Commercial fishing remains one of the most hazardous occupations worldwide, with high fatality rates and complex incident dynamics, making the joint understanding of incident type and fatality frequency critically important. This study addresses the limitation of conventional approaches that model these outcomes independently by proposing a copula-based joint modeling framework. Specifically, a Copula-Based Poisson-Mixed Multinomial Logit Model is developed to simultaneously analyze five categories of vessel incidents and associated fatality counts using vessel-level data derived from the Commercial Fishing Incident Database. The approach integrates a multinomial logit model for incident type with a negative binomial model for fatality frequency, linked through alternative copula structures to capture unobserved dependence. Empirical results indicate that the negative binomial specification provides the best fit for fatality counts, while the Student-t copula outperforms Gaussian and FGM alternatives, achieving the highest AIC and BIC values and revealing strong dependence between outcomes. Key determinants include vessel characteristics, human factors, and operational conditions, with drowning and crew exposure emerging as dominant contributors to fatalities. The findings demonstrate that accounting for dependence significantly improves model performance and yields more reliable inference, offering valuable insights for safety management and policy interventions in commercial fishing operations.
Traffic Conflict Techniques (TCTs) estimate safety proactively by identifying critical vehicle interactions that could have led to a crash. Existing TCTs constrained by ideal assumptions of interaction points and constant current speed often fail to consider the influence of neighboring vehicle density. The key challenge associated with incorporating the heterogeneity and disordered nature of traffic in estimating TCTs lies in varying vehicle dynamics and the collision scenarios. This study models a dynamic two-dimensional surrogate safety approach called the Vehicle Safety Envelope (VSE), which is the elliptical safety boundary around a vehicle that is required for safe and comfortable maneuvering through traffic. VSE is then utilized as a spatial filter to identify and classify vehicle-vehicle conflicts using trajectory data from five signalized intersections in India. Results showed that lateral and longitudinal safety clearance distance, the key parameters for defining VSE, were found to have a linear dependency on vehicle size and speed, indicating that at a higher speed, a larger VSE is required to maintain the same level of safety. The VSE approach was validated through comparison with two well-established surrogate safety measures, namely Modified Time to Collision and Deceleration to Avoid Crash, along with a Severity Index (SI) developed to quantify conflict risk. Smaller vehicles such as motorised two-wheelers and motorized three-wheelers were found responsible for a larger proportion of side-swipe conflicts. These results emphasize the dynamic nature of VSE, demonstrating its potential for proactive conflict estimation. The VSE could enhance safety perception for decision modules if implemented in an autonomous driving environment.
To identify key factors that exacerbate crash severity under adverse weather conditions such as rain, snow, and fog, and support effective crash prevention, this study develops a hybrid analytical framework combining the Gradient Boosting Decision Tree (GBDT) model, the interpretable Shapley Additive Explanations (SHAP) framework, and a Random Parameters Logit Model with Heterogeneity in Means (RPL-HM) using crash data from Tianjin, China (2018-2023). The results indicate that fog significantly affects the likelihood of injury crashes, with substantial heterogeneity in means. A visibility-lighting composite indicator is proposed, and the findings show that insufficient lighting exacerbates the likelihood of severe accidents under low visibility. Road classification and crash participants also have a significant impact on crash severity. Factors such as highways, crashes involving both motor vehicles and pedestrians, speeding, and failure to yield are all significantly associated with a higher occurrence of severe crashes. The hybrid modeling framework and findings provided in this paper offer a theoretical foundation and practical guidance for formulating targeted road traffic safety management policies under adverse weather conditions, contributing to a reduction in both the frequency and severity of crashes in such environments.
Truck drivers exhibit significant individual variability in their responses to in-vehicle alarms, which critically impacts road freight safety. This study aims to dynamically assess truck driving styles by integrating alarm-response behaviors and exploring their seasonal variations. We established a comprehensive framework utilizing 20 indicators that encompass vehicle operations, driving behaviors, and alarm responses based on real-world GPS and alarm data. A K-means++ clustering algorithm coupled with an XGBoost (Extreme Gradient Boosting) model was developed to classify and identify driving styles across four seasons, while SHAP (SHapley Additive exPlanations) was employed to interpret feature impacts globally. The proposed approach demonstrated robust predictive performance across all seasons (Accuracy > 0.94). Results identified six distinct driving styles: Responsive-Agile, Risk-Negligent, Conservative-Delayed, Nocturnal-Adapted, Overreactive, and Trajectory-Unstable. Notably, individual drivers exhibit significant seasonal transitions in their styles; for instance, alarm responses tend to be slower in spring and summer but faster in autumn and winter. Furthermore, SHAP analysis revealed that initial response time, speed decay ratio, and nighttime driving intensity are critical determinants in style classification. These findings provide freight companies with actionable insights for targeted safety training and dynamic risk management.
