Road geometry is a modifiable determinant of crash occurrence and severity; addressing it is critical for achieving sustainable transport systems. Yet, policy action requires clear prioritization across road types and years to ensure sustainable resource allocation. This study analyzes fatal and injury outcomes by roadway geometric context in Türkiye (2015–2024) and proposes a cell-level prioritization framework integrating crash burden, severity, and short-term deviations to support long-term sustainable road safety management. Annual data were structured as Year × Road type × Geometry × Category, with severity measured as deaths and injuries per 100 crashes (Kmin = 30). A Road Geometry Severity Index (RGSI; 0–100) combined standardized severity, log crash burden, and deviation from a three-year baseline. Isolation Forest and a MAD-based rule identified anomalies, while K-means clustering (K = 4) revealed burden–severity profiles. Results show deaths per 100 crashes declined from 7.91 (2015) to 3.29 (2022), then rose to 6.22 (2024). Severity was highest on provincial (8.82) and state roads (7.23), compared to motorways (4.66). High-severity cells were dominated by provincial-road contexts, especially dangerous curves and junction-related categories. The highest-priority cell was 2018–Provincial Road–Junction–No Junction (RGSI = 100). Under the predefined contamination specification (γ = 0.05), the Isolation Forest model flagged 35 anomalous cells, all of which also satisfied the MAD-based anomaly criterion. Findings highlight persistent high-priority roadway geometric contexts and demonstrate the potential of RGSI as a transparent infrastructure-prioritization tool. 1. Introduction Road traffic injuries remain a major and unevenly distributed global public health burden. The World Health Organization (WHO) estimates that approximately 1.19 million people die each year due to road traffic crashes, with road traffic injuries being the leading cause of death among children and young adults aged 5–29 years [ 1]. In the WHO Global Status Report on Road Safety 2023, the global death toll is similarly estimated at ~1.19 million deaths (2021), underscoring that progress has been modest relative to the scale of preventable harm [ 2]. Recent burden assessments also emphasize that road traffic injuries remain a leading cause of death and disability among young populations, particularly in low- and middle-income settings where rapid motorization and constrained trauma care can compound the burden of severe road traffic injuries [ 3]. Against this background, contemporary road safety frameworks increasingly emphasize upstream, system-level interventions capable of preventing high-severity events before they occur. Global road safety efforts increasingly frame prevention through a “Safe System” logic: deaths and serious injuries are not inevitable by-products of mobility but predictable outcomes of interacting risks across roads, vehicles, speeds, and road users [ 2]. This policy orientation is reinforced by internationally recognized targets to substantially reduce road traffic deaths and injuries, as well as by national strategy documents that adopt aligned goals and monitoring structures [ 4]. In line with this broader policy orientation, Türkiye’s “Road Traffic Safety Strategy Document (2021–2030)” and its associated action plans establish a national framework aimed at substantially reducing road traffic fatalities and serious injuries while institutionalizing inter-agency coordination, implementation, and monitoring mechanisms [ 4]. From a measurement perspective, this national context is also important because Türkiye’s official road-traffic fatality statistics have adopted a 30-day definition for crash-related deaths since 2015 [ 5]. First, traditional hotspot screening approaches are inherently reactive and highly localized, relying strictly on spatial coordinates to identify high-frequency collision points. While valuable, coordinate-based hotspot analysis often suffers from the regression-to-the-mean phenomenon and tends to mask systemic, infrastructure-wide design vulnerabilities. The RGSI framework shifts this paradigm from a spatial coordinate perspective to a structural geometric stratum perspective (road-type and geometry-category strata). By evaluating safety performance at the geometric stratum cell level, RGSI isolates how specific engineering configurations (such as horizontal curvature thresholds or unsignalized junction categories) systematically amplify injury severity across the entire national network, regardless of a single spatial point. Second, unlike standard composite risk indices that rely on static, linear weighting systems, the RGSI is structurally multi-dimensional and dynamic. It simultaneously balances three distinct mathematical layers: the absolute