Automated driving in level 3 autonomy has been adopted by multiple companies such as Tesla and BMW, alleviating the burden on drivers while unveiling new complexities. This article focused on the under-explored territory of micro accidents during automated driving, characterized as not fatal but abnormal aberrations such as abrupt deceleration and snake driving. These micro accidents are basic yet pervasive events that might results in more severe accidents. Through collecting a comprehensive dataset of user generated video recording such micro accidents in natural driving scenarios, this article locates key variables pertaining to environments and autonomous agents using machine learning methods. Subsequently, crowdsourcing method provides insights into human risk perceptions and reactions to these micro accidents. This article thus describes features of safety critical scenarios other than crashes and fatal accidents, informing and potentially advancing the design of automated driving systems.
Accident detection using Closed Circuit Television (CCTV) footage is one of the most imperative features for enhancing transport safety and efficient traffic control. To this end, this research addresses the issues of supervised monitoring and data deficiency in accident detection systems by adapting excellent deep learning technologies. The motivation arises from rising statistics in the number of car accidents worldwide; this calls for innovation and the establishment of a smart, efficient and automated way of identifying accidents and calling for help to save lives. Addressing the problem of the scarcity of data, the presented framework joins Generative Adversarial Networks (GANs) for synthesizing data and Convolutional Neural Networks (CNN) for model training. Video frames for accidents and non-accidents are collected from YouTube videos, and we perform resizing, image enhancement and image normalisation pixel range adjustments. Three models are used: CNN, Fine-tuned Convolutional Neural Network (FTCNN) and Vision Transformer (VIT) worked best for detecting accidents from CCTV, obtaining an accuracy rate of 94% and 95%, while the CNN model obtained 88%. Such results show that t
We introduce a counting process to model the random occurrence in time of car traffic accidents, taking into account some aspects of the self-excitation typical of this phenomenon. By combining methods from probability and differential equations, we study this stochastic process in terms of its statistical moments and large-time trend. Moreover, we derive analytically the probability density functions of the times of occurrence of traffic accidents and of the time elapsing between two consecutive accidents. Finally, we demonstrate the suitability of our modelling approach by means of numerical simulations, which address also a comparison with real data of weekly trends of traffic accidents.
Traffic accidents, especially at intersections, are a major road safety concern. Previous research has extensively studied intersection-related accidents, but the effect of building-induced visibility restrictions at intersections on accident rates has been under-explored, particularly in urban contexts. Using OpenStreetMap data, the UK's geographic and accident datasets, and the UK Traffic Count Dataset, we formulated a novel approach to estimate accident risk at intersections. This method factors in the area visible to drivers, accounting for views blocked by buildings - a distinctive aspect in traffic accident analysis. Our findings reveal a notable correlation between the road visible percentage and accident frequency. In the model, the coefficient for "road visible percentage" is 1.7450, implying a strong positive relationship. Incorporating this visibility factor enhances the model's explanatory power, with increased R-square values and reduced AIC and BIC, indicating a better data fit. This study underscores the essential role of architectural layouts in road safety and suggests that urban planning strategies should consider building-induced visibility restrictions. Such con
With the rapid development of civil aviation and the significant improvement of people's living standards, taking an air plane has become a common and efficient way of travel. However, due to the flight characteris-tics of the aircraft and the sophistication of the fuselage structure, flight de-lays and flight accidents occur from time to time. In addition, the life risk factor brought by aircraft after an accident is also the highest among all means of transportation. In this work, a model based on back-propagation neural network was used to predict flight accidents. By collecting historical flight data, including a variety of factors such as meteorological conditions, aircraft technical condition, and pilot experience, we trained a backpropaga-tion neural network model to identify potential accident risks. In the model design, a multi-layer perceptron structure is used to optimize the network performance by adjusting the number of hidden layer nodes and the learning rate. Experimental analysis shows that the model can effectively predict flight accidents with high accuracy and reliability.
