Search Results (308)

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

Mobile edge computing storage is increasingly used to support immersive services and Internet of Things applications that generate continuous real-time data streams. Sustained availability must therefore be maintained under both abrupt failures and software aging. Prior studies often evaluate reactive mechanisms (e.g., failover and live migration) and preventive mechanisms (e.g., software rejuvenation) separately, so their combined effect in microservice-based distributed edge storage is still unclear. We develop a Stochastic Reward Net (SRN) model for a multi-node edge storage architecture that captures hardware and software failures, software aging, high availability, live migration, and rejuvenation at both node and microservice levels. Using the model, we compare six policy scenarios and quantify Capacity-Oriented Availability COA), defined as the expected number of usable microservices while the storage layer is operational. Steady-state and sensitivity analyses over twelve timing parameters show that policies including live migration achieve the highest, or effectively tied-highest, COA across wide ranges of failure and repair rates. They also show that uncoordinated rejuvenation schedules can reduce availability when rejuvenation starts before live migration completes and terminates services prior to evacuation, a phenomenon we refer to as a Proactive Crash (PC). Across the tested ranges, edge/storage failure rates and rejuvenation trigger intervals dominate availability, while detection delays, repair times, and rejuvenation duration have a smaller influence. These results give guidelines for configuring proactive high availability so that migration completes before rejuvenation and rejuvenation is neither too frequent nor too sparse. Full article

Crash hotspot analysis has been widely studied in road traffic safety. Conventional approaches primarily rely on the spatial density or frequency of crash locations but fail to capture vehicle traversal patterns and segment-level exposure. In addition, when detailed traffic volume data are unavailable, it becomes difficult to assess risk while accounting for road exposure. In particular, Network Kernel Density Estimation (NKDE) is sensitive to bandwidth selection and remains limited in representing exposure-normalized, path-consistent risk at the road-segment level. To overcome these limitations, this study proposes a path-based risk segmentation framework that integrates crash paths with simulation-based exposure. Origin–crash coordinate pairs are extracted from crash reports, and vehicle paths are reconstructed over a road network. Monte Carlo simulation is used to estimate a relative exposure proxy across road segments and combine it with path-derived traversal patterns to compute segment-level risk. A case study in Daejeon Metropolitan City demonstrates that the proposed method addresses key limitations of NKDE by yielding more coherent risk segments and improving path alignment, and it identifies high-risk segments more effectively than the conventional NKDE baseline, particularly under small top-$\alpha \%$ selection ratios, as measured by the path-based hit rate. This study provides a new perspective on crash risk analysis by shifting from point-based to path-based interpretation and by explicitly normalizing risk with an exposure proxy under data-limited conditions. It offers a practical framework for identifying high-risk segments at the road network level. Full article

In January 2025, New York City became the first major U.S. city to implement a cordon-based congestion pricing policy via the Central Business District Tolling Program. While the policy’s effects on traffic volume are well-documented, its impact on road safety remains underexplored. This study evaluates the short-term effects of the program on two distinct metrics: total crash counts (frequency) and injury rates (severity, defined as the number of persons injured per 10,000 residents), using a monthly panel dataset of ZIP code-level data from January 2024 to December 2025. We employ a rigorous multi-method causal inference framework—including difference-in-differences, matched difference-in-differences, and generalized synthetic control—to estimate changes in injury rates and total crash counts independently. Across all empirical specifications, we find no statistically significant reduction in either traffic injuries or collisions following the policy’s implementation. Event study analyses confirm a consistent null effect month-over-month, with no transient or sustained safety dividend. Subject to short-term methodological constraints, our findings suggest that congestion pricing functions primarily as a demand management tool; realizing immediate road safety benefits in complex urban grid networks likely requires complementary physical infrastructure interventions. Full article