log-scaled crash burden, the normalized internal severity (fatalities and injuries normalized per 100 crashes), and an operational trend component that computes short-term severity deviations from a moving three-year rolling baseline. This prevents the index from being heavily biased toward high-volume macro highways while remaining highly sensitive to sudden, structural safety regressions in lower-volume provincial networks. Third, conventional safety prioritization approaches typically rank infrastructure based on simple arbitrary thresholds or basic statistical sorting. The proposed framework enhances objective decision-making by coupling the continuous RGSI scoring with an unsupervised machine learning pipeline (Isolation Forest and Median Absolute Deviation rule). This allows the model to systematically detect statistical anomalies in geometric contexts where safety performance has degraded relative to historical baselines, offering highway authorities a transparent and predictive tool for targeted infrastructure asset management and proactive safety audits. More broadly, customized composite metrics are increasingly adopted in complex analytical domains when conventional single-indicator measures cannot adequately represent multidimensional risk structures or operational decision priorities. Within this policy and prevention framework, road infrastructure and geometric context emerge as especially important modifiable determinants of crash occurrence and crash severity, making them central to engineering and policy prioritization. While conventional road safety paradigms heavily emphasize behavioral components such as driver error, distraction, or non-compliance, modern proactive safety frameworks recognize that infrastructure status plays a primary, foundational role in shaping and constraining driver behavior itself. Elements such as roadway geometric context, lane configurations, and pavement surface conditions directly dictate driver perception, workload, and speed selection, thereby acting as the structural root cause of behavioral failures. Horizontal curvature is a particularly prominent example: FHWA notes that horizontal curves account for more than 25% of fatalities and that the average crash rate on curves is roughly three times higher than on tangent segments, motivating targeted countermeasure programs [ 6]. Recent methodological studies have also leveraged connected-vehicle and trajectory-based data to improve the identification of high-severity run-off-road crash conditions on horizontal curves, further emphasizing the importance of curve-focused safety assessment approaches [ 7]. Evidence from applied safety research similarly shows that curve characteristics (e.g., sharp radius, short length, and spatial arrangement) can be associated with materially higher high-severity crash burden, reinforcing the need to identify and prioritize problematic geometric contexts [ 8, 9]. Beyond curvature, junction and intersection design is another high-leverage domain where geometric form can shape conflict patterns and injury severity [ 10]. For instance, converting conventional intersections to roundabouts has been associated with large reductions in fatalities and injuries in meta-analytic and before-after evaluations [ 10, 11]. Recent empirical studies have further confirmed that specific structural configurations, such as the number and type of lanes or the presence of median barriers, exert a profound, statistically significant impact on crash severity, demonstrating that self-explaining and forgiving infrastructure design can mitigate human error [ 12]. At the same time, the safety consequences of geometric context can be heterogeneous, interacting with traffic operations, environment, and driver behavior, suggesting that “one-factor-at-a-time” summaries may miss meaningful, actionable patterns across combinations of road type and geometry [ 13, 14]. Analytically, road safety research has increasingly leveraged machine learning (ML) to model injury severity and to extract patterns from high-dimensional crash records. A recent systematic review and meta-analysis (2014–2025) highlights the rapid expansion of ML and deep learning for crash injury severity prediction while also pointing to substantial heterogeneity in modeling choices, evaluation metrics, and reporting practices [ 15]. Applied studies and technical reports similarly document the use of supervised ML approaches for crash severity classification and unsupervised methods for grouping or hotspot identification [ 16, 17]. However, supervised prediction is often challenged by structural issues such as class imbalance and low fatality rates, especially when fatal outcomes constitute a small fraction of all crashes, potentially limiting predictive stability and interpretability for policy prioritization [ 