This study proposes a vehicle collision risk field model that represents risk values based on collision probability and uses the vehicle's rectangular contour as the risk field boundary. The model matches the distance that would be traveled over the time-to-collision (TTC) at the current relative speed with the statistical or driver-perceived probability of a collision, so that the calculated risk value can be consistent with the actual collision probability or the driver's perceived probability of collision. Constructing the risk field based on the rectangular contour also allows a more accurate representation of the vehicle's planar shape. Additionally, for vehicles traveling stably in different lanes and gradually approaching each other longitudinally, the model resolves the misjudgment of oblique collision risks common in traditional elliptical-frustum risk fields. Moreover, an adaptive mechanism for adjusting risk sensitivity was introduced. Finally, the study presented application demonstrations of the collision risk field in a human-driving simulator lane-change experiment and in a simulated lane-change scenario for an autonomous vehicle. The proposed model and its application demonstrations can provide a reference for the application of risk field models in human-driving and autonomous-driving scenarios.
Truck driver fatigue remains a critical safety concern on U.S. highways and is exacerbated by constraints in accessing appropriate rest opportunities. Prior studies largely emphasize public rest areas, while private truck stops, despite providing substantial parking supply, are less often examined at a corridor scale. This study uses corridor-based descriptive mapping and negative binomial regression to assess associations between truck parking characteristics and fatigue-consistent CMV crash counts along Florida freight corridors (2019-2023). Segment-level crashes were linked with parking inventory and Truck AADT, with exposure and segment length included in log form. Results indicate that Truck AADT is strongly associated with higher expected crash counts (elasticity = 0.30, p < 0.001), and segment length is also positively associated (elasticity = 0.50, p < 0.001). After controlling these factors, public parking spaces and a facility preference index (overnight availability/fuel/24-h operation) were not statistically significant, while private parking capacity was positively associated with fatigue-consistent crashes (p = 0.005). Findings highlight that exposure dominates corridor-level fatigue-consistent crash occurrence and that parking-supply measures, especially private capacity, should be interpreted in terms of corridor demand and effective accessibility, not nominal capacity alone.
Driving automation is increasingly being deployed in real-world traffic environments, yet empirical evidence on how automation engagement shapes crash severity remains limited. Using crash data from 2024 and 2025, this study examines factors associated with crash severity under different levels of driving automation. The dataset includes 18,413 automation-engaged crashes, with automation levels ranging from Society of Automotive Engineers (SAE) Level 1 to Level 5. AutoGluon, an automated machine learning (AutoML) framework was employed to model crash severity separately for lower-level (SAE 1-2) and higher-level (SAE 3-5) driving automation, with model interpretation conducted using SHapley Additive exPlanations (SHAP) and permutation importance. Results indicate that harmful event type, collision configuration, and object struck are the dominant predictors of crash severity across automation levels, while roadway geometry, environmental conditions, and person-level demographics play secondary but meaningful roles. Comparisons across automation levels reveal differences in the relative importance and functional effects of roadway and contextual factors. In addition, a qualitative analysis of crash narratives involving fully automated vehicles identified recurring patterns related to maneuver execution failures, lane-keeping issues, and interactions with human-driven vehicles, despite the absence of injury outcomes in these cases. The results inform policy decisions on infrastructure readiness, operational design domain management, and safety oversight for automated driving systems.
Work zones remain critical hotspots for crashes due to rapidly changing geometries, temporary traffic control, and heterogeneous data sources. Traditional safety modeling approaches often rely on centralized data, which limits scalability and raises privacy concerns. Federated learning (FL) offers a promising alternative by enabling decentralized model training while preserving data security. This study investigates FL as a decentralized modeling paradigm for dynamic work zone safety. The primary objective is to operationalize an FL-based framework capable of leveraging heterogeneous transportation data while preserving data locality. The proposed framework is demonstrated through proof-of-concept implementations involving three work zone-relevant components: visual perception of work zone elements, driver behavior modeling using naturalistic trajectory data, and contextual awareness derived from structured work zone information. For the perception component, FL is implemented across decentralized visual data sources to illustrate node-level integration through federated aggregation, while the behavioral and contextual components are evaluated using conventional modeling approaches for framework compatibility. Results indicate that FL can achieve performance comparable to or exceeding centralized baselines while improving scalability, modularity, and privacy preservation. The findings highlight FL as a viable foundation for adaptive and privacy-aware work zone safety modeling.
