Search Results (134)

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. 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 CORUS-XUAM project defined three two-way U-space corridors linking Frankfurt Airport’s Terminal 2 on the city outskirts with the city-center Trade Fair. These corridors avoid the approach cones of the northern and central runways and bypass hospital no-fly zones and large buildings. In our previous studies, we first used fast-time simulations to evaluate the U-space routing and its operating concept, based on historical air traffic data. Included were arriving and departing airplanes as well as police, and medical helicopters throughout the city. The focus was on the limitations of the airspace, avoiding conflicts with other airspace users and between the airtaxis using a different corridor or delaying the departure, as well as determining the throughput potential of such a corridor system. Building on our previous studies, this study incorporates higher-fidelity traffic simulation data and an updated demand analysis for the airtaxi shuttle service. Our new sizing analysis reveals that ground operations typically, not airspace capacity, constitute the primary bottleneck. Full article

Bicycle safety remains a critical concern as cycling gains popularity, especially in urban areas where traffic conditions pose significant risks. The increasing presence of bicycles and derivatives of them further complicates traffic dynamics, raising the potential for accidents and injuries. This study examines bicycle accident trends in Poland from 2016 to 2023 using data provided by the Polish Police, supplemented with medical insights on injury mechanisms. The analysis highlights key patterns, such as the higher incidence of accidents during summer, fewer accidents on weekends compared to weekdays, and the disproportionate fatality rate among cyclists over 60. Failure to yield the right of way emerges as a leading cause of accidents. These findings underscore the need for a multifaceted approach to improving cyclist safety, including infrastructural enhancements, public education, stricter enforcement of traffic laws, and advancements in medical response and protective gear. Addressing these factors can contribute to a safer environment that supports the continued growth of sustainable and active transportation. Full article

The rapid development of electric vehicles (EVs) around the world has resulted in new challenges for road safety. Identifying the features of EV crashes is a precondition for developing effective countermeasures. However, due to the short history of EV development, existing studies on EV crashes are quite limited. China, which has the largest EV market in the world, has witnessed a substantial increase in EV crashes in recent years. Therefore, this study comprehensively investigated the characteristics of EV crashes by analyzing the 2023 traffic crash data from Changshu. This is a pioneering study that discusses EV safety by comparing real EV crashes and ICEV crashes from a city in China, the largest EV market in the world. It was found that EV crashes had a higher fatality rate compared to internal combustion engine vehicle (ICEV) crashes. Compared to ICEV crashes, EV crashes are more likely to hit pedestrians and occur during the starting phase. Among the vehicles involved in crashes, the proportion of EVs used for passenger and freight transport was higher than that of ICEVs. In addition, for EV crashes, the proportion of female drivers was much higher, but the proportion of elderly drivers was much lower. Thus, to identify the significant factors influencing crash severity, a logistic regression model was built. The results confirm that EV crashes are more likely to be more fatal than ICEV crashes. In addition, hitting pedestrians and light trucks and crashes occurring in rural areas, at intersections, during winter, and on weekdays could significantly increase the risk of fatalities. These findings are expected to provide new perspectives for improving EV safety within the wave of automotive electrification. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Globally, and specifically in Poland and Lithuania, the incidence of road accidents has been on a decline over the years. The overall figures remain significantly high. Thus, it is imperative to take substantial measures to further decrease these statistics. The objective of this article is to estimate the future frequency of traffic accidents in both countries. To achieve this, a comprehensive yearly analysis of traffic incidents in Poland and Lithuania was performed. Using police records, forecasts for the years from 2024 to 2030 were established. Various neural network models were employed to predict the number of accidents. The results suggest that there remains potential for stabilization in traffic accident rates. It is undeniable that the increasing volume of vehicles on the roads, along with the development of new highways and expressways, plays a crucial role in this scenario. The result obtained depends on the model parameters (testing, validation, and training phases). Sustainable development requires comprehensive solutions, which also include improving road safety. Our research contributes to this goal by creating a tool that provides insight into the number of road accidents in analyzed countries. Full article

