Search Results (290)

Road traffic accident consequences are multidimensional, involving fatalities, injuries, and property loss. Existing studies have mainly focused on single outcomes, limiting the understanding of heterogeneous mechanisms across different consequence dimensions. Based on road accident data from Yancheng City in 2022, this study develops an integrated framework combining multi-output prediction, NSGA-II multi-objective optimization, SHAP-based interpretation, LOWESS nonlinear analysis, and DirectLiNGAM causal inference. The results show that the optimized Voting ensemble achieved competitive and comparatively balanced performance across the three accident-consequence dimensions. Road category, traffic control type, junction/road-segment type, and crash-cause category are identified as key influencing factors, with differentiated effects across accident consequences. POI variables exhibit nonlinear and threshold effects, while causal analysis further indicates that road infrastructure and traffic control conditions are positioned upstream in the formation mechanism of accident consequences. This study provides evidence for multidimensional accident-consequence category prediction and differentiated traffic safety management, rather than traditional continuous regression-based modeling. Full article

Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics. Full article

(1) Background: Motorcyclists remain disproportionately represented in road-traffic fatalities and serious injuries worldwide, yet the behavioral factors associated with their crash involvement are still incompletely understood. (2) Methods: This study integrates several established behavioral theories—Human Information Processing (HIP), Reason’s Generic Error-Modelling System (GEMS), the Theory of Planned Behavior (TPB), and Protection Motivation Theory (PMT)—into a single mixed-theory framework in order to examine simultaneously how risk behavior and protective behavior are associated with self-reported motorcycle crash involvement. A cross-sectional survey was administered to 2910 active motorcyclists using a Modified Motorcycle Rider Behavior Questionnaire (MRBQ) to capture four dimensions of risk behavior. (3) Results: A second-order confirmatory factor analysis (CFA) confirmed that the four risk dimensions load onto a single higher-order motorcyclist risk behavior construct, and the full measurement model demonstrated good reliability, convergent validity, and discriminant validity. Structural equation modeling (SEM) showed excellent fit. Motorcyclist risk behavior was positively and significantly associated with crash involvement, whereas protective behavior was negatively associated with it; because protective equipment mainly reduces injury severity rather than preventing crashes, this inverse relationship is interpreted as an indirect association rather than a direct reduction in crash occurrence, and both hypotheses were supported. (4) Conclusions: The findings support the value of integrating error-based and motivation-based theories when modeling motorcyclist safety and highlight the need for generationally tailored interventions that simultaneously reduce risky riding and promote consistent protective behavior. Full article

Urban road-safety agencies increasingly rely on administrative incident registries that contain many events but few fatalities. This study develops a reproducible machine-learning benchmark for rare-event road-traffic fatality prediction using an urban incident registry from Medellín, Colombia. The analysis is framed as a risk-ranking problem rather than as high-certainty binary classification, because fatal outcomes account for less than 1% of the records. The benchmark compares a prevalence-only reference, logistic regression, CART, Random Forest, and XGBoost under a shared preprocessing and time-aware validation design. Historical records are used for training and later observations are held out for testing, reducing temporal leakage and approximating prospective use. Model performance is evaluated with metrics suited to severe class imbalance, including ROC-AUC, PR-AUC, Youden-based threshold summaries, Precision@1%, bootstrap uncertainty intervals, calibration diagnostics, feature-importance analysis, and sensitivity checks. The results show that the available registry variables support risk enrichment but not high-precision fatality classification. The study contributes a transparent baseline for computational road-safety research and clarifies the limits of registry-based prediction when exposure, traffic-flow, roadway, infrastructure, weather, and post-crash response variables are not available. Full article

Road traffic injuries remain a central challenge for sustainable transport, public health, and mobility governance. The task of monitoring these injuries requires indicators that jointly capture harm severity, road and environmental context, and patterns of vehicle involvement at scale. Using harmonised English-language Romanian police crash exports (2023–2025), we build 92,790 records with 36 variables and estimate two complementary k-means typologies: a severity partition based on the fatality, injury, and vehicle-count fields (a register proxy for involvement, not vehicle-type attributes) and a context partition based on the road, environment, mechanism, and cause fields with one-hot encoding and TruncatedSVD. Reported tables and figures reproduce the archived MiniBatch pipeline for replication; for context, full-batch k-means clustering on the same embedding is the recommended default when cross-year prevalence stability is required (train–test TVD 0.039 versus 0.569 under MiniBatch). We report silhouette-guided choices ($k=6$ severity, $k=4$ context), cross-seed stability, feature ablations, and a 2023–2024 versus 2025 prevalence comparison. A Pearson ${\chi }^2$ test on severity × context labels reveals strong statistical significance, yet Cramér’s V remains small—statistical association with limited practical coupling, consistent with complementary rather than redundant partitions. Limitations include police-reported injury counts; a coarse vehicle proxy; weak context geometry; and large MiniBatch context drift, which binds inference to within-year descriptive profiling unless analysts refit the model, add version labels, or adopt full-batch context clustering. The contribution is an integrated, reproducible profiling and governance workflow for dashboards and follow-on modelling—not a fixed multi-year cluster taxonomy. Full article