School zones in the United States require compliance of drivers to decreased speed limits during school start and end times, typically indicated by flashing beacons and warning signs. This study examined school zone safety by evaluating the effects of speed limit differentials on driver speeds in active school zones, the influence of roadway characteristics on driver behavior, and crash costs associated with school zones. The main goal was to attain a sustainable school zone safety policy. The analysis used speed observations from 378,506 vehicles, school and roadway characteristics, and crash data (2014 to 2018) across 18 study sites. Results showed that 85th-percentile speeds often exceeded posted speed limits during both active and passive school zone periods, with greater non-compliance being associated with larger speed limit differentials. Driver speeds were influenced by school zone status, vehicle type, time of day, traffic signals, street parking, and crosswalks. On average, speeds were 6.2 mph higher during passive periods than during active periods. However, high crash rates were observed during active school zone periods. Crashes during active periods resulted in average crash costs that were 52.5% lower than those during passive periods. The findings provide insights into human factors and mobility behavior in school zones, allowing transportation agencies to make informed and sustainable decisions for school zone design and safety. Full article

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next $\tau =0.2$ s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor ($w=d=0.1$ s, $k=2$) is warnable on only $18.3\%$ of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget $\alpha$ with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at $\tau =0.2$ s, the scorer achieves AUPRC $\approx 0.251$ against a base rate of $0.638\%$, AUROC $\approx 0.986$, and ECE $\approx 0.034$. After calibration at $\alpha =5\%$, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR_run, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported $\mu$-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used. Full article

Within the continuous development of automotive safety and increasingly stringent crash regulations under the Vision Zero initiative, physical crash testing remains essential for assessing occupant injury risk. This study focuses on the evaluation of occupant dynamics in full-overlap frontal collisions, based on real crash tests. Key parameters influencing injury severity, including impact speed, seat belt usages, and occupant anthropometry, were analyzed to identify worst-case scenarios. Frontal crash test protocols from regulatory and consumer programs were included in the analysis. Physical tests were conducted according to FMVSS 208 using Hybrid III 50th percentile male and 5th percentile female dummies. Both belt-restrained and unrestrained (unbelted) conditions were considered. Numerical simulations using LS-DYNA are used as a complementary tool to support and extend the interpretation of the experimental findings, particularly in assessing the influence of impact speed, seat belt usage, and occupant anthropometry on injury metrics. The results evaluate the factors with the greatest impact on injury risk and demonstrate the importance of physical frontal crash tests in the evaluation of the occupant protection. All experimental tests were carried out at IAV Vehicle Safety. Full article

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The proposed method in this research involves the bidirectional Long Short Term Memory (LSTM), Convolutional Neural Network with LSTM (CNN–LSTM), and transformer models. The model is furthermore tuned using random or grid search. For the pedestrian–vehicle scenario, the CNN–LSTM model achieved 99.76% accuracy, 99.77% precision, and 99.76% recall, highlighting its strong classification performance. In the vehicle–motorcyclist scenario, the bidirectional LSTM reached 99.73% accuracy with precision and recall of 99.15%, demonstrating its effectiveness in detecting imminent crashes. The optimized CNN-LSTM by random search has focused on decreasing the false-positive rate and increasing the positive rate. It has achieved superior results compared to previous research. These results suggest that the system could be effectively implemented as an early collision warning solution on edge devices. Full article