18]. These limitations are especially relevant when the available data are annually aggregated and organized around road-type and geometry categories rather than individual crash-level microdata. From a sustainability-engineering perspective, proactive identification of high-severity infrastructure contexts may support resilient transportation systems, optimized engineering-resource allocation, and long-term reduction of societal losses associated with severe traffic crashes. More broadly, recent sustainability-oriented engineering studies have similarly emphasized the integration of technical optimization with human-centered and resource-conscious system design approaches [ 19]. Accordingly, complementary approaches that emphasize prioritization and pattern discovery may be more useful than individual-level prediction in such settings. In practice, decision-makers often require interpretable outputs that (i) identify where severe outcomes cluster across infrastructure contexts, (ii) flag unusual changes relative to recent baselines, and (iii) translate complex strata into actionable priority lists. Türkiye-specific road safety work likewise underscores the importance of characterizing contributing factors and vulnerable road user outcomes using available administrative statistics and contextually meaningful strata [ 20]. Accordingly, this study aims to (i) quantify temporal and structural variation in fatal and injury outcomes across road types and road-geometry strata in Türkiye (2015–2024), and (ii) develop a cell-level prioritization framework that integrates crash burden, severity, and short-term deviations to support targeted road safety action, complemented by unsupervised anomaly detection and clustering for interpretable severity profiling. 2. Materials and Methods 2.1. Study Design and Reporting Framework The methodological aim is two-fold: (i) to quantify temporal and structural variation in crash severity across geometry and road-type strata; and (ii) to derive a cell-level prioritization score (RGSI) integrating crash burden, severity, and recent deviations, complemented by unsupervised anomaly detection and clustering. All computational steps are deterministic given the input files, with fixed random seeds for stochastic procedures, and the intermediate analysis tables used to generate the reported results and figures were exported as versioned outputs to enable independent replication. This study is a retrospective, repeated cross-sectional analysis of annually aggregated road-traffic crash outcomes stratified by road type and road-geometry categories over 2015–2024. The overall conceptual flow and operational stages of the proposed experimental framework are systematically presented in Figure 1. 2.2. Data Sources and Structure Annual datasets were obtained from the official General Directorate of Highways (KGM) for the years 2015–2024. Two structured tables were extracted for each year: Geometry table (Table 1 in each annual file): counts of crashes, deaths, and injuries disaggregated by road-geometry domains (e.g., horizontal alignment, vertical alignment, junctions, crossings, other) and their categories, further stratified by road type (e.g., motorway, state road, provincial road, connector road) and a “TOTAL” column. Driver-fault table (Table 2 in each annual file): counts and percentages of driver-fault categories, stratified by road type and “TOTAL”. The unit of analysis for geometry-based modeling is a cell defined as: Cell = (yeart, roadtyper, geometrydomaind, categoryc). In the present study, the term “roadway geometric context” is used broadly to include horizontal alignment, vertical alignment, junction, crossing, and related roadway-environment categories contained within the administrative database. All inferential and ML components were conducted at this cell level. 2.3. Data Extraction and Preprocessing Because the annual spreadsheets use multi-row headers and merged cells, raw sheets were imported without predefined headers and transformed into a tidy (long) format through deterministic parsing rules: (i) header rows were reconstructed by forward-filling merged header cells; (ii) measurement columns were generated by combining road-type labels with metric labels; and (iii) section headers (geometry domains) were detected as rows with missing values across all numeric columns and forward-filled to subsequent category rows. All preprocessing operations were executed through a deterministic pipeline to eliminate manual intervention and ensure full reproducibility of the final analytical dataset. Intermediate cleaned datasets, harmonized category mappings, and reconstructed annual tables were version-controlled and exported prior to statistical analysis. 