Despite growing policy support for active travel, the fatality rate of vulnerable road users has remained persistently high in recent years, while the emergence of autonomous vehicles has further increased the complexity of mixed traffic environments. Interactions between cyclists and motorized vehicles are a major contributor to these fatalities, highlighting the urgent need for effective cyclist protection strategies. As one of the most widely adopted active transport modes, cycling safety cannot be assessed solely through crash statistics; understanding cyclists’ perceived safety is equally critical, as it reflects how infrastructure design and dynamic traffic conditions influence cycling behavior. In this study, we propose a cyclist safety perception framework that combines vision–language models with interpretable machine learning to analyze perceived safety in mixed traffic scenarios. A vision–language model is employed to generate semantic descriptions of traffic scenes, while an Explainable Boosting Machine quantifies both individual and interactive contributions of traffic-related features. By integrating visual information with road attributes extracted from OpenStreetMap, the proposed framework achieves a binary safety classification accuracy of 71% and a mean absolute error of 1.01 on a safety score scale ranging from 1 to 9. The results demonstrate the potential of combining multimodal perception and explainable models to support cyclist-centered safety assessment and inform sustainable and intelligent transportation system design. More specifically, the results show that protected cycling infrastructure is the most significant factor in improving perceived safety, whereas road construction has the opposite effect. Full article

Traffic crash severity modeling is an important and promising aspect of road safety research. It aims to assess how key human-, vehicle-, roadway-, and environment-related factors interact to shape severity outcomes of crashes. Existing studies in this regard have predominantly relied on traditional statistical methods and simple machine learning approaches. While statistical analysis techniques are often based on unrealistic underlying assumptions, conventional machine learning models often suffer from interpretability issues. This study proposes an interpretable crash severity prediction framework that combines machine learning and deep learning models with post hoc explainability using SHAP. The research utilizes crash data from a rapidly developing region of Qassim in the Kingdom of Saudi Arabia. Crash severity was classified into three groups: fatal, injury, and property damage only (PDO). Four predictive models were developed and evaluated. These include: Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FFNN), and Gradient-Boosting Machine (GBM). Various performance metrics, including accuracy, balanced accuracy, macro F1-score, and ROC–AUC, were used to assess the model. Descriptive statistical analysis showed that speeding, head-on collisions, wrong-way driving, blown-out tires, and driver fatigue are the major causes of fatal injuries. Empirical results revealed that the proposed prediction models achieved an accuracy ranging between 0.94 and 0.96 for the test data, with the RF model slightly outperforming the other models. Model interpretability analysis indicated that crash severity is significantly influenced by parameters such as crash cause, type, speed, and roadway type. The proposed framework demonstrated the effectiveness of machine learning (ML) and deep learning (DL) approaches for crash severity prediction and provides practical insights to support roadway safety interventions and policy development aimed at reducing severe and fatal crashes. Full article

Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” pattern for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment. Full article

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. Full article

Road safety is a prerequisite for sustainable mobility, yet cyclists remain disproportionately exposed to severe outcomes in mixed traffic. Using police-reported bicycle–vehicle crashes from the national SEWIK registry in Poland (152,567 cyclist-involved records; 2015–2024), this study modeled five ordered injury-severity classes with a CatBoost gradient-boosting classifier, evaluated performance with quadratic weighted kappa and complementary class-sensitive metrics under extreme imbalance (including benchmark comparisons and calendar-based walk-forward stress tests), and interpreted predictions with SHAP to summarize transparent, feature-level association patterns. The results indicate modest overall ordinal discrimination (hold-out QWK ≈ 0.20), while highlighting elevated recall for rare fatal outcomes together with low precision, implying a substantial false-positive trade-off if outputs were used as deterministic classifiers. Global and local explanations point to stronger associations for cyclist age, shorter offender licensure tenure (a registry proxy for experience-related factors), regional context, and built-up versus non-built-up settings consistent with higher kinetic-energy environments; these variables should be interpreted cautiously because registry data are observational and omit key exposures (e.g., measured impact speed and cycling volume). Overall, the study contributes a nationwide, explainable severity-profiling workflow for prioritizing cyclist protection: combining benchmarked ML, multi-metric reporting, and XAI diagnostics can support monitoring and evaluation of speed management, infrastructure, and licensing-system improvements—without overstating causal effects from administrative records alone. Full article

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings. Full article

The paper analyses the economic impact of the reduction in road traffic accidents in Slovakia between 2000 and 2024 and quantifies both direct and indirect costs of road crashes. Over this period, annual crashes declined from more than 50,000 to approximately 11,500 and fatalities from over 600 to 262, demonstrating the effectiveness of national road safety strategies. The methodology is based on the national road accident database, complemented by macroeconomic and demographic indicators, and follows European recommendations for the valuation of external costs of transport. The study applies the value of a statistical life, the value of a statistical life year, the relative severity index and the critical accident rate, with particular emphasis on comparing the value of a statistical life and the relative severity index. The total VSL-based economic costs of road traffic crashes in 2024 are estimated at approximately €1.25 billion, underscoring the scale of the socioeconomic burden. Building on the forecasted values for 2025, the paper further tests and compares these methodologies on a specific road section, illustrating their practical implications for project appraisal and safety management. The results confirm that VSL-based estimates systematically exceed RSI-based estimates by 21–45% per year, reflecting the broader societal costs captured by the VSL concept. The study shows that investments in safety measures are economically worthwhile and reduce the burden on public finances, while also highlighting the need to harmonize methodologies and improve data quality. Full article

