Search Results (401)

Land use significantly influences mobility dynamics, affecting both travel behavior and mode choice. Traditional indicators such as the Floor Area Ratio, Land-Use Mix Index, and Built-up Area Ratio are widely used to describe settlement patterns; yet, they often fail to capture their functional impacts on road networks. This study introduces two complementary indicators—Settlement Ratio (SR) and Settled Area (SA)—developed through a spatial analysis framework integrating GIS data and MATLAB processing. SR offers a continuous typological profile of built-up functions along the road axis, while SA measures the percentage of anthropized land within fixed analysis windows. Applied to two Italian state roads, SS14 and SS309, in the Veneto Region, the dual-indicator approach reveals how the intensity (SR) and extent (SA) of settlement vary across different territorial contexts. In suburban segments, SR values exceeding 15–20, together with SA levels between 10% and 15%, highlight the significant spatial impact of isolated development clusters—often not evident from macro-scale observations. These findings demonstrate that the SR–SA framework provides a robust tool for analyzing land use in relation to road function. Although the study focuses on spatial structure and indicator design, future developments will explore correlations with traffic flow, speed, and crash data to support road safety analyses. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving $R^2$ = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning. Full article

Traumatic liver injury remains a significant contributor to trauma-related morbidity and mortality worldwide. In Saudi Arabia, motor vehicle accidents (MVAs) are the predominant mechanism of injury, particularly among young adults. This study aimed to evaluate the clinical characteristics, management strategies, and outcomes of patients with liver trauma over a ten-year period at a tertiary academic level-one trauma center. A retrospective cohort study was conducted from January 2015 to December 2024. All adult patients (aged 18–65 years) who sustained blunt or penetrating liver injuries and underwent a pan-CT trauma survey were included. Demographic data, Injury Severity Scores (ISSs), imaging timelines, management approach, and clinical outcomes were analyzed. Statistical analysis was performed using JASP software with a significance threshold set at p < 0.05. A total of 111 patients were included, with a mean age of 33 ± 12.4 years; 78.1% were male. MVAs were the leading cause of injury (75.7%). Most patients (80.2%) had low-grade liver injuries and received non-operative management (NOM), with a high NOM success rate of 94.5%. The median time to CT was 55 ± 64 min, and the mean time to operative or IR intervention was 159.9 ± 78.8 min. Complications occurred in 32.4% of patients, with ventilator-associated pneumonia (19.8%) being most common. The overall mortality was 6.3%. Multivariate analysis revealed that shorter time to CT significantly reduced mortality risk (OR = 0.5, p < 0.05), while a positive e-FAST result was strongly associated with increased mortality (OR = 3.3, p < 0.05). Higher ISSs correlated with longer monitored unit stays (ρ = 0.3, p = 0.0014). Traumatic liver injuries in this cohort were predominantly low-grade and effectively managed conservatively, with favorable outcomes. However, delays in imaging and operative intervention were observed, underscoring the requirement for streamlined trauma workflows. These findings highlight the requirement for continuous trauma system improvement, including protocol optimization and timely access to imaging and surgical intervention. Full article

The active safety of tractors remains a major concern in rural road environments, where tractor drivers face high crash risks due to limited vehicle visibility. In Serbia, 1.4% of crashes involve tractors, mainly due to poor visibility (64.3%), lack of beacon lights, unsafe overtaking, and unmarked stopped tractors (14.3% each). These issues reduce safety, increase fuel consumption and emissions, and cause economic losses. A driving simulator study with 117 drivers examined how visibility equipment affects speed perception. The results showed that 20 km/h was best estimated with all visibility aids, while 10 km/h was most accurately judged with only the slow-moving vehicle emblem. These findings emphasize the potential for simple, cost-effective visibility measures to enhance the active safety of tractors in mixed rural traffic conditions. By enhancing tractor visibility, these measures reduce crash risks, minimize unnecessary acceleration and deceleration, and lower fuel consumption and emissions associated with traffic disturbances. Furthermore, by preventing crashes, these solutions contribute to reducing resource consumption in crash-related medical care, vehicle repairs, and infrastructure damage. Integrating improved visibility equipment into rural traffic policy can significantly enhance tractors’ active safety and reduce the risk of crashes in agricultural regions. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety. Full article

