Search Results (1,853)

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. 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

The increasing adoption of reclined seating postures in modern vehicle interiors challenges the assumptions underpinning current passive safety systems and occupant protection assessment frameworks. While restraint technologies and certification protocols have historically been developed for upright configurations, emerging trends in autonomous driving and comfort-oriented designs promote relaxed postures that fundamentally alter occupant kinematics, loading path, and consequently the injury mechanisms. This review critically synthesizes experimental and numerical studies addressing occupant biomechanics, restraint system performance, and injury risk in reclined seating. Evidence from crash tests using Anthropomorphic Test Devices and Post-Mortem Human Surrogates, alongside high-fidelity numerical Human Body Models, is analyzed to identify consistent trends and methodological limitations. The results highlight increased forward excursion, elevated submarining propensity, and posture-dependent abdominal and lumbar loading as critical consequences of increased seatback recline. Furthermore, this review discusses the effectiveness of adaptive restraint strategies, including active repositioning and modified airbag–belt integration. By identifying existing research gaps and regulatory limitations, this work aims to provide a roadmap for the development of future safety systems that ensure robust protection for all occupants in the era of automated mobility. Full article

Mini-roundabouts are increasingly implemented as compact, low-cost alternatives to conventional roundabouts and signalized intersections, especially at low-speed, space-constrained urban locations where safety is a concern. Their design emphasizes speed management, reduced conflict severity, and operational simplicity, contributing to safer mobility for all road users. This paper reviews U.S., German, and UK design guidelines and synthesizes empirical safety evidence from before-and-after studies of mini-roundabout conversions. In terms of design, the U.S. practice typically relies on a single large design vehicle and more permissive geometry, whereas the German guidance adopts a multi-vehicle approach with tighter curvature and stronger compactness to enforce lower speeds, affecting crash risk and driver behavior. The UK guidance is distinguished by its flush or slightly domed central marking and flexible application approach. Conversions from two-way stop-controlled (TWSC) or one-way stop-controlled (OWSC) intersections yield substantial reductions in injury and severe crashes, with total crash reductions of 17–42%. Conversions from all-way stop-controlled (AWSC) intersections present more variable outcomes, including increases in total crashes, because drivers are still reacting based on the previous control and may not adjust their expectations quickly. Modular roundabouts are also examined as alternative compact interventions for constrained or high-risk sites, with early evidence indicating reductions in severe crashes and improved speed control while minimizing construction costs and right-of-way impacts. Full article

Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R² ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes. Full article

Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a transparent nine-stage reconciliation pipeline applied to 51 years (1975–2025) of national HRGC incident data from the Federal Railroad Administration Form 57 and Form 71 datasets. The hierarchical pipeline integrated deterministic alignment and multistage inference methods to produce an audited, geographically consistent dataset. The study formalizes four longitudinal county-level cumulative exposure indices that characterize spatiotemporal patterns of incident concentration relative to static population and infrastructure denominators. These metrics include accumulated incidents per million population (AIPM), accumulated incidents per crossing (AIPC), crossings per million population (CPM), and crossings per 100 square miles (CPHSM). All four metrics exhibited pronounced right-skewness: AIPM, CPM, and CPHSM approximated exponential forms, and AIPC approximated a log-normal form. Statistical tests detected statistically significant tail deviations in three metrics; CPM did not reject the exponential fit at conventional significance levels. Spatial analysis shows coherent regional concentration in incident rates in the Central Plains and lower Mississippi corridors. The national time series exhibits a late-1970s plateau, sustained exponential decline beginning around 1980, and stabilization but persistent incident rates after 2001. Population-normalized AIPM remained statistically indistinguishable between the reconciled and record-dropped datasets; however, crossing-based metrics changed materially when reconstructing denominators from the reconciled crossing universe. Statistical comparisons confirmed that incident-only denominators introduced substantial measurement bias in local risk assessment. State-level rank reversals persisted even when omnibus distributional tests failed to reject equality. By formalizing multistage data cleaning and quantifying its analytical impact over an unprecedented longitudinal horizon, this study establishes denominator integrity and geographic reconciliation as prerequisites for valid HRGC exposure assessment and provides a framework for future predictive modeling. 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