The resilience of urban roadway network is crucial for maintaining effective mobility during disruptions. However, traffic safety has received limited attentions in the domain of resilience assessment. The study introduces the concept of safety resilience, which integrates traffic safety consideration into resilience research. Unlike traditional resilience that primarily focuses on traffic efficiency metrics, safety resilience is defined as a network ability to maintain and restore safety performance under disturbances by evaluating real-time traffic risk. The study presents the evaluation of safety resilience based on traffic conflicts at intersections and on roadways and explores the impact of different traffic control measures, including lane control, signal control, and connectivity improvement, on both traffic safety and efficiency. The results demonstrate that the proposed Safety Resilience Index (SRI) provides an effective means to assess network safety resilience by capturing the trade-off between safety and efficiency. Lane restrictions are found to be more suitable for road segments with high safety risk, while lane widenings are more effective in mitigating severe congestion. Signal control can enhance safety resilience but increase travel time and rear-end conflicts on roadways. Although connectivity improvement can theoretically enhance resilience from the topological perspective, it may result in worse overall network safety and efficiency. These findings underscore the need for a balanced approach that integrates both safety and efficiency in enhancing urban network resilience.
The rapid growth of road freight has led to an increasing number of trucks, where their long braking distance, large mass, and limited maneuverability make them particularly vulnerable to safety risks, especially at high-risk nodes such as tunnels, bridges, and step grades. These risks are generated at the microscopic level but accumulate and propagate along the entire trip, creating the need for a modeling framework capable of capturing both local safety mechanisms and system-level traffic evolution. To address this need, this study proposes a multiscale hybrid traffic flow model that integrates an improved Cellular Automata (CA) model for detailed characterization of accident-prone segments and a Cell Transmission Model (CTM) for long-distance freight transport. Two transition areas are designed to ensure consistent information exchange between the discrete CA and continuous CTM domains. Using naturalistic driving data for calibration and validation, the hybrid model accurately reproduces truck-car interaction patterns and system-level traffic dynamics. The results show that vehicle heterogeneity and different vehicle combinations lead to distinct car-following behaviors and safety outcomes, as reflected in surrogate safety measures such as Time Headway and Time-to-Collision. Although trucks tend to adopt more cautious strategies, truck-truck interactions still exhibit the highest risk levels due to reduced maneuverability and accumulated disturbance. These findings provide actionable insights for freight operations and road safety management, supporting the development of targeted regulations and truck-specific safety strategies at high-risk locations.
Arterial wrong-way crashes (AWWCs) pose a safety problem on Florida roadways, yet most wrong-way driving research focuses on limited-access facilities with less attention on arterials. This article applies a corridor approach to examine AWWCs in Central Florida, identify arterial WWD hotspots, and demonstrate the transferability of this approach from South Florida to Central Florida. Arterial corridors were defined using context classification and through lane count, resulting in 1,475 corridors with 2,522 AWWCs. Negative binomial and logistic regression models used corridor variables to identify factors influencing AWWC frequency and probability. Both models revealed that corridors in suburban and urban contexts, urban principal arterials, urban minor arterials, rural principal arterials, corridors with higher signalized intersection densities, and one-way corridors exhibited elevated AWWC risk, while corridors with higher vegetation median proportions demonstrated reduced risk. Using model predictions, 47 corridors were identified as high-risk hotspots by both models. These shared hotspots were typically long, high-volume corridors with many signalized intersections, low vegetation median proportions, and were concentrated in Orange and Hillsborough Counties. These findings confirm that the corridor approach is transferable beyond South Florida and can provide agencies with a practical tool for screening roadway networks and prioritizing corridors for proactive safety treatments.
At tunnel entrances and exits, the abrupt transition in cross-sectional and lighting conditions poses substantial safety risks, which are further exacerbated in spiral tunnels due to their coupled radius-slope design. This study proposes an improved artificial potential field-based risk modeling framework that integrates geometric alignment (radius and slope), lighting conditions, driver characteristics, and vehicle dynamics, enabling more precise quantification of driving risk under the combined influence of multiple factors. Using three alignment design schemes from a real-world engineering project as prototypes for the spiral tunnel, driving simulation experiments were conducted to systematically quantify and compare driving risks at the spiral tunnel portals across different radius-slope combinations. Results show that radius and longitudinal slope near tunnel portals significantly affect driving risk. A small radius coupled with a steep slope markedly increases entrance risk. Compared with open-road sections, internal tunnel zones exhibit a higher risk, and the 100 m before the exit generally presents a greater risk than the 100 m after the entrance. This study clarifies the mechanisms underlying risks at spiral tunnel portals under complex alignment and environmental conditions, and provides theoretical and practical guidance for improving alignment design and mitigating portal risks.