Automatic traffic accidents detection has appealed to the machine vision community due to its implications on the development of autonomous intelligent transportation systems (ITS) and importance to traffic safety. Most previous studies on efficient analysis and prediction of traffic accidents, however, have used small-scale datasets with limited coverage, which limits their effect and applicability. Existing datasets in traffic accidents are either small-scale, not from surveillance cameras, not open-sourced, or not built for freeway scenes. Since accidents happened in freeways tend to cause serious damage and are too fast to catch the spot. An open-sourced datasets targeting on freeway traffic accidents collected from surveillance cameras is in great need and of practical importance. In order to help the vision community address these shortcomings, we endeavor to collect video data of real traffic accidents that covered abundant scenes. After integration and annotation by various dimensions, a large-scale traffic accidents dataset named TAD is proposed in this work. Various experiments on image classification, object detection, and video classification tasks, using public mainstr
Traffic accidents are one of the biggest challenges in a society where commuting is so important. What triggers an accident can be dependent on several subjective parameters and varies within each region, city, or country. In the same way, it is important to understand those parameters in order to provide a knowledge basis to support decisions regarding future cases prevention. The literature presents several works where machine learning algorithms are used for prediction of accidents or severity of accidents, in which city-level datasets were used as evaluation studies. This work attempts to add to the diversity of research, by focusing mainly on concentration of accidents and how machine learning can be used to predict hotspots. This approach demonstrated to be a useful technique for authorities to understand nuances of accident concentration behavior. For the first time, data from the Federal District of Brazil collected from forensic traffic accident analysts were used and combined with data from local weather conditions to predict hotspots of collisions. Out of the five algorithms we considered, two had good performance: Multi-layer Perceptron and Random Forest, with the latte
Motorcycle accidents are a prevalent problem in Texas, resulting in hundreds of injuries and deaths each year. Motorcycles provide the driver with little physical protection during accidents compared to cars and other vehicles, so when there is a collision involving a motorcycle, the motorcyclist is likely to be injured. While there are numerous reasons for motorcycle accidents, most are caused by negligence and could have been avoided. Because of the increasing popularity of motorcycles and scooter in Texas, coupled with an increase in the number of motorcycle accidents, the Texas Department of Transportation (TxDOT) has amped its efforts to improve motorcycle safety. From the data, it has been visible that teenage drivers are the most vulnerable to motorcycle accidents. In this report, we have tried to find out the probability of young driver and passenger motorcyclist's injury based on different conditions and to predict the rate of changing injury to this group in the upcoming years.
Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of
The macro influencing factors analysis of urban traffic safety is important to guide the direction of urban development to reduce the frequency of traffic accidents. In this study, a Bayesian vector autoregressive(BVAR) model was developed to exploring the impact of six macro-level economic and transport factors, including population, GDP, private vehicle ownership, bus ownership, subway rail mileage and road average speed on traffic accidents with the small sample size transport annual report data in Beijing. The results show that the BVAR model was suitable for time series analysis of traffic accidents in small sample situations. In macroeconomic factors, GDP growth was considered to reduce the number of traffic accidents in the long term, while population growth had a positive effect on traffic accidents in the short term. With the respect to macro-transport factors, road average speed and private vehicle ownership was perceived to increase traffic accidents in long duration, whereas bus ownership and subway rail mileage had long-term negative effects, with the greatest positive effect for road average speed and the greatest negative effect for subway rail mileage. This study su
The portrayal of crowd accidents by the media can influence public understanding and emotional response, shaping societal perceptions and potentially impacting safety measures and preparedness strategies. This paper critically examines the portrayal of crowd accidents in news coverage by analyzing the texts of 372 media reports of crowd accidents spanning 26 diverse news sources from 1900 to 2019. We investigate how media representations of crowd accidents vary across time and geographical origins. Our methodology combines lexical analysis to unveil prevailing terminologies and sentiment analysis to discern the emotional tenor of the reports. The findings reveal the prevalence of the term "stampede" over "panic" in media descriptions of crowd accidents. Notably, divergent patterns are observable when comparing Western versus South Asian media (notably India and Pakistan), unveiling a cross-cultural dimension. Moreover, the analysis detects a gradual transition from "crowd stampede" to "crowd crush" in media and Wikipedia narratives in recent years, suggesting evolving lexical sensitivities. Sentiment analysis uncovers a consistent association with fear-related language, indicative
We consider hyperbolic partial differential equations (PDEs) for a dynamic description of the traffic behavior in road networks. These equations are coupled to a Hawkes process that models traffic accidents taking into account their self-excitation property which means that accidents are more likely in areas in which another accident just occurred. We discuss how both model components interact and influence each other. A data analysis reveals the self-excitation property of accidents and determines further parameters. Numerical simulations using risk measures underline and conclude the discussion of traffic accident effects in our model.
Time series analysis applied to occupational accident reports is a powerful tool for understanding the evolution of occupational accidents over time. It provides valuable information to make informed decisions. In this study, data from reports of work accidents collected from the MINISTRY OF LABOR AND EMPLOYMENT PROMOTION. MTPE were analyzed by time series. Significant patterns and trends in accident reporting have been identified, leading to more effective prevention strategies and better health and safety management.
Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads involved in accidents are required to submit an accident report to the Federal Railroad Administration (FRA). These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident. Although these narratives provide more information than a fixed field entry, the terminologies used in these reports are not easy to understand by a non-expert reader. Therefore, providing an assisting method to fill in the primary cause from such domain specific texts(narratives) would help to label the accidents with more accuracy. Another important question for transportation safety is whether the reported accident cause is consistent with narrative description. To address these questions, we applied deep learning methods together with powerful word e
The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace accidents being governed by self-organized criticality, suggesting that nearly all workplace accidents have a common underlying cause, independent of their severity. Such power-law scaling is found for all labor categories documented by the U.S. Bureau of Labor Statistics. Our results provide scientific support for the Heinrich accident triangle, with the practical implication that suppressing the rate of severe accidents requires changing the attitude toward workplace safety in general. By creating a culture that values safety, empowers individuals, and strives to continuously improve, accident rates can be suppressed across the full range of severities.
This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016-2023, we trained an XGBoost classifier optimized through randomized search cross-validation and adjusted for class imbalance via class weighting. The final model achieves an overall accuracy of 78%, with strong performance on the majority class (Severity 2), attaining 87% precision and recall. Feature importance analysis reveals that time of day, geographic location, and weather-related variables, including visibility, temperature, and wind speed, rank among the strongest predictors of accident severity. However, contrary to initial hypotheses, precipitation and visibility demonstrate limited predictive power, potentially reflecting behavioral adaptation by drivers under overtly hazardous conditions. The dataset's predominance of mid-level severity accidents constrains the model's capacity to learn meaningful patterns for extreme cases, highlighting the need for alternative sampling strategies, enhanced feature engineering, and integration of external datasets. These finding
Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers, forming a key component of advanced driver assistance systems. In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover when required. Using dashcam video as input offers a cost-effective solution, but it is challenging due to the complexity of real-world driving scenes. Accident anticipation systems need to operate in real-time. However, current methods involve extracting features from each detected object, which is computationally intensive. We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes without requiring explicit object-level features. The network consists of transformer and graph modules, and we use the vision foundation model VideoMAE-V2 for global feature extraction. Experiments on four benchmark datasets (DAD, DoTA, DADA, and
Traffic accidents can be studied to mitigate the risk of further events. Recent advances in machine learning have provided an alternative way to study data associated with traffic accidents. New models achieve good generalization and high predictive power over imbalanced data. In this research, we study neural network-based models on data related to traffic accidents. We begin analyzing relative feature colinearity and unsupervised dimensionality reduction through autoencoders, followed by a dense network. The features are related to traffic accident data and the target is to classify accident severity. Our experiments show cross-validated results of up to 92% accuracy when classifying accident severity using the proposed deep neural network.
A comprehensive understanding of traffic accidents is essential for improving city safety and informing policy decisions. In this study, we analyze traffic incidents in Munich to identify patterns and characteristics that distinguish different types of accidents. The dataset consists of both structured tabular features, such as location, time, and weather conditions, as well as unstructured free-text descriptions detailing the circumstances of each accident. Each incident is categorized into one of seven predefined classes. To assess the reliability of these labels, we apply NLP methods, including topic modeling and few-shot learning, which reveal inconsistencies in the labeling process. These findings highlight potential ambiguities in accident classification and motivate a refined predictive approach. Building on these insights, we develop a classification model that achieves high accuracy in assigning accidents to their respective categories. Our results demonstrate that textual descriptions contain the most informative features for classification, while the inclusion of tabular data provides only marginal improvements. These findings emphasize the critical role of free-text dat
Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. Here we formulate accident reconstruction from publicly accessible reports and scene measurements as a parameterized multimodal learning problem. We construct CISS-REC, a dataset of 6,217 real-world accident cases curated from the NHTSA Crash Investigation Sampling System, and develop a reconstruction framework that grounds report semantics to road topology and participant attributes, reconstructs lane consistent pre-impact motion, and refines collision relevant interactions through localized geometric reasoning and temporal allocation. Our method outperforms representative baselines on CISS-REC, achieving the strongest overall reconstruction fidelity, including improved accident point accuracy and collision consistency. These results show that public accident reports can serve as scalable computational substrates for quantitatively verifiable accident reconstruction, with potential value for traffic safety analysis, simulation and autonomous driving resea