Road traffic accidents (RTAs) are a leading cause of physical injury worldwide, but they also frequently result in post-traumatic stress disorder (PTSD). This systematic review examines the prevalence, predictors, comorbidity, and treatment of PTSD among RTA survivors. Four electronic databases (PubMed, Scopus, EBSCO, and ProQuest) were searched following PRISMA 2020 guidelines. Articles were included if reporting on the presence of post-traumatic stress disorder as a result of a road traffic accident in adults aged 18 years and older. Including peer-reviewed journal articles and awarded doctoral theses across all publication years, and written in English, Macedonian, Serbian, Bosnian, Croatian, and Bulgarian, identified 259 articles, and using Literature Evaluation and Grading of Evidence (LEGEND) assessment of evidence 96 were included in the final review, involving 50,275 participants. Due to the heterogeneity of findings, quantitative data were synthesized thematically rather than through meta-analytic techniques. Findings are reported from Random Control Trial (RCT) and non-RCT studies. PTSD prevalence following RTAs ranged widely across studies, from 20% (using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 criteria) to over 45% (using International Classification of Diseases, 10th Revision, ICD-10 criteria) within six weeks post-accident (non-RCT). One-year prevalence rates ranged from 17.9% to 29.8%, with persistence of PTSD symptoms found in more than half of those initially diagnosed up to three years post-RTA (non-RCTs). Mild or severe PTSD symptoms were reported by 40% of survivors one month after the event, and comorbid depression and anxiety were also frequently observed (non-RCTs). The review found that nearly half of RTA survivors experience PTSD within six weeks, with recovery occurring over 1 to 3 years (non-RCTs). Even minor traffic accidents lead to significant psychological impacts, with 25% of survivors avoiding vehicle use for up to four months (non-RCT). Evidence-supported treatments identified include Cognitive Behavioural Therapy (CBT) (RCTs and non-RCTs), Virtual Reality (VR) treatment (RCTs and non-RCTs), and Memory Flexibility training (Mem-Flex) (pilot RCT), all of which demonstrated statistically significant reductions in PTSD symptoms across validated scales. There is evidence for policy actions including mandatory and regular psychological screening post RTAs using improved assessment tools, sharing health data to better align early and ongoing treatment with additional funding and access, and support and interventions for the family for RTA comorbidities. The findings underscore the importance of prioritizing research on the psychological impacts of RTAs, particularly in regions with high incident rates, to understand better and address the global burden of post-accident trauma. Full article

Road crashes impose significant societal costs, and while links between static land use and safety are established, the long-term impacts of dynamic land use conversions remain under-explored. This study addresses this gap by investigating and quantifying how specific land use transitions over a decade influence subsequent road crash frequency in Metropolitan Melbourne. Our objective was to understand which conversion pathways pose the greatest risks or offer safety benefits, informing urban planning and policy. Utilizing extensive observational data covering numerous land use conversions, we employed Negative Binomial models (selected as the best fit over Poisson and quasi-Poisson alternatives) to analyze the association between various transition types and crash occurrences in surrounding areas. The analysis revealed distinct and statistically significant safety outcomes. Major findings indicate that transitions introducing intensified activity and vulnerable road users, such as converting agricultural land or parks to educational facilities (e.g., Agri → Edu, coefficient ≈ +0.10; Park → Edu, ≈+0.12), or intensifying land use in previously less active zones (e.g., Park → Com, ≈+0.07; Trans → Park, ≈+0.10), significantly elevate long-term crash risk, particularly when infrastructure is inadequate. Conversely, conversions creating low-traffic, nature-focused environments (e.g., Water → Park, ≈–0.16) or channeling activity onto well-suited infrastructure (e.g., Trans → Com, ≈–0.12) demonstrated substantial reductions in crash frequency. The critical role of context-specific infrastructure adaptation, highlighted by increased risks in some park conversions (e.g., Com → Park, ≈+0.06), emerged as a key mediator of safety outcomes. These findings underscore the necessity of integrating dynamic, long-term road safety considerations into land use planning, mandating appropriate infrastructure redesign during conversions, and prioritizing interventions for identified high-risk transition scenarios to foster safer and more sustainable urban development. Full article

The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article

In an ever-changing driving environment where vehicles are becoming smarter, more autonomous, and more connected, a paradigmatic change in signals for drivers might be required. This need is correlated with road safety (social sustainability). There are several factors affecting road safety, and one of these, especially important on rural roads, is speed. One way to actively influence drivers’ speed is to intervene with regard to speed limit signs by providing credible and effective limits. This goal can be pursued by working on variable speed limits that align with the boundary conditions of the installation site. In this research, an analysis was conducted on the rural road network within the Metropolitan City of Bari (Italy) that involved collecting the speeds on each of the investigated two-way, two-lane rural roads of the network. In addition to the speeds, all the most relevant geometric details of the roads were considered, together with environmental factors like rainfall. A generalized linear model was developed to correlate the operating speed limits and other variables together with information about rainfall, which degrades tire–pavement friction and thus, road safety. After the development of this model, safety performance functions, depending on the amount of rain or number of days of rain, were calculated with the intent of predicting crash frequency, starting with the operative speed and rain conditions. Operative speed, speed limit, percentage of non-compliant drivers, traffic level, and site length were found to be associated with all typologies and locations of crashes investigated. Full article

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article

This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. The number of fatalities on public roadways was then modeled using a multilayer perceptron artificial neural network employing the significant variables as predictors according to the generalization capacity of complex predictive models. The OLS and TOBIT findings indicated that the variables motorcycles and scooters per capita, municipal human development index, and number of SUS emergency units were the most important for modeling traffic fatalities at the scene at the national and regional levels. Applying these variables, the neural network’s best results achieved a hit rate of 88% for Brazil and 95% for the Northeast model. The contribution of this study is providing an approach combining various methods and considering a range of variables influencing traffic fatalities at the scene. The findings offer insights for policymakers, researchers, and practitioners involved in road safety initiatives, mainly where crash data are scarce, and macro-level analysis is necessary. Full article

This study evaluates the impact of light-emitting diode (LED) illuminated signs, known as active road signs, on road safety at urban intersections. Transportation safety specialists emphasize the importance of visibility and placement of signage. LED signs are increasingly deployed at accident-prone locations to improve safety and regulate traffic. This study focuses on stop-controlled intersections (SCIs) in Montréal, Québec, to propose a new backlit sign for evaluation. An unbiased experiment utilizing multinomial logistic regression (MNL) was designed to compare drivers’ reactions to different signage. Microscopic models based on observed turning movement counters (TMCs) were calibrated for conflict estimation using a genetic algorithm (GA). Generalized linear models (GLMs) estimated accident and conflict frequencies under different treatment scenarios. The results showed significant conflict reductions at intersections with LED-backlit signs (BLSs), including 65.5% at night and 46.8% in daylight. Pedestrian crossing conflicts decreased by 55.6% and 27.8%. This study introduces an evaluation framework that integrates driver compliance behavior into simulation and crash modeling to assess a newly designed BLS treatment. It provides a framework for assessing safety treatments in contexts where crash data are limited. Findings offer insights for improving SCIs and enhancing transportation safety using LED stop signs. Full article

Vehicle color coatings have long been recognized as a factor influencing road safety, particularly regarding their impact on speed perception and crash risk. This study aims to examine how different vehicle color coatings affect drivers’ perception of speed under nighttime driving conditions, with a specific focus on sustainability and visibility. A controlled laboratory experiment was conducted using a driving simulator to replicate realistic night traffic scenarios. A total of 161 participants evaluated passenger vehicles in four distinct color treatments, white (high-reflective paint), yellow (matte safety film), blue (glossy metallic finish), and black (low-reflective coating), at two speeds: 30 km/h and 50 km/h. Participants’ perceived speeds were collected and analyzed using standardized statistical methods. Results indicated a consistent pattern: speed was overestimated at 30 km/h and underestimated at 50 km/h across all vehicle colors. Lighter-colored vehicles (white and yellow) were perceived as moving faster than darker-colored vehicles (blue and black), with significant differences between black and yellow (30 km/h), yellow and blue (30 km/h), and black and white (50 km/h). Additionally, female participants tended to estimate higher speeds than male participants across most conditions. Other individual factors, such as place of residence, driver’s license type, driving experience, and frequency of driving, also showed measurable effects on speed perception. By using a simulator and accounting for diverse demographic characteristics, the study highlights how perceptual biases related to vehicle color can influence driver behavior. These findings emphasize the importance of considering vehicle color in traffic safety strategies, including driver education, vehicle design, and policy development aimed at reducing crash risk. Full article