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile Regression (QQR) framework, we analyze the risk transmission mechanisms across the conditional distribution of stock returns. The empirical results reveal a notable regime-dependent reversal: a negative influence is exerted by geopolitical risk during a bullish market regime, while a counterintuitive positive association is present for the bearish market conditions. This effect is more pronounced for emerging and commodity-rich markets, which may provide a potential hedge during supply-side shocks. Moreover, the QQR analysis focused on the United States of America stock market provides an examination of the different potential transmission mechanisms of geopolitical variants. The results suggest that geopolitical threats (GPRT) represent a persistent factor that negatively affects the market for normal and bullish market regimes, while geopolitical acts (GPRA) represent a tail-risk catalyst that exacerbates losses during severe market crashes. The results remain robust to an alternative specification of returns and indicate the necessity of distinguishing between geopolitical acts and threats from a risk management standpoint, as well as correctly identifying the market regime. 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

Autonomous battery electric vehicles (BEVs) have the potential to reshape urban mobility systems, yet their sustainability impacts remain underexplored in Gulf-region cities where traffic dynamics, land-use structures, and environmental conditions differ substantially from Western contexts. This study introduces a Saudi-specific assessment framework that integrates monetised externalities with empirically calibrated traffic dynamics to evaluate how automation influences safety, congestion, land use, emissions, and noise. To the best of our knowledge, this is the first Riyadh-calibrated monetised external-cost evaluation of autonomous BEVs that couples externality valuation with simulation-validated time-varying traffic dynamics (SAR per vkm and SAR per pkm), enabling realistic peak-period sustainability assessment. The framework’s key contribution is linking external-cost modelling with spatiotemporal traffic behaviour derived from Riyadh’s 2023 mobility patterns, providing a more realistic basis for sustainability evaluation. Using national datasets from transport, energy, and statistical authorities, the model estimates substantial reductions in external costs when transitioning from human-driven to autonomous BEVs, driven primarily by lower crash exposure and smoother traffic flow. To validate these findings under real operating conditions, a dynamic analysis incorporating hourly and seasonal traffic variability was developed, revealing that automation delivers its strongest improvements during peak-demand periods where congestion externalities are highest. The integrated results demonstrate the relevance of autonomous BEVs for dense rapidly growing Saudi cities and provide actionable insights for future mobility planning. The study highlights the policy importance of coordinated transport, land-use, and energy strategies to ensure that automation contributes meaningfully to national sustainability goals under Vision 2030. 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

Until now, there have been no published attempts to utilize ensemble learning approaches to pre-crash velocity estimation. In this research article, we focus on the method of vehicle crash velocity prediction based on the random forest regression approach. In particular, the study aims to develop and validate a random forest-based non-linear model for estimating pre-crash velocity using EES-related parameters for compact vehicles in a crash scenario against an immovable, stationary barrier. The estimation technique is trained and evaluated using the compact vehicle class from the NHTSA database, which consists of 399 records of frontal impacts against a rigid barrier. The relative error obtained for the presented calculation method is $7.57\mathrm{\%}$, with absolute error being equal to $1.12 \mathrm{m}/\mathrm{s}$. We subsequently compare our results with some other techniques which were tested on this dataset. Despite the simplicity of random forest regression, we obtain surprisingly good results, as the method outperforms linear regressor and artificial neural network predictors, which have relative errors of $8.17\mathrm{\%}$ and $9.63\mathrm{\%}$, respectively. The independence of Event Data Recorders along with the ease of obtaining the necessary data makes the proposed approach a highly desirable tool in forensic analysis, especially in cases involving older vehicles. Full article

Over the past decade, road safety research has experienced rapid development due to the rapid expansion of large crash databases, the adoption of artificial intelligence techniques, and the demand for proactive and predictive safety solutions. This study conducts a data-driven review of recent research trends in transport safety. It focuses on main domains including crash severity analysis, human factors, vulnerable road users (VRUs), spatial modeling, and artificial intelligence applications. A systematic search of the Scopus database identified 15,599 relevant scientific papers published between 2016 and 2025. After constructing this corpus, titles, abstracts, and keywords were preprocessed using a natural language pipeline. The analysis employed BERTopic, a transformer-based topic modeling framework. The analysis identified 29 distinct research topics, further synthesized into five major thematic areas: (1) crash severity and injury analysis, (2) driver behavior and human factors, (3) vulnerable road users, (4) artificial intelligence, machine learning, and computer vision in intelligent transportation systems, and (5) spatial analysis and hotspot detection. A notable increase in publications related to artificial intelligence and machine learning has been evident since 2020. The results show a transition from descriptive, post-crash studies to integrated, multimodal, predictive analysis. Overall, the findings reveal a paradigm shift in the field. This study also identifies ethical and economic issues associated with the use of artificial intelligence in intelligent transportation systems, including data management, infrastructure requirements, system security, and model transparency. The results signify a transition from intuition-based models to explainable, spatially explicit, and data-intensive models, ultimately facilitating proactive risk assessment and informed decision-making. Full article