Search Results (519)

Wildlife–vehicle collisions (WVCs) remain a significant cause of animal mortality worldwide, particularly in regions experiencing rapid road network expansion. During the COVID-19 pandemic, a number of studies reported decreased WVC rates, attributing this trend to reduced traffic volumes. However, the validity of the simplified assumption that “fewer vehicles means fewer collisions” remains underexplored from a mechanistic perspective. This study aims to reevaluate that assumption using two simulation-based models that incorporate both the physics of vehicle movement and behavioral parameters of road-crossing animals. Employing an inverse modeling approach with quasi-realistic traffic scenarios, we quantify how vehicle speed, spacing, and animal hesitation affect collision likelihood. The results indicate that approximately 10% of modeled cases contradict the prevailing assumption, with collision risk peaking at intermediate traffic densities. These findings challenge common interpretations of WVC dynamics and underscore the need for more refined, behaviorally informed mitigation strategies. We suggest that integrating such approaches into road planning and conservation policy—particularly under the European Union’s ‘Vision Zero’ framework—could help reduce wildlife mortality more effectively in future scenarios, including potential pandemics or mobility disruptions. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were reviewed. Among the 164 pedestrian fatalities (involving 92 males and 72 females), the most common scenario involved a pedestrian crossing the road (57.3%). In 61 cases (64.9%), pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side (i.e., crossing from right to left from the driver’s perspective, as vehicles drive on the left in Japan). In 33 cases (35.1%), pedestrians crossed from the vehicle’s lane side to the oncoming traffic lane side. Among cases of pedestrians crossing from the vehicle’s lane side, 54.5% were struck by the near side of the vehicle’s front, whereas 39.7% of those crossing from the oncoming traffic lane side were hit by the far side of the vehicle’s front (p = 0.02). Therefore, for both crossing directions, collisions frequently involved the front left of the vehicle. When pedestrians were struck by the front centre or front right of the vehicle, the collision speeds were higher when pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side rather than crossing from the vehicle’s lane side to the oncoming traffic lane side. A significant difference in collision speed was observed for impacts with the vehicle’s front centre (p = 0.048). The findings suggest that increasing awareness that older pedestrians may cross roads from the oncoming traffic lane side may help drivers anticipate and avoid potential collisions. Full article

Non-Newtonian debris flows represent a critical challenge for hydraulic infrastructure in mountainous regions, often causing significant damage and service disruption. However, current models typically simplify these flows as Newtonian, leading to inaccurate design assumptions. This study addresses this gap by comparing the hydraulic behavior of Newtonian and non-Newtonian flows in an alluvial fan, using the Amoray Gully in Apurímac, Peru, as a case study. This gully intersects the Interoceánica Sur national highway via a low-water crossing (baden), making it a relevant site for evaluating debris flow impacts on critical road infrastructure. The methodology integrates hydrological analysis, rheological characterization, and hydraulic modeling. QGIS 3.16 was used for watershed delineation and extraction of physiographic parameters, while a high-resolution topographic survey was conducted using an RTK drone. Rainfall-runoff modeling was performed in HEC-HMS 4.7 using 25 years of precipitation data, and hydraulic simulations were executed in HEC-RAS 6.6, incorporating rheological parameters and calibrated with the footprint of a historical event (5-year return period). Results show that traditional Newtonian models underestimate flow depth by 17% and overestimate velocity by 54%, primarily due to unaccounted particle-collision effects. Based on these findings, a multi-barrel circular culvert was designed to improve debris flow management. This study provides a replicable modeling framework for debris-prone watersheds and contributes to improving design standards in complex terrain. The proposed methodology and findings offer practical guidance for hydraulic design in mountainous terrain affected by debris flows, especially where infrastructure intersects active alluvial fans. Full article

Wildlife–vehicle collisions (WVCs) pose a growing threat to road safety and wildlife conservation. This research explores the relationship between the moon phases and the occurrence of nighttime WVCs in Serbia from 2015 to 2023. A total of 2767 nighttime incidents were analyzed to assess whether the full moon is associated with an increased collision frequency. The results revealed a statistically significant rise in the average annual number of WVCs during full moon nights compared to other nights, indicating that increased lunar illumination may affect animal movement and impact collision rates. However, no statistically significant differences were observed when comparing the frequency of WVCs across all four lunar phases. Spatial analysis identified the South Bačka and Podunavlje districts as the most at-risk regions for WVCs during full moon periods. As the first study of its kind in Serbia, this research provides new insights into the spatial and temporal patterns of WVCs. The findings can assist in developing focused mitigation strategies, such as improved signage, speed control strategies, and awareness campaigns, especially in regions with increased risk during full moon nights. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