Background/Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC) are complex multisystem conditions with significant functional disability. Many patients experience symptoms of orthostatic intolerance, which can be captured in some cases as Orthostatic Hypotension (OH) or Postural orthostatic Tachycardia Syndrome (PoTS) on objective testing. Conservative treatments are recommended for first-line symptom management, but there is a lack of efficacy evidence. This study aims to assess the feasibility of an 8-week clinically supervised, personalised Dysautonomia Management Protocol (DMP) in a cohort of ME/CFS and LC patients with subjective and objective evidence of orthostatic intolerance (dysautonomia). Methods: ME/CFS and LC patients with objective dysautonomia on the 10 min active Lean Test (LT) were recruited to an 8-week DMP, with interventions introduced cumulatively every two weeks. Interventions included increasing daily fluid intake to 3 litres and salt intake to 10 g, pacing to avoid crashes and calf activation. Baseline and weekly data collection included the LT, Composite Autonomic Symptom Score questionnaire (COMPASS-31) and Yorkshire Rehabilitation Scale (YRS). Results: Sixteen participants completed the 8-week program, five discontinued during the program, and one was withdrawn following a severe crash. The COMPASS-31 improved by 7.7 points from week 1 to week 8 (p = 0.045), with a medium Cohen’s d effect size of 0.55. For the same period, there was a non-significant (p = 0.16) improvement in the YRS symptom severity score by 2 points. Comparing the final two weeks of the program with the first two weeks, mean heart rate during the LT decreased by 4.8 beats per minute (p = 0.032), with a medium Cohen’s d effect size of 0.44. Adherence to the interventions was highly variable, with none of the patients able to fully employ all four recommendations. Conclusions: The results suggest that targeted conservative interventions could influence autonomic function and symptom reduction. However, the magnitude of change was limited, and statistical significance might not necessarily relate to a clinically significant improvement in symptoms. Full article

This paper offers a hybrid forecasting approach that merges a local-level state space Kalman filter with a Long-Short-Term Memory (LSTM) neural network to assess the downside risk of the Botswana Pula versus the US Dollar (BWP/USD). Inspired by the inability of conventional econometric models to capture complex latent structural shifts and nonlinear patterns, our architecure uses a bottom-up hierarchical methodology in which the smoothed level component of the exchange rate is isolated by the Kalman filter and subsequently fed into the LSTM architecture. Three key indicators for assessing downside risk—Maximum Drawdown (MDD), Conditional Drawdown-at-Risk (CDaR), and Downside Deviation—are used to assess model performance across various time-frames (7, 30, 90, 180, and 240 days). As confirmed by Kupiec and Christoffersen’s backtesting processes, the findings show a high degree of alignment between projected and actual values, with negligible downside deviation bias and robust calibration. Moreover, global economic and geopolitical shocks, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the 2015–2016 Shanghai Stock Exchange crash, are important factors that influence exchange rate volatility, according to explainable artificial intelligence techniques, particularly SHAP (SHapley Additive exPlanations) analysis. Downside risk is also greatly increased by regional currency links, especially the impact of the ZAR/BWP exchange rate. On the other hand, domestic temporal variables, such as week, quarter, and month, have very little impact. These results emphasise how Botswana’s currency rate is structurally vulnerable to external shocks and how crucial it is to include both global and regional considerations in risk analysis. The research concludes that the accuracy and transparency of projections for exchange rate risk significantly improve when practical filtering is combined with deep learning and explainable AI. To improve macroeconomic resilience and guide successful financial risk management plans in emerging market environments, policymakers are advised to employ AI-driven forecasting techniques, enhance regional monetary coordination, and set up real-set learning systems. Full article

Cannabis is the most-used psychoactive drug in Aotearoa—New Zealand (NZ); recreational use remains illegal, while medicinal use was legalized in 2020. Cannabis use is associated with increased risk of injury; however, there is little known on the causes and circumstances of cannabis-related fatal injuries. This retrospective population study utilized coronial case files to describe the contribution and circumstances of cannabis-related fatal injuries in NZ. Between 2012 and 2016, cannabis was reported in 273 of 3599 unintentional/assault injury deaths (1.32 deaths per 100,000 person-years, 95% CI 1.17, 1.49). High-risk groups included males aged 15–44 years, Indigenous Māori, and those in deprived areas, for whom higher rates of post mortem testing were conducted. Cannabis-related fatalities mainly resulted from road crashes and multi-drug poisonings with concomitant alcohol use common, especially in traffic crashes on public roads (49% of concomitant use). Cannabis use was mainly observed in the decedent (n = 256, 94%). One in five deaths involved a worker, either as a user or as a bystander to another’s use. Coronial files identified important opportunities for safety countermeasures targeting cannabis use among drivers and its concomitant use with alcohol. Improved coverage of post mortem testing could address data limitations, including biased testing patterns and missing medical use. Full article