In the context of China's new-type urbanization, rural roads have become high-risk traffic environments due to land-use changes, mixed traffic composition, and lagging infrastructure. Using crash data from five rural roads in Beijing during 2023-2024, this study constructed meso-level units by integrating macro-level urbanization characteristics with micro-level road attributes. Urbanization level was introduced as a moderating variable, and a negative binomial-conditional autoregressive (NB-CAR) model incorporating interaction terms was developed to investigate the factors influencing traffic crash frequency and the underlying mechanisms in urban-rural transitional areas. The results show that, compared with low urbanizing areas, moderately urbanizing areas are significantly positively associated with crash frequency, whereas highly urbanizing areas are significantly negatively associated with crash frequency. In addition, the interaction effects between the presence of central medians and urbanization level, as well as between segment length and highly urbanizing areas, indicate that crash risk arises from the combined influence of multiple factors rather than from any single factor. The study further suggests that traffic safety infrastructure and management capacity in moderately urbanizing areas often lag behind rapid traffic growth. These areas should therefore be prioritized in proactive risk governance, with systematic crash reduction pursued through fault-tolerant design and shared safety governance.
With the rise of intelligent driving and human-machine codriving, effective yet minimally intrusive risk prompts have become crucial for road safety. While important groundwork on prompt effectiveness has been laid in prior work, there is still an opportunity to build on this foundation and further investigate the joint parametric optimization of frequency, duration, and lead time. In extending prior efforts in scene classification, a need has emerged for the development of data-driven, quantitative approaches to urgency quantification that complement the existing methods. We conducted a within-subject driving simulator study with 40 drivers across nine risk scenarios. Using baseline physiological and behavioral data from a no-prompt control, we constructed an objective urgency index (UI) and evaluated how prompt-parameter combinations affected driving, eye movements, and physiology. The P2-D1-L3 setting (two prompts, 1 s duration, 3 s lead time) was Pareto-optimal, as it improved lateral stability while preserving longitudinal control. Lead time was the dominant factor, and it outperformed frequency and duration. Prompting produced the greatest benefits in medium-urgency scenes, taking an inverted U-shaped moderation effect. Mediation analysis indicated that improved lateral control was driven primarily by physiological arousal, which was indexed by pupil dilation. These findings quantify the optimal parameter boundaries and support the fine-grained design of risk-promotion HMIs in intelligent vehicles.
Roundabouts, serving as critical nodes in urban transportation networks, the analysis of conflicts between motorized vehicles (MVs) and non-motorized vehicles (NMVs) is complex. Existing research has failed to adequately consider the impact of trajectory characteristics on conflict analysis modeling and has ignored the multidimensional mapping relationship between conflict levels and micro-level traffic behavior, resulting in the simplification of traditional conflict indicator classification. Therefore, this study introduced Circular Trajectory Entropy (CTE), Radial Deviation Index (RDI) and Curvature Change Rate (CCR) to measure the randomness of MV and NMV trajectories, and used a multi-class logistic model to quantify the impact of micro-characteristics and trajectory fluctuations on the severity of conflicts. Statistical results showed that MVs mainly adopted conservative deceleration behavior in response to potential conflicts, whereas NMVs exhibited higher path randomness and greater trajectory fluctuations at roundabouts. A logistic regression model based on multi-feature fusion performs exceptionally well in analyzing minor and severe conflicts. When using SHAP plots to reveal key influencing factors, it was found that CCR was the most influential feature in distinguishing between minor and severe conflicts. These findings can be used to evaluate the severity of conflicts between MVs and NMVs at signal-free roundabouts.
According to the World Health Organization, road traffic accidents contribute to approximately 1.3 million deaths annually, posing a global public safety challenge. Current deployment strategies for traffic police face difficulties in balancing accident prevention and real-time response, leading to inefficiencies. This study proposes a two-phase framework for traffic police dispatch, integrating pre-deployment and dynamic emergency response. In the pre-deployment phase, a deep learning model using accident data from Yinzhou District, Ningbo City (April 2020 to October 2021) identifies high-risk zones-defined as statistically significant spatial clusters of high accident frequency-for police positioning. The dynamic dispatch phase utilizes a real-time optimization algorithm based on the golden hour (<= 10 min) to reduce response times. Experimental results show a 28.71% improvement in average response time (from 5.54-7.21 min to 4.52 min) and an 85.43% police utilization rate, outperforming baseline methods. This approach improves response efficiency and enhances officer utilization. The framework demonstrates potential scalability, offering insights for improving road safety in urban settings with similar characteristics.