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

While studies have typically examined the driving habits of young drivers living in large urban cities, few have examined the habits of young drivers living in smaller cities with large rural surrounding areas. Three surveys were disseminated to 193 young drivers, 65 police officers, and 62 driving instructors to examine the driving habits and challenging driving situations young drivers experience. Almost a fifth (18.1%) reported consuming alcohol prior to driving; alcohol consumption prior to driving was significantly associated with eating food/drinking beverages while driving, cellphone use, and speeding. The most challenging situations young drivers reported were night driving, encountering wild animals on the road, and driving in extreme weather conditions (e.g., ice, snow). Driving instructors reported that young drivers had challenges with lane positioning, speed control, and navigating traffic signs and signals. Additionally, police officers reported issuing tickets to young drivers primarily for failure to stop, distracted driving, impaired driving, and speeding. Young drivers living in smaller cities and rural communities have unique challenges, including interactions with wildlife, driving on gravel roads, and driving in poor weather and road conditions (e.g., ice, snow). Opportunities for young drivers to be exposed to these scenarios during driver training are critical for increasing awareness of these conditions and reducing crash risk. Full article

Road traffic accidents (RTAs) are a significant public health and safety concern, resulting in numerous injuries and fatalities. The growing number of cases referred to traffic accident rooms in courts has underscored the necessity for an automated solution to determine victim indemnifications, particularly given the limited number of specialized judges and the complexity of cases involving multiple victims. This paper introduces RTACompensator, an artificial intelligence (AI)-driven decision support system designed to automate indemnification calculations for road accident victims. The system comprises two main components: a calculation module that determines initial compensation based on factors such as age, salary, and medical assessments, and a machine learning (ML) model that assigns liability based on police accident reports. The model uses Arabic bidirectional encoder representations from transformer (AraBERT) embeddings to generate contextual vectors from the report, which are then processed by extreme gradient boosting (XGBoost) to determine responsibility. The model was trained on a purpose-built Arabic corpus derived from real-world legal judgments. To expand the dataset, two data augmentation techniques were employed: multilingual bidirectional encoder representations from transformers (BERT) and Gemini, developed by Google DeepMind. Experimental results demonstrate the model’s effectiveness, achieving accuracy scores of 97% for the BERT-augmented corpus and 97.3% for the Gemini-augmented corpus. These results underscore the system’s potential to improve decision-making in road accident indemnifications. Additionally, the constructed corpus provides a valuable resource for further research in this domain, laying the groundwork for future advancements in automating and refining the indemnification process. Full article

Background/Objectives: The incidence of pedestrian traffic injuries is an escalating concern for public health worldwide. Particularly in fast-developing nations, such as Saudi Arabia, these injuries form a significant portion of trauma-related healthcare challenges. This study aims to explore age-specific differences in trends, seasonal variations, and the overall impact of pedestrian traffic injuries in Riyadh, Saudi Arabia, with a focus on injury characteristics and clinical outcomes. Methods: The study conducted a retrospective analysis using data from the Saudi Trauma Registry (STAR) covering the period between August 2017 and December 2022. It employed descriptive statistics, chi-square tests, and multivariable linear regression analyses to explore demographic trends, characteristics of injuries, and hospital-based outcomes. Results: This study analyzed data from 1062 pedestrian injury cases, revealing key demographic and clinical patterns. Most incidents occurred on weekdays (71.9%) and during nighttime hours (63.3%), with seasonal peaks observed from April to June (30.4%). The lower extremities (27.5%) and head (21.3%) were the most frequently injured body regions. ICU admissions were more common among individuals aged 30–40, females, and those with head or chest trauma, while higher in-hospital mortality was associated with patients over 60 years old, transport by private or police vehicles, and extended ICU and hospital stays. Approximately 25.6% of cases required ICU care, with an overall in-hospital mortality rate of 4.9%. Conclusions: This study provides an in-depth analysis of pedestrian traffic injuries treated at a trauma center in Riyadh, highlighting significant demographic, temporal, and clinical patterns. Understanding these trends is essential for optimizing resource allocation and improving emergency care outcomes. Furthermore, the identified age-specific risk factors and seasonal variations underscore the critical need for targeted interventions and policy enhancements to improve road safety and reduce the burden of pedestrian injuries. Full article

