and ex-ante impact assessments for new developments. Further applications of the proposed study and methodological advancements may include integration with socio-economic data, real-time traffic flows, and accident statistics to build predictive models capable of anticipating functional vulnerabilities. Based on the most frequent and recurrent crash types that will be identified, it will also be possible to define and implement targeted safety measures—such as the future installation of roadside restraint systems, traffic-calming devices, revised access and intersection management strategies, or speed limit adjustments—tailored to the specific characteristics and risks of each segment. Implementing this methodology on a regional or national scale would enable more precise identification of land-use and mobility conflicts, guiding territorial policies towards sustainable and resilient solutions. Here, the combination of GIS tools, dynamic modelling, and continuous spatial indicators represents a promising frontier for both research and road governance.

6. Conclusions

The methodology proposed in this study enables the creation of a high-quality, validated dataset, which is essential for detailed spatial analysis and precise calculation of the SR. GIS-based processing ensures a continuous and accurate representation of anthropization along road infrastructures, offering valuable insights into the relationship between land-use patterns and road network performance.

The SR indicator—complemented by the SA metric—effectively highlights how different land-use categories (residential, community, agricultural, and infrastructural) can influence traffic dynamics, operating speeds, and safety. The joint analysis and probabilistic correlation of the spatial trends of the SR and SA indices with operating speed patterns along the road network represent a promising future approach for gaining deeper insight into how settlement organization influences infrastructure performance and safety levels. A particularly relevant aspect will be the ability to compare road segments with similar SA values but differing SR distributions across land-use categories. Although such comparative analysis falls outside the scope of this study, future research could leverage this potential to investigate the role of settlement configurations in shaping road infrastructure functionality and identifying related risk factors. Also, the integration of SR data with historical crash records should present opportunities for risk-based analysis by identifying spatial correlations between settlement density and incident frequency. Although not implemented here, future studies could explore the use of artificial intelligence and machine learning techniques to develop predictive models capable of identifying high-risk road segments based on surrounding territorial characteristics. Moreover, this methodological framework supports long-term infrastructure planning and territorial governance. Coupled with urban development projections, it can help anticipate areas of rapid growth and guide proactive interventions aimed at preventing congestion and mitigating safety risks.

In conclusion, the combined use of SR and SA provides a scalable, data-driven approach to understanding the interaction between settlement patterns and road infrastructure performance. When integrated with traffic, safety, and speed data, these indicators can underpin the development of advanced tools for intelligent infrastructure management. Future efforts should focus on expanding the geographic application, refining predictive potential, and incorporating real-time data sources—contributing to safer, more adaptive, and sustainable mobility networks.