2.4. Text Normalization and Category Harmonization All string fields were standardized by applying whitespace normalization and canonical label harmonization to ensure consistent category definitions across years. Non-data entries embedded within the table body (e.g., source/footnote rows) were excluded prior to analysis. Geometry categories were analyzed within their respective domains only, and cross-domain aggregation was intentionally avoided because multiple geometry-context classifications may simultaneously apply to the same crash event. 2.5. Handling of “TOTAL” Rows Two datasets were maintained: Detailed dataset: all non-TOTAL category rows (used for cell-level analyses, RGSI, anomaly detection, and clustering). TOTAL dataset: TOTAL rows (used for reporting aggregated annual totals when complete). Geometry-domain categories were not aggregated to reconstruct annual totals because multiple geometry contexts may simultaneously describe the same crash event. Annual datasets covering the period 2015–2024 were obtained from the official traffic accident statistics database published by the General Directorate of Highways (KGM), Republic of Türkiye. Each year was provided as a separate spreadsheet file containing road-type-specific and geometry-based crash statistics, fatality counts, and injury counts. These mutually exclusive road-type categories included Motorway, State Road, Provincial Road, Connector Road, and Controlled-Access Highway groups as defined in the administrative tables. 2.6. Outcome Definitions and Derived Severity Metrics In the present study, severity refers to aggregate crash-conditional casualty severity at the cell level rather than individual crash-level injury severity classification. Accordingly, the proposed severity measures represent aggregated fatalities and injured persons normalized by the number of reported crashes within each analysis cell. For each cell (t,r,d,c), the following observed counts were extracted: K t , r , d , c = n u m b e r o f c r a s h e s , D t , r , d , c = n u m b e r o f d e a t h s , I t , r , d , c = n u m b e r o f i n j u r i e s . Two primary severity metrics were derived to summarize crash severity [ 21]: D e a t h s p e r 100 c r a s h e s t , r , d , c = D t , r , d , c K t , r , d , c ୍ଠ 100 , I n j u r i e s p e r 100 c r a s h e s t , r , d , c = I t , r , d , c K t , r , d , c ୍ଠ 100 . Because the administrative datasets report total casualty counts rather than binary crash outcomes, a single crash may involve multiple fatalities and/or multiple injured individuals. Consequently, deaths or injuries per 100 crashes may exceed 100 in high-severity cells. In aggregated roadway-context cells with relatively small crash counts but severe multi-casualty events, injuries per 100 crashes may therefore reach unusually large values. 2.7. Inclusion Threshold for Cell-Level Modeling To reduce instability from very small denominators, cell-level analyses that depend on severity rates (RGSI, anomaly detection, and clustering) were restricted to cells with: K_(t,r,d,c) ≥ K_min, where the primary specification used K_min = 30 crashes per cell. 2.8. RGSI: Road Geometry Severity Index (Cell-Level Prioritization) The RGSI score was designed as a reproducible prioritization index combining: (i) severity (deaths per 100 crashes), (ii) burden (crash volume), and (iii) deviation from recent historical baseline (trend sensitivity). All transformations and scaling steps were performed strictly within the analysis dataset used to compute RGSI. 2.9. Winsorization for Visualization-Stable Severity Input Because deaths per 100 crashes can be extreme for some cells even under K_min, we applied percentile-based winsorization and used the winsorized severity measure as the input for RGSI and for visualization in Figure 2D [ 22]: S t , r , d , c ( w ) = m i n m a x S t , r , d , c , q 0.01 ( S ) , q 0.99 ( S ) , where S_(t,r,d,c) denotes deaths per 100 crashes and q_p (S) denotes the empirical p-quantile of S over the RGSI analysis set. Raw (non-winsorized) severity rates were retained for tabular reporting. 2.10. Burden Transformation Crash burden was represented using a logarithmic transform: B t , r , d , c = l o g ( 1 + K t , r , d , c ) . 2.11. Deviation from Recent Baseline For each fixed stratum (r,d,c), a trailing baseline was computed from the prior three years (excluding the current year): S ପ୍ତ t − 1 , r , d , c ( w ) = m e a n S t − 1 , r , d , c ( w ) , S t − 2 , r , d , c ( w ) , S t − 3 , r , d , c ( w ) , with a minimum of two prior observations required; otherwise, the deviation term was set to zero. The deviation component was then: Δ t , r , d , c = S t , r , d , c ( w ) − S ପ୍ତ t − 1 , r , d , c ( w ) . When insufficient historical observations were available for a stable baseline estimation, the deviation component was conservatively set to zero to avoid introducing unstable temporal effects into the RGSI calculation. 