Motorcyclists remain a disproportionately large group of vulnerable road users, with fatality rates significantly higher than that in other road groups. Additionally, fatal accidents involving motorcyclists have a more slowly decreasing trend in comparison to that of other road users, while the number of this kind of users is growing fast. For all these reasons, there is a need to understand what the key factors leading to fatal accidents are in order to identify the possible measures to minimize the accidents themselves or at least their consequences. This would permit, indeed, to positively impact the road traffic system, leading to the creation of the safest road traffic system possible, as it is the goal of the Sustainable Safety approach. The aim of this study is to dive into the mentioned problem, analyzing fatal motorcycle accidents in Slovenia over a decade, highlighting the key factors contributing to these incidents. By integrating data from four databases, the study evaluated accident trends, infrastructural elements, and rider behavior through a multi-stage analysis. Firstly, data were collected from four national, up-to-date databases that contain information about road accidents themselves, the road infrastructure, additional police data, and media descriptions. After merging this information into one comprehensive database, where each row represents all the data available for one accident, a general analysis of accidents’ trends over the considered 10-year period was developed, considering at first all fatal road accidents, then deepening it to accidents caused by a motorcyclist, and finally to single-vehicle accidents. A statistical analysis followed, aimed at identifying a statistical correlation between the accidents and the factors leading to them. The results of the first accident analysis indicated that excessive speed, incorrect driving direction, and overtaking maneuvers are the primary causes of fatal accidents, especially on non-urban roads preferred by motorcyclists. Single-vehicle accidents frequently involve collisions with roadside objects, including safety barriers and poles, underscoring the need for targeted infrastructural improvements. The following correlation analysis revealed that a total of seven factors were statistically significant: three human factors (age, gender, experience)—which were the ones with the strongest correlations—one infrastructural factor (pavement width), and three factors belonging to external conditions (accident type, cause, and location). Of these, four were positively correlated to the causer, while three, i.e., pavement width, causes, and road location, were negatively correlated. This study provides a foundation for future research on less severe accidents and proactive risk behavior analysis, aiming to improve motorcyclist safety comprehensively. Full article

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing for real-time monitoring, analysis, and reporting of traffic incidents. The system not only identifies accidents but also aids in coordinating emergency responses, such as dispatching ambulances, fire services, and police, while simultaneously managing traffic flow. The approach begins with the creation of a diverse highway accident dataset, combining public datasets with drone and CCTV footage. YOLOv11s is retrained on this dataset to enable real-time detection of critical traffic elements and anomalies, such as collisions and fires. A vision–language model (VLM), Moondream2, is employed to generate detailed scene descriptions, which are further refined by a large language model (LLM), GPT 4-Turbo, to produce concise incident reports and actionable suggestions. These reports are automatically sent to relevant authorities, ensuring prompt and effective response. The system’s effectiveness is validated through the analysis of diverse accident videos and zero-shot simulation testing within the Webots environment. The results highlight the potential of combining drone and CCTV imagery with AI-driven methodologies to improve traffic management and enhance public safety. Future work will include refining detection models, expanding dataset diversity, and deploying the framework in real-world scenarios using live drone and CCTV feeds. This study lays the groundwork for scalable and reliable solutions to address critical traffic safety challenges. Full article

Pedestrian safety is a critical concern worldwide, as pedestrians account for nearly a quarter of all road crash deaths. In Poland, in the last decade, the number of pedestrians killed in road accidents varied from 25 to 30% of all road accident victims each year. A similar tendency is observed in EU countries, but the average number of pedestrian fatalities is lower and amounts to 20%. Numerous activities have been undertaken to improve the safety of vulnerable road users. Land planning plays a crucial role in enhancing pedestrian safety. Effective land-use planning can mitigate risks by integrating pedestrian-friendly infrastructure into urban design. Numerous measures have been implemented to improve the safety of vulnerable road users, including education campaigns, speed reduction measures, and infrastructure enhancements. One of the latest initiatives involves enhancing the visibility of pedestrian crossings through the installation of additional lighting systems. In order to assess the effects of the undertaken activities, a number of zebra crossings with and without additional luminance were investigated. Crash data gained from police statistics, along with the calculated crash rates (CRs), were utilized to evaluate changes in safety performance at selected crosswalks. For this purpose, a „before–after” method was applied. Importantly, the research results did not show a clear impact of additional lighting on reducing the number of road crashes and they highlight that other factors, including the geometric characteristics of crossings and their location and proximity to land uses generating significant pedestrian traffic, significantly influence crash rates. Full article