Road intersection crashes remain a major contributor to injuries due to complex conflict patterns and multimodal interactions. Among the factors influencing intersection safety, inadequate intersection sight distance (ISD) attributed to roadside sight obstructions can limit drivers’ ability to respond to conflicting movements, potentially affecting crash injury outcomes. Despite its importance, visual obstruction has rarely been examined as a distinct context in traffic crash injury severity modeling. This study investigates crash injury severity at visually obstructed intersections using an ordinal probit modeling framework applied to 951 intersection crashes documented with sight obstruction as a contributing factor in Wyoming over the period 2014 through 2023. Crash data were analyzed to identify the effects of driver behavior, vehicle characteristics, roadway geometry, environmental conditions, and traffic control on ordered injury severity outcomes ranging from property damage only (PDO) to fatal and serious injury. Nonparametric bootstrap resampling with 1000 iterations was employed to assess parameter stability and construct empirical confidence intervals. Average marginal effects were estimated to quantify the change in probability of each injury severity level associated with key predictors. The results indicate that alcohol involvement produces the largest severity shift, reducing the probability of PDO outcomes by 51.2 percentage points while increasing the probability of fatal and serious injury by 34.2 percentage points. Hillcrest grade locations increase fatal and serious injury risk by 14.4 percentage points, while adverse road surface conditions, including snowy, icy, and wet pavements, consistently reduce fatal and serious injury probability by 12.5 to 15.1 percentage points, reflecting behavioral adaptation to visually salient hazard cues. Bootstrap validation confirms strong parameter stability across all estimates, with 94% of parameters showing bootstrap standard errors within 25% of their asymptotic counterparts. By formally establishing visually obstructed intersections as a dedicated severity modeling context and integrating systematic bootstrap validation, this study contributes both substantive and methodological insights to support evidence-based prioritization of intersection safety improvements. Full article

Seatbelt use is well established as a life-saving measure. Nevertheless, many drivers and passengers continue to neglect seatbelt use. This study examines the risks associated with unrestrained occupants involved in motor vehicle crashes. Using data from the Fatality Analysis Reporting System from 2000 to 2018, the relative risk of fatal traffic accidents for unrestrained vehicle occupants in the United States was estimated using the maximum likelihood estimation method. The findings indicate that unrestrained passengers make up about 12% of all passengers on the road and face a roughly 4.3 times greater likelihood of fatality in severe crashes. Additionally, unrestrained drivers, whose higher risk profiles are linked not only to their lack of restraint but also to broader patterns of hazardous driving behavior, account for over 8% of all drivers and exhibit a risk approximately 5.4 times higher in causing fatal crashes compared to restrained drivers. The findings of this study reveal the prevalence and consequences of unrestrained vehicle occupants and supports ongoing efforts to promote seatbelt utilization and bolster road safety protocols. By doing so, we can alleviate the burden of preventable injuries and fatalities on individuals, families, and society at large, thus fostering a safer and more secure transportation environment for all. Full article

Accurately estimating the severity of crash injuries among older drivers is paramount for enhancing traffic safety, a task challenged by class imbalance and label noise. Traditional predictive paradigms often struggle to identify rare severe cases, as they tend to prioritize global accuracy, thereby compromising sensitivity to high-risk outcomes. To overcome these limitations, this study develops a Log-Loss Cleaned and Probability-Calibrated Cascade (L-CSC) framework by strategically integrating existing advanced algorithmic components for robust and reliable severity prediction. Initially, a Log-Loss-based noise filtering mechanism is implemented to purge outliers and ambiguous samples from the training data, thereby enabling higher-quality representation learning. Subsequently, a two-stage cascade architecture is designed to decouple the classification task. Stage I employs a Preliminary Screening Model, optimized via Bayesian optimization for F2-score, to specifically maximize the recall for severe and fatal cases. In Stage II, a Stacking ensemble classifier is deployed to achieve a fine-grained classification of injury levels among the cases identified in the initial screening. Finally, Isotonic Regression is employed to calibrate the output probabilities from both stages, ensuring that the resulting risk estimations are statistically sound and reliable. Empirical evaluations demonstrate that the L-CSC framework effectively balances overall performance with critical risk detection, achieving a robust Macro-F1 of 0.7296. Specifically, compared to the best-performing baseline, the recall and F1-score for the critical severe and fatal category showed relative improvements of over 82% and 62%, respectively. Ablation analyses further substantiate the vital contributions of both the data cleaning and calibration modules. This research demonstrates that the cascaded framework effectively mitigates the biases inherent in imbalanced datasets, providing a robust algorithmic foundation to potentially support future traffic safety interventions. Full article