2.12. Standardization Each component was standardized over the RGSI analysis set using z-scores: Z ( X ) = X − μ X σ X , where μ_X and σ_X are the mean and standard deviation of X in the analysis set. 2.13. RGSI Construction and Scaling Conventional road safety indicators based solely on crash frequency or fatality counts may not sufficiently capture the combined influence of severity, crash burden, and temporal instability across heterogeneous geometric contexts. Accordingly, the RGSI framework was designed as a composite prioritization metric intended to integrate multiple dimensions of infrastructure-related crash severity burden into a unified and interpretable decision-support structure. The raw RGSI score was defined as a weighted linear combination: R G S I t , r , d , c ( r a w ) = w S Z S t , r , d , c ( w ) + w B Z B t , r , d , c + w Δ Z Δ t , r , d , c , with primary weights (w_S, w_B, w_Δ) = (0.5, 0.3, 0.2). The final RGSI was linearly rescaled to a [0, 100] range: R G S I t , r , d , c = 100 ୍ଠ R G S I t , r , d , c ( r a w ) − m i n ( R G S I ( r a w ) ) m a x ( R G S I ( r a w ) ) − m i n ( R G S I ( r a w ) ) . The primary weighting structure was intentionally designed to emphasize crash severity while still incorporating crash burden and temporal deviation components. Greater weight was assigned to severity because the framework aims to prioritize high-consequence infrastructure contexts for safety intervention. These weights should therefore be interpreted as a pragmatic prioritization configuration rather than a universally optimal weighting scheme. 2.14. Unsupervised Anomaly Detection Two complementary anomaly definitions were used to address common reviewer concerns regarding robustness and method dependence. 2.15. ML Anomaly Detection (Isolation Forest) An Isolation Forest model was fitted to the feature vector using the scikit-learn (v1.8.0) library in Python with n_estimators = 300, contamination = 0.05, and random_state = 42 [ 23]: x t , r , d , c = S t , r , d , c ( w ) , Δ t , r , d , c , B t , r , d , c , Cells predicted as outliers were flagged as ML anomalies: M L A n o m t , r , d , c ∈ { 0,1 } . The model decision score was retained as a continuous anomaly score to enable ranked sensitivity checks. 2.16. Robust Statistical Anomaly Rule (Sensitivity/Verification) A robust z-score was computed via the median absolute deviation (MAD) for (i) S^((w)) and (ii) Δ [ 22]: R Z ( X ) = 0.6745 X − m e d i a n ( X ) M A D ( X ) , M A D ( X ) = m e d i a n | X − m e d i a n ( X ) | Cells were flagged as robust anomalies if: R Z Δ t , r , d , c > 3.5 or RZ S t , r , d , c w > 3.5 . This second definition was reported to demonstrate that ML-flagged anomalies were not artifacts of a single algorithmic choice. 2.17. Clustering (Severity-Profile Segmentation) To segment cells into distinct burden–severity profiles, K-means clustering was applied to standardized features: z t , r , d , c = s c a l e S t , r , d , c w , I n j u r i e s p e r 100 c r a s h e s t , r , d , c , B t , r , d , c , where scale indicates standardization to zero mean and unit variance. The primary specification used K = 4 clusters with n_ init = 20 and random seed fixed at 42. Cluster profiles were summarized using cluster-level means of crashes, severity metrics, mean RGSI, and anomaly rates. 2.18. Descriptive Outputs and Figure Construction All primary descriptive tables were generated deterministically from the analysis dataset: (i) annual totals and severity ( Figure 2A), (ii) road-type severity comparisons ( Figure 2B), (iii) RGSI distribution with anomaly overlay ( Figure 2C), and (iv) severity–burden scatter with cluster and anomaly encoding ( Figure 2D). For Figure 2D, the y-axis uses the winsorized severity input S^((w)) defined in Equation (3) to preserve readability; raw severity values remain available in the exported tables and were used for tabular reporting. 2.19. Sensitivity Analyses To address expected reviewer critiques regarding thresholding, algorithmic assumptions, and parameter dependence, the following sensitivity analyses were prespecified: 1. Minimum crash threshold: repeat RGSI/anomaly/clustering using K_ min∈{20,50,100} and compare stability of top-ranked RGSI cells. 2. Winsorization range: repeat using (q 0.02, q 0.98) and no winsorization, reporting the effect on Figure 2D readability and RGSI ranking stability. 3. Isolation Forest contamination: repeat ML anomaly detection with γ∈{0.02,0.05,0.10} and report overlap with the robust anomaly rule. 4. Clustering choice: repeat clustering for K∈{3,4,5}; report cluster-profile robustness and reassignment stability of RGSI top decile cells. 