Traffic Safety Monitoring Research Articles

Real-time accident risk identification for freeway weaving segments based on video analytics

Accurately identifying the risk of traffic accidents in real time for specific road sections is crucial for implementing proactive traffic control strategies. The existing studies have focused on real-time prediction of collisions between two vehicles, identification of accident risks on road segments relies heavily on historical accident data. This study proposes a framework for identifying road section accident risks at the granular level by utilizing traffic conflicts as an intermediary. In this framework, vehicles are detected and tracked in real time and the pixel coordinates are then efficiently converted into world coordinates based on the camera projection principle. A binary logit model is constructed to identify road section accident risks using carefully selected macroscopic traffic flow parameters. The proposed framework was validated on two one-sided ramp weaves in Jinan, China, achieving an accuracy of 76.2 %. The findings will serve as a foundation for traffic safety monitoring and proactive traffic control.

Adaptive Image-Defogging Algorithm Based on Bright-Field Region Detection

Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with the dark-channel prior being the most frequently employed. However, halo and bright-field color distortion issues persist. To further improve image quality, an adaptive image-defogging algorithm based on bright-field region detection is proposed in this paper. Modifying the dark-channel image improves the abrupt changes in gray value in the traditional dark-channel image. By setting the first and second lower limits of transmittance and introducing an adaptive correction factor to adjust the transmittance of the bright-field region, the limitations of the dark-channel prior in extensive ranges and high-brightness areas can be significantly alleviated. In addition, a guide filter is utilized to enhance the initial transmittance image, preserving the details of the defogged image. The results of the experiment demonstrate that the algorithm presented in this paper effectively addresses the mentioned issues and has shown outstanding performance in both objective evaluation and subjective visual effects.

Image Fusion of Multi-Temporal Images of Eyes Feature using Hybrid Approach of PCA and SWT

A wide variety of information is represented by an image feature, which may be applied in various applications such as mage fusion, video processing, medical diagnosis, traffic safety monitoring, visual surveillance, feature matching, image segmentation, pattern matching, person identification, sentiment analysis, human computer interaction and many more fields. This paper focused on multi-temporal feature extraction of eyes feature from human face images captured in two different time slots to reduce the semantic gap from images and to improve the image quality by image fusion using PCA, SWT and hybrid approach of PCA and SWT algorithm. In this paper, comparative study of multi-temporal image fusion using Principle Component Analysis (PCA), Stationary Wavelet Transform (SWT) algorithms and hybrid approach of PCA and SWT are employed and its experimental results are evaluated with its performance analysis. Image fusion performance is compared based on eight quantitative quality measures as SSIM, MSE, NAE, CC , SC , AD, SD and MI.The outcomes of comparison show that employing the hybrid approach of PCA+SWT transform can improve image fusion performance. The applicability of this work approach may have several uses when the utilization of human facial features is feasible.

Moving Target Detection Algorithm for Millimeter Wave Radar Based on Keystone-2DFFT

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario’s radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data.

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications

The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed “time spent in conflict (TSC)” is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs. The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs. The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.

Административные наказания в юрисдикционной деятельности сотрудников дорожного надзора ГИБДД МВД России

As the number of traffic accidents rises due to unsatisfactory road conditions, the system of administrative penalties for traffic offenses faces challenges that demand an urgent response, especially since the topic lacks prior research. The author employs logical methods, comparative techniques, and structural analysis to identify the theoretical problems emerging from the application of administrative penalties used by the traffic police officers of the General Administration for Traffic Safety of the Ministry of Internal Affairs of Russia. The work follows the historical development of the adminictrative penalties system through the Soviet and Post-Soviet periods of Russian administrative law science. The author considers the main scientific approaches that have influenced the definition of the administrative penalty, taking into special account the sphere of traffic offenses. Drawing from the scholarly dispute author offers an original definition of traffic safety, administrative penalties in the jurisdictional activities of traffic police officers of the General Administration for Traffic Safety of the Ministry of Internal Affairs of Russia. The article thoroughly explores the goals of an administrative sentence, its types, characteristics, and structure. Additionally, the author offers new insights on such kinds of penalties as a warning and administrative fine for the traffic offenses uncovered in the course of traffic safety monitoring. Comparing the changes in the administrative penalties in the form of a fine in the Soviet legislation and modern Russian Code of Administrative Offenses, the author reflects upon such principles of law as severity and adequacy of the administrative punishment in the field of traffic offenses. In conclusion, the author suggests improvements to articles 11.21 and 11.22 of the Russian Code of Administrative Offenses, specifically to increase the fines in order to advance the practical relevance of the jurisdictional activities of traffic police officers of the General Administration for Traffic Safety of the Ministry of Internal Affairs of Russia and reduce the accident incidence rate.

