Maritime administrations frequently implement short-term intensive safety actions to mitigate collision risk in mixed commercial–fishing traffic waters. However, empirical evidence on their effectiveness remains limited because maritime accidents are rare and behavior-level risk indicators are not routinely incorporated into policy evaluation. This study develops an AIS-based evaluation framework that uses monthly near-miss counts as a behavior-level proxy of navigational risk and combines this proxy with a difference-in-differences (DID) design to assess a maritime special safety action in Ningbo–Zhoushan waters. Using large-scale AIS trajectory data, near-miss events are identified based on DCPA and TCPA criteria and then aggregated to the sea area–month level. The analysis covers 9 sea areas from January to December 2023 (108 observations). In the baseline two-way fixed-effects specification, the coefficient on Treat × Post is positive but statistically insignificant (β = 0.4094, SE = 0.2581), indicating that the intervention did not produce robust evidence of a reduction in the AIS-based near-miss indicator. Event-study estimates likewise show no statistically significant persistent dynamic treatment effect within the observation window. These findings suggest that, under the proxy measure and identification strategy used in this study, the special safety action did not generate a clearly identifiable reduction in near-miss counts in treated waters. Methodologically, the study demonstrates the practical value of combining AIS-derived behavioral indicators with quasi-experimental policy evaluation. At the same time, the results should be interpreted with caution, because near-miss counts are structurally related to traffic intensity and traffic composition, and the policy period overlaps with the seasonal fishing moratorium. The proposed framework nevertheless offers a useful basis for evidence-based evaluation of non-engineering maritime safety interventions in complex mixed-traffic environments.
Urban intersections are critical locations in road network where vehicular traffic and pedestrians interact. It results in substandard safety performance due to geometric deficiencies. Traditional safety evaluations depend on historical records solely which suffers systematic under reporting and provide are active frame work. This study used a reactive proactive approach by integrating historical crash data analysis with microscopic traffic simulations and surrogate safety measures (SSMs) to evaluate five high risk intersections in Yeka sub city Addis Ababa. Kotebe K/mihret, kotebe Zero hulet, Kotebe College, Abem and Kara junctions. Intersections were screened and prioritized using the equivalent property damage only (EPDO) scoring method. A microscopic simulation was developed using SUMO and trajectories were analyzed using the surrogate safety assessment model (SSAM) to capture time to collision (TTC) and post encroachment time (PET). Based on identified deficiencies engineering interventions are proposed following AACRA, ERA and AASHTO design standards. The post redesign SSAM evaluation revealed a 36.9% overall reduction in total traffic conflicts. This confirmed that integrating historical crashes with proactive surrogate measures creates a more robust framework for urban traffic safety management. This study concluded that for evaluating urban intersection safety, integrating historical crash analysis with surrogate conflict measures provide more comprehensive and reliable frame work than relying on historical crash records solely.
Endalkchew Zemene· American Journal of Traffic...· 0 citations
Maritime navigation efficiency is commonly assessed using isolated route, speed, energy, or traffic indicators that do not fully represent voyage context. This study proposes a context-aware framework based on GPS–AIS data fusion, planned-route geofencing, metocean information, and encounter-based validation. The Navigation Efficiency Resilience Index (NERI) combines target achievement, trajectory-derived response activity, and disturbance intensity into a bounded, time-resolved diagnostic index. The framework was evaluated using a Singapore–Montevideo container-ship voyage with 30 s position data, surrounding-vessel AIS, corridor-specific cross-track limits, and collocated metocean variables. The voyage-level mean NERI was 0.679, and its 10th percentile was 0.519. Lower values occurred mainly in constrained waters, approach areas, and the metocean-intensive Cape transition, whereas the Indian Ocean and South Atlantic legs achieved higher mean values of 0.704 and 0.736, respectively. For the analysed datasets, the regular own-ship position record produced more stable trajectory-derived indicators than the less regularly sampled own-ship AIS series, without implying an inherent accuracy advantage. The full NERI formulation achieved an AUROC of 0.83 and an AUPRC of 0.41 for CPA/TCPA conflict-window classification. NERI therefore provides a decomposable, plan-relative analytical layer for retrospective voyage monitoring and diagnostics, but it is not a direct safety or collision-risk measure.
Y. Kalinichenko, Andrii Holovan, N.V. Vasalatii et al.· Future Transportation· 0 citations
OBJECTIVE
We analyzed monthly administrative data on road crashes and casualties in Greece for the period April 2010-June 2024 (N = 171), with the aim of supporting operational road safety planning.
METHODS
We applied interrupted time series analysis (ITSA) to separate stable seasonal patterns from the impact of the Corona virus disease 2019 (COVID-19) shock. The main analysis is based on segmented regression (ITSA) with monthly dummies for seasonality, estimated using ordinary least squares (OLS) with Newey-West heteroskedasticity and autocorrelation consistent (HAC) errors, with quasi-Poisson models used as count-data robustness checks. Outcomes include monthly counts of crashes, fatalities, and serious and slight injuries.