5. Annual totals completeness: confirm that annual crash totals derived from summing road types (excluding TOTAL) match TOTAL columns for years where TOTAL is complete, and document exceptions (notably 2018 and 2022). All sensitivity analyses used identical preprocessing and inclusion rules, differing only in the specified parameter under evaluation. Particular attention was given to the stability of top-ranked RGSI cells across alternative parameter specifications to evaluate the robustness of the prioritization framework. Overall, the sensitivity analyses suggested that the highest-priority RGSI cells remained broadly stable across alternative parameter specifications, indicating that the overall prioritization structure was not driven by a single modeling assumption or parameter choice. Although minor variations in ranking order and anomaly counts were observed, the dominant high-priority geometry contexts were consistently preserved across all tested configurations. 2.20. Reproducibility and Software All preprocessing, metric computation, RGSI scoring, anomaly detection, and clustering were implemented in a fully reproducible pipeline. The computational environment used Python 3.14.3 and the following key libraries: pandas 3.0.1, NumPy 2.4.3, scikit-learn 1.8.0, Matplotlib 3.10.8, and seaborn 0.13.2. All random seeds were fixed to ensure computational reproducibility, and identical preprocessing rules were applied across all annual files. The complete analytical workflow was designed to produce deterministic outputs from the raw source spreadsheets. 2.21. Data Quality and Consistency Controls To ensure consistency across years, all annual datasets were subjected to harmonization and validation procedures, including category reconciliation, duplicate control, header reconstruction, and verification of annual totals against detail-level strata. Missing or incomplete TOTAL fields were reconstructed from disaggregated road-type counts using identical aggregation rules across all years. 3. Results and Discussion 3.1. Data Coverage and Structure The dataset covered 2015–2024 (10 annual files). For the road-geometry module, the data included 6 road-type columns, 5 geometry domains, and 39 geometry categories after excluding “TOTAL” rows. The driver-fault module comprised 21 fault categories ( Table 1). 3.2. Temporal Pattern of Crash Severity Across 2015–2017, deaths per 100 crashes were 7.91, 7.56, and 7.78, respectively. In 2018, total crashes were 514,640, deaths 18,520, and injuries 547,115, corresponding to 3.60 deaths per 100 crashes and 106.31 injuries per 100 crashes. In 2019–2021, deaths per 100 crashes ranged between 6.55 and 6.60, while in 2023 it was 6.71 and in 2024 it was 6.22. In 2022, total crashes were 428,200, deaths 14,075, and injuries 410,705, corresponding to 3.29 deaths per 100 crashes and 95.91 injuries per 100 crashes ( Table 2; Figure 2A). The marked shifts observed in 2018 and 2022 should be interpreted cautiously, as these years also corresponded to incomplete TOTAL crash fields in the original administrative datasets, requiring reconstruction from detailed road-type strata. The reconstruction procedure used mutually exclusive road-type totals only and did not aggregate geometry-domain rows. Accordingly, the observed reductions in deaths per 100 crashes during 2018 and 2022 may partially reflect administrative completeness limitations rather than genuine temporal declines in crash severity. 3.3. Differences by Road Type (2015–2024 Aggregated) When aggregating across 2015–2024, deaths per 100 crashes varied substantially by road type. Provincial roads reported 297,780 crashes and 26,250 deaths (8.82 deaths per 100 crashes). State roads reported 1,707,135 crashes and 123,505 deaths (7.23 deaths per 100 crashes). Motorways reported 406,100 crashes and 18,940 deaths (4.66 deaths per 100 crashes). Connector roads reported 474,465 crashes and 1630 deaths (0.34 deaths per 100 crashes) ( Table 3; Figure 2B). 3.4. Highest-Severity Geometry–Road Combinations (Crash Count ≥ 30) These severity indicators should be interpreted as casualty-based rates normalized by crash counts rather than probabilities bounded by 100%. Extremely large injury-per-crash values observed in certain cells are attributable to the aggregated casualty structure of the administrative database rather than duplicated crash records or preprocessing inconsistencies. 3.5. Composite-Index Prioritization and Unsupervised Anomaly Screening Anomaly detection was evaluated using two approaches: (i) Isolation Forest (ML) and (ii) a robust z-score rule. Under the predefined contamination specification (γ = 0.05), corresponding to approximately 5% expected anomalies, the Isolation Forest model flagged 35 cells as anomalous. All 35 of these model-flagged cells were also identified by the robust anomaly rule. Clustering (K = 4) separated cells into distinct burden–severity profiles. The cluster with the highest mean RGSI (Cluster 1; n = 15) had a mean deaths-per-100-crashes of 113.75 and an anomaly rate of 1.00. A higher-burden cluster (Cluster 3; n = 41) had a mean crash count of 1391.15 and an anomaly rate of 0.463, while the largest clusters had near-zero anomaly rates (Cluster 0: 0.00; Cluster 2: 0.003) (cluster profile; Figure 2D). 