This paper investigates crash severity in single-vehicle road crashes involving collisions with roadside light poles in Croatia. Due to the absence of detailed object-type classifications in the official crash database, media reports were used to identify relevant incidents in combination with the official state database, resulting in 38 crashes identified between 2016 and March 2025. Descriptive analysis and crosstabulation were applied to explore patterns in crash outcomes. A CHAID decision tree analysis was then applied in an exploratory capacity to highlight possible predictors of injury or fatal outcomes, acknowledging the limitations of the small sample size. Results showed that the speed limit was the only variable significantly associated with crash severity, with all crashes above 50 km/h resulting in injuries or fatalities. The findings highlight the importance of speed management and support the potential for implementing passively safe poles to reduce the consequences of such crashes. The study also discusses the performance of different pole types in line with EN 12767:2019, defines risk zones, and proposes solutions for the example locations. The results offer future research implications and valuable insights for road safety improvement, especially in areas with frequent pole collisions. Full article

Wildlife-vehicle collisions (WVCs) threaten biodiversity, particularly in the Gulf of Mexico, where road expansion increases habitat fragmentation. This research analyzes WVC patterns in southeastern Mexico, estimating collision rates across road types and assessing environmental factors influencing roadkill frequency. Field monitoring in 2016 and 2023 recorded vertebrate roadkills along roads in Campeche, Chiapas, and Tabasco. Principal Component Analysis (PCA) and Generalized Additive Models (GAM) evaluated landscape influences on WVC occurrences. A total of 354 roadkill incidents involving 73 species of vertebrates were recorded, with mammals accounting for the highest mortality rate. Hotspots were identified along Federal Highway 259 and State Highways Balancán, Frontera-Jonuta, and Salto de Agua. Road type showed no significant effect. Land cover influenced WVCs, with cultivated forests, grasslands, and savannas showing the highest incidences. PCA identified temperature and elevation as key environmental drivers, while GAM suggested elevation had a weak but notable effect. These findings highlight the risks of road expansion in biodiversity-rich areas, where habitat fragmentation and increasing traffic intensify WVCs. Without targeted mitigation strategies, such as wildlife corridors, underpasses, and road signs, expanding infrastructure could further threaten wildlife populations by increasing roadkill rates and fragmenting habitats, particularly in ecologically sensitive landscapes like wetlands, forests, and coastal areas. Full article

The frequency of road accidents in specific locations is determined by a number of variables, among which an important role is played not only by common determinants such as inappropriate behavior of road users, but also by external factors characterizing a given location. Taking this into account, the main objective of the study was to answer the question of which variables determine that the intensity of car accidents is higher in certain parts of the city of Poznań compared to other locations. The study was based on source data from the police Accident and Collision Records System (SEWiK). For the purposes of the analysis, two variants of the regression method were used: ordinary least squares (OLS) and geographically weighted regression (GWR). The obtained results made it possible to identify variables that increase the likelihood of a traffic accident in specific parts of the city, and the variables that proved to be statistically significant include the size of the built-up area and the number of traffic lights. The results obtained using the GWR technique indicate that the way in which the analyzed features influence road accidents can vary across the city, which may emphasize the complexity of the analyzed phenomenon. The results can be used by relevant entities (transport traffic planners and many others) to create road safety policies. Full article

The automatic identification of traffic collisions is an emerging topic in modern traffic surveillance systems. The increasing number of surveillance cameras at urban intersections connected to traffic surveillance systems has created new opportunities for leveraging computer vision techniques for automatic collision detection. This study investigates the effectiveness of transfer learning utilizing pre-trained deep learning models for collision detection through dashcam images. We evaluated several state-of-the-art (SOTA) image classification models and fine-tuned them using different hyperparameter combinations to test their performance on the car collision detection problem. Our methodology systematically investigates the influence of optimizers, loss functions, schedulers, and learning rates on model generalization. A comprehensive analysis is conducted using 7 performance metrics to assess classification performance. Experiments on a large dashcam-based images dataset show that ResNet50, optimized with AdamW, a learning rate of 0.0001, CosineAnnealingLR scheduler, and Focal Loss, emerged as the top performer, achieving an accuracy of 0.9782, F1-score of 0.9617, and IoU of 0.9262, indicating a strong ability to reduce false negatives. Full article

Traffic calming has emerged as a key urban strategy to reduce vehicle speeds and mitigate road traffic risks, with increasing recognition of its broader implications for public health, human behaviour, and urban liveability. This systematic literature review examines the multifaceted impacts of traffic-calming measures—from speed limit reductions to physical infrastructure and enforcement-based interventions—by synthesising findings from 28 peer-reviewed studies. Guided by the PRISMA framework, the review compiles research exploring links between traffic calming and outcomes related to public health, behaviour, and urban quality of life. Research consistently indicates that such interventions reduce both the frequency and severity of collisions, improve air and noise quality, and promote active mobility. These effects are shaped by user perceptions: non-motorised users tend to report higher levels of safety and accessibility, whereas motorised users often express frustration or resistance. Beyond safety and environmental improvements, traffic calming has been associated with greater use of public space, stronger social connections, and enhanced environmental aesthetics. The findings also show that key challenges may affect the effectiveness of traffic calming and these include negative attitudes among drivers, mixed outcomes for air quality, and unintended consequences such as traffic displacement or increased noise when interventions are poorly implemented. Overall, the findings suggest that traffic calming can serve as both a public health initiative and a tool for enhancing urban liveability, provided that the measures are designed with contextual sensitivity and supported by inclusive communication strategies. Full article

This paper explores the application of artificial intelligence in urban energy infrastructure construction and enhances the operation and maintenance safety of infrastructure through edge computing and advanced sensors. At present, urban manhole covers cover a large number of roads, but there is a lack of effective real-time monitoring methods. In order to effectively solve these problems, this study proposes a domain adaptive network algorithm (EDDNet) based on data fusion. By optimizing the loss function, the attention mechanism is used to make the model pay more attention to the deep features related to the abnormal state of the inspection cover. The algorithm solves the problem of broadband vibration analysis and reduces the misclassification rate in various behavioral scenarios, including pedestrian traffic, slow-moving vehicles, and intentional surface collisions. A data acquisition sensor network is established, and a six-degree-of-freedom coupled vibration model and a structural vibration model of the inspection cover are established. The vibration peak under high load conditions is modeled and simulated using impact load data, and a fitting curve is generated to achieve deep optimization of the model and enhance robustness. The experimental results show that the classification accuracy of the network reaches 95.23%, which is at least 10.2% higher than the baseline model. Full article

Uneven roads pose significant challenges to vehicle stability, passenger comfort, and safety, especially in snowy and mountainous regions. These problems are often complex and challenging to resolve with traditional detection and stabilization methods. This paper presents a dual-method approach to improving vehicle stability by identifying road irregularities and dynamically adjusting the balance. The proposed solution combines YOLOv8 for real-time road anomaly detection with a GY-521 sensor to track the speed of servo motors, facilitating immediate stabilization. YOLOv8 achieves a peak precision of 0.99 at a confidence threshold of 1.0 rate in surface recognition, surpassing conventional sensor-based detection. The vehicle design is divided into two sections: an upper passenger seating area and a lower section that contains the engine and wheels. The GY-521 sensor is strategically placed to monitor road conditions, while the servomotor stabilizes the upper section, ensuring passenger comfort and reducing the risk of accidents. This setup maintains stability even on uneven terrain. Furthermore, the proposed solution significantly reduces collision risk, vehicle wear, and maintenance costs while improving operational efficiency. Its compatibility with various vehicles and capabilities makes it an excellent candidate for enhancing road safety and driving experience in challenging environments. In addition, this work marks a crucial step towards a safer, more sustainable, and more comfortable transportation system. Full article

Decreases in animal mortality due to wildlife–vehicle collisions have been consistently documented as an environmental effect of human mobility restrictions aimed at containing the spread of the COVID-19 pandemic. In this study, we investigate this phenomenon on the mid-sized Mediterranean island of Lesvos, considering a multi-species group of mammals over a five-year systematic recording of animal casualties. We developed a method to analyze the relationship between actual casualties and risk, drawing inspiration from Markowitz’s theory on multi-asset optimization in economics. Additionally, we treated this phenomenon as a Poisson probabilistic process. Our main finding indicates that the lockdown year diverged markedly in modeled return–risk space, exhibiting a displacement on the order of 10² compared to the multi-year baseline—an outcome that reflects structural changes in risk dynamics, not a literal 100-fold decrease in observed counts. This modeled shift is significantly larger compared to published evidence regarding individual species. The results concerning the vulnerability of specific mammals, analyzed as a Poisson process, underscore the importance of singular events that can overshadow the overall systemic nature of the issue. We conclude that a promising strategy for addressing this problem is for conservationists to integrate animal-friendly measures into general human road safety policies. 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