RESULTS
Road safety indicators peak in June-August and reach their lowest levels in January-March. In March 2020 there was an abrupt level drop in all outcomes (crashes, fatalities, serious injuries, slight injuries) (followed by only partial recovery). The rebound after March 2020 is more visible for total crashes and slight injuries. For fatalities and serious injuries, the count-data checks do not show the same clear upward pattern. This indicates stabilization at a lower post-COVID-19 baseline for the most severe outcomes. Pre/post comparisons around March 2020 indicate large observed differences relative to pre-pandemic average monthly levels, corresponding descriptively to approximately 7,200 fewer crashes and about 1,070 fewer deaths over March 2020-June 2024. We refer to absolute monthly numbers, which are useful for planning system load. They should not be interpreted as exposure-adjusted individual crash risk, because monthly exposure measures such as vehicle-kilometres traveled (VKT) were not available for the full study period.
CONCLUSIONS
The study is quasi-experimental and describes associations. It does not establish full causal attribution. All data and code are provided as supplementary files.
I. Sitzimis· Traffic Injury Prevention· 0 citations
In maritime accident prevention, it is important to identify not only high-risk sea areas but also which accident-types are most likely to occur there. This study combines survey responses from 826 Korea Coast Guard practitioners with 3856 maritime accidents mapped onto an H3 grid over Korean territorial waters during 2021–2023, and proposes a practitioner-informed framework for predicting accident-type-specific risk. The survey showed limited use of quantitative, standardized accident risk criteria but high demand for AI-based prediction and area-level risk analysis. Practitioners’ perceived accident frequency differed substantially from the empirical accident distribution, whereas their prevention priorities aligned more closely with the actual pattern. Accordingly, this study treats the accident-type taxonomy not as a fixed prediction target but as a design variable of the label space for decision support. A two-stage framework first estimates accident occurrence at the H3 grid-time level and then classifies the accident-type conditional on occurrence. Comparing survey-aligned, data-aligned, union, sufficient-sample, and full administrative (7-class) framings under a common training protocol shows that accident-type organization creates trade-offs among field interpretability, coverage, class granularity, and predictive stability. The study thus reframes maritime accident prediction as an accident-type-specific decision-support problem-linking practitioner perception with empirical evidence.
Dayoung Kim, Won Choi, Seung Sim et al.· Journal of Marine Science an...· 0 citations
This study analyzes road traffic accident risk in Tanzania and introduces a composite Regional Road Accident Risk Index (RARI), defined as accidents per 100 km of road, to compare regional risk and assess progress toward SDG 3.6. A multi-method approach was used: national traffic fatality trends from 2000–2022 were modeled using ARIMA forecasting to 2030; a five-year regional panel of 30 regions from 2018–2022 was examined using fixed-effects Poisson regression; and spatial clustering of RARI values was assessed using GIS and Global Moran’s I. Results show that official fatalities declined markedly from the mid-2010s to 2020 but rose again in 2022–2023, suggesting that Tanzania is unlikely to sustain progress toward the SDG target without renewed interventions. Regression findings indicate that driver-related factors, especially speeding, reckless driving, and negligence, are the strongest predictors of accident counts, while vehicle defects and alcohol-related factors also increase risk. RARI reveals substantial regional disparities, with the national average close to one accident per 100 km annually, the highest-risk urban region reaching about 2.5 accidents per 100 km, and the lowest-risk regions around 0.3 accidents per 100 km. Spatial analysis confirms significant clustering, with high-risk areas concentrated around major urban centers and trunk highways. The study is limited by likely under-reporting in police data, lack of vehicle-kilometres-travelled data, and the short regional panel. Nevertheless, the combined forecasting, regression, and spatial approach provides actionable evidence for prioritizing enforcement, infrastructure improvements, vehicle safety checks, and protection of vulnerable road users in high-risk regions.
S. Hamisi· Journal of Environment, Clim...· 0 citations
Short-horizon collision–risk warning can use instantaneous assessment, forecast-then-assess, or direct prediction from encounter histories. We evaluate the third route with the Anticipatory TRAffic-context Collision–risk Transformer (ATRACT), which estimates the maximum near-future fuzzy Collision Risk Index (CRI) directly from own-ship kinematic and collision-geometry histories without trajectory rollout. Experiments use ten days of Automatic Identification System (AIS) data from the Danish straits, comprising 1.63 million decision windows. Using a vessel-day-track partition and five random initializations, ATRACT attains an area under the receiver operating characteristic curve (ROC-AUC) of 0.814±0.003, compared with 0.808±0.006 for the evaluated forecast-then-CRI baseline (VCRF) and 0.729 for instantaneous CRI. Thresholds fixed at a nominal 5% false-alarm rate (FAR) on disjoint calibration tracks yield test FARs of 4.98% and 5.38% for ATRACT and VCRF. Although seed-0 track-bootstrap intervals include zero at every evaluated offset, ATRACT shows 4.4–10.8 percentage points higher mean pre-onset detection across 0.5–4 min over five random initializations; VCRF detects more individual high-risk windows at this operating point. Four direct-risk encoders achieve a narrow ROC-AUC range of 0.813–0.819. These results support direct temporal interaction modeling as a viable route while limiting conclusions to the evaluated forecast-first implementations.
D. Park, Seunghun Lee, Sangmin Kim· Electronics· 0 citations