3.6. Road-Type Differences and Why Provincial-Road Strata Dominate High-Severity Cells 3.7. Horizontal Alignment and “Dangerous Curve” Cells as Recurrent High-Priority Contexts 3.8. Junction-Related Contexts and the Role of Intersection Design While our dataset is aggregated and does not permit causal attribution to specific intersection treatments, the junction-related concentration among high-RGSI cells ( Table 5) is consistent with the broader intersection-safety evidence base and suggests that geometry-aware screening should include junction contexts alongside curve contexts in infrastructure prioritization programs. 3.9. Why Unsupervised ML (Rather than Supervised Prediction) Is a Defensible Choice Here 3.10. Robustness of Anomaly Signals and Why a Dual Definition Helps 3.11. Interpretation of RGSI as a Prioritization Tool (and Why Winsorization Is Appropriate for Visualization) 3.12. Türkiye-Specific Context and Data-Coverage Considerations This study presents a systematic evaluation of road-traffic crash outcomes across various road-geometry and road-type strata in Türkiye from 2015 to 2024. By transitioning from traditional macro-level frequency reporting to a micro-level, data-driven approach, we developed and applied a comprehensive evaluation framework that integrates the Road Geometry Severity Index (RGSI) with unsupervised machine learning and anomaly detection techniques. The empirical results indicate that traffic fatality and injury outcomes vary substantially across different roadway geometric contexts, with particular vulnerability observed in provincial and secondary road networks where geometric consistency may vary. Thus, this methodology offers a systemic approach to managing road safety by converting massive, multi-year statistical accident datasets into highly local, geometry-specific prioritizing strategies. From a practical standpoint, the contributions of this study provide actionable insights for highway agencies and road safety practitioners. By identifying systemic anomaly patterns across specific geometric strata, this framework moves beyond traditional, reactive hot-spot analysis. It allows authorities to deploy targeted engineering countermeasures and optimize resource allocation directly where structural road design (such as horizontal curvature bounds or complex junction contexts) amplifies crash injury severity. However, certain practical limitations must be acknowledged. First, the empirical findings are bounded by the granularity of the historical accident records (2015–2024), which may contain inherent underreporting variations across different regional administrative classes. Second, while the study establishes a robust Road Geometry Severity Index (RGSI), it focuses primarily on static geometric and administrative strata, without integrating real-time environmental factors or microscopic behavioral dynamics like instantaneous vehicle speeds. Looking toward future prospects, these limitations map out clear avenues for subsequent research. The future framework aims to incorporate micro-level traffic flow data and weather anomalies into the unsupervised machine learning pipeline to establish dynamic, proactive safety-condition forecasting models. Furthermore, scaling this methodology into predictive infrastructure asset management software will facilitate real-time safety audits, allowing planners to mitigate high-severity patterns during the geometric design phase before conflicts materialize on the field. Therefore, the proposed framework should be viewed as a decision-support and screening tool intended to guide further engineering assessment rather than as a standalone causal prioritization mechanism. Overall, the proposed framework contributes to sustainability-oriented transportation engineering by integrating infrastructure safety prioritization, proactive safety assessment, and data-driven decision support within a resilient mobility perspective. Author Contributions H.B.T.: Conceptualization; Methodology; Data curation; Formal analysis; Visualization; Writing—original draft; Writing—review & editing. F.Y.: Conceptualization; Methodology; Writing—review and editing; Supervision. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. 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Methodological flowchart of the proposed four-stage road safety framework. Figure 1. Methodological flowchart of the proposed four-stage road safety framework. Figure 2. Descriptive outputs: ( A) annual crash severity trends; ( B) road-type severity comparison; ( C) RGSI distribution with anomaly overlay; and ( D) burden–severity clustering visualization. Figure 2. Descriptive outputs: ( A) annual crash severity trends; ( B) road-type severity comparison; ( C) RGSI distribution with anomaly overlay; and ( D) burden–severity clustering visualization. Table 1. Data coverage. Table 1. Data coverage. Item Value Year range 2015–2024 Number of files 10 Number of road types (Geometry) 6 Number of geometry domains (Geometry) 5 Number of categories (Geometry, excluding TOTAL) 39 Number of categories (Driver faults) 21 Table 2. Annual totals and severity. Table 2. Annual totals and severity. Year Crashes Deaths Injuries Deaths per 100 Crashes Injuries per 100 Crashes 2015 262,475 20,755 557,485 7.91 212.40 2016 262,070 19,800 549,195 7.56 209.56 2017 263,140 20,465 550,335 7.78 209.14 2018 514,640 18,520 547,115 3.60 106.31 2019 227,835 14,920 483,405 6.55 212.17 2020 191,790 12,650 361,010 6.60 188.23 2021 219,065 14,360 411,670 6.56 187.92 2022 428,200 14,075 410,705 3.29 95.91 2023 267,850 17,965 513,795 6.71 191.82 2024 270,230 16,815 497,085 6.22 183.95 Table 3. Road-type totals (2015–2024 aggregated). Table 3. Road-type totals (2015–2024 aggregated). Road Type Crashes Deaths Injuries Deaths per 100 Crashes Injuries per 100 Crashes Provincial Road 297,780 26,250 834,850 8.82 280.36 State Road 1,707,135 123,505 3,960,960 7.23 232.02 Motorway 406,100 18,940 40,370 4.66 9.94 Connector Road 474,465 1630 45,620 0.34 9.62 TOTAL 1,964,455 170,325 4,935,600 8.67 251.25 Table 4. Top 10 highest-severity cells (crashes ≥ 30). Table 4. Top 10 highest-severity cells (crashes ≥ 30). Year Geometry Domain Category Road Type Crashes Deaths Injuries Deaths per 100 Crashes Injuries per 100 Crashes 2018 Horizontal alignment Dangerous curve Provincial Road 44 126 4036 286.36 9172.73 2023 Horizontal alignment Dangerous curve Provincial Road 56 99 2674 176.79 4775.00 2024 Horizontal alignment Dangerous curve Provincial Road 55 76 2580 138.18 4690.91 2018 Junction Roundabout State Road 218 278 9778 127.52 4485.32 2022 Horizontal alignment Dangerous curve Provincial Road 46 55 2093 119.57 4550.00 2018 Junction No junction Provincial Road 511 542 15,466 106.07 3026.61 2018 Junction Four-way Provincial Road 39 39 1102 100.00 2825.64 2018 Vertical alignment Inclined Provincial Road 215 203 6831 94.42 3177.21 2018 Other None Provincial Road 677 602 18,712 88.92 2763.96 2018 Crossings No crossing Provincial Road 716 626 19,050 87.43 2660.61 Note: Severity measures are casualty-based aggregates normalized by crash counts. Because individual crashes may involve multiple injured persons and/or fatalities, injury and fatality rates may substantially exceed 100 in some high-severity aggregated cells. Table 5. ML prioritization (RGSI): Top 10 cells. Table 5. ML prioritization (RGSI): Top 10 cells. Year Road Type Geometry Domain Category Crashes Deaths Deaths per 100 Crashes RGSI ML Anomaly 2018 Provincial Road Junction No junction 511 542 106.07 100.00 1 2018 Provincial Road Vertical alignment Inclined 215 203 94.42 96.61 1 2018 Provincial Road Other None 677 602 88.92 94.45 1 2022 Provincial Road Horizontal alignment Dangerous curve 46 55 119.57 93.30 1 2018 Provincial Road Crossings No crossing 716 626 87.43 93.16 1 2018 Provincial Road Horizontal alignment Dangerous curve 44 126 286.36 93.11 1 2023 Provincial Road Horizontal alignment Dangerous curve 56 99 176.79 85.21 1 2018 Provincial Road Horizontal alignment Straight 510 400 78.43 83.41 1 2018 Provincial Road Vertical alignment Level 517 401 77.56 82.70 1 2018 Provincial Road Junction Three-way (T) 45 36 80.00 78.49 1 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Share and Cite MDPI and ACS Style Tosun, H.B.; Yavuz, F. Road-Geometry Severity Index for Prioritizing High-Severity Crash Contexts in Turkey: A Composite-Index and Unsupervised Learning Approach. Sustainability 2026, 18, 5697. https://doi.org/10.3390/su18115697 AMA Style Tosun HB, Yavuz F. Road-Geometry Severity Index for Prioritizing High-Severity Crash Contexts in Turkey: A Composite-Index and Unsupervised Learning Approach. Sustainability. 2026; 18(11):5697. https://doi.org/10.3390/su18115697 Chicago/Turabian Style Tosun, Hümeyra Bolakar, and Fatih Yavuz. 2026. "Road-Geometry Severity Index for Prioritizing High-Severity Crash Contexts in Turkey: A Composite-Index and Unsupervised Learning Approach" Sustainability 18, no. 11: 5697. https://doi.org/10.3390/su18115697 APA Style Tosun, H. B., & Yavuz, F. (2026). Road-Geometry Severity Index for Prioritizing High-Severity Crash Contexts in Turkey: A Composite-Index and Unsupervised Learning Approach. Sustainability, 18(11), 5697. https://doi.org/10.3390/su18115697 Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details . Article Metrics Article metric data becomes available approximately 24 hours after publication online.