Unmanned aerial vehicles as element of road traffic safety monitoring

Unmanned aerial vehicles initially designed to satisfy the needs of military organizations have significantly expanded their functional purpose during the past decades and are now used by many government institutions and bodies addressing a wide range of organizational, strategic and prognostic issues. Studying the equipment capability of government institutions participating in road traffic safety facilities resulted in a conclusion that unmanned aerial vehicles are intensively used when addressing organizational, administrative, operative, analytical, preventive and tactical-technical issues. Payload elements of modern aerial vehicles enable dealing with planning, construction and operation of motor ways, situational and prediction modeling of traffic flows in order to ensure population mobility, monitoring of climatic conditions and environmental situation, and help addressing the issues of crime prevention, promoting detection and investigation of road safety offenses. Unmanned aerial vehicles are an element of an intellectual transport system comprising modern informational, communication and telematic technologies. These aerial vehicles provide for automated search, recording and reception of information for automated processing, which allows for efficient monitoring of road transport system and groups of vehicles in the conditions of certain spatial and time parameters in order to ensure transport mobility of population and road safety in general.

Research on Intelligent Traffic Model Based on Neural Network Algorithm

For the national economic construction and social development, intelligent traffic safety design has a very important role. In addition, cars have entered thousands of households in recent years, and the flow of intelligent traffic has increased significantly. The transformation and design of intelligent traffic safety facilities is an important work related to people’s life safety. Building with GIS technology as the core, the computer interlocking simulation system and join the safety of the application of computer technology, network communication technology, the grading management mode, the implementation of traffic safety monitoring and management of the whole process of engineering construction in the true sense realize the standardization and systematization of safety information construction process management, so that they can master the traffic engineering construction all aspects of the data and safety information, guarantee the quality of project construction.

Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.

Semantic Gap Reduction From Mouth Feature Threshold Value Using Viola Jones Algorithm

Image features are useful in defining image precisely and uniquely which are helpful in classification and recognition of images. Various feature values can be extracted by using different techniques used in image processing applications such as pattern recognition, feature matching, image segmentation, image fusion, video processing, visual surveillance, remote sensing, traffic safety monitoring, medical diagnosis, and human computer interaction etc. Number of factors of image affects on accuracy of feature extraction from different databases of images. The distance of the object (or person) from the camera and threshold value for feature detected plays an important role to capture accurate feature from the image database. The runtime required to capture the feature from image is also considerable factor when an algorithm for feature extraction is selected. The paper considers the distance and threshold value used for mouth feature detection and run-time required to capture the mouth feature from face database using Viola Jones algorithm. The experimental result shows increased threshold value at certain level for mouth feature detection gives better result and improved mouth detection rate for semantic gap reduction.

Detecting anomalies and attacks in network traffic monitoring with classification methods and XAI-based explainability

Assuring the network traffic safety is a very important issue in a variety of today’s industries. Therefore, the development of anomalies and attacks detection methods has been the goal of analyses. In the paper the binary classification-based approach to network traffic safety monitoring is presented. The well known methods were applied to artificially modified network traffic data and their detection capabilities were tested. More detailed interpretation of the nature of detected anomalies is carried out with the help of the XAI approach. For the purpose of experiments a new benchmark network traffic data set was prepared, which is now commonly available.

A Study on the Smart Maritime Traffic Safety Monitoring System Based on AI & AR

A Study on the Smart Maritime Traffic Safety Monitoring System Based on AI & AR

Deep Learning-Based Reasoning With Multi-Ontology for IoT Applications

In the era of mobile big data, data driven intelligent Internet of Things (IoT) applications are becoming widespread, and knowledge-based reasoning is one of the essential tasks of these applications. While most knowledge-based reasoning work is conducted with knowledge graph, ontology-based reasoning method can inherently achieve higher level intelligence by leveraging both explicit and tacit knowledge in specific domains, and its performance is determined by precise refinement of the inference rules. However, most ontology-based reasoning work concentrates on semantic reasoning in a single ontology, and fail to utilize association of multiple ontologies in various domains to extend reasoning capacity. This is even the case for the IoT applications where knowledge from multiple domains needs to be utilized. To overcome this issue, we propose a deep learning-based method to associate multiple ontology rule bases, thereby discover new inference rules. In our method, we first use a regression tree model to determine the threshold value for parameters in inference rules that constitute the ontology rule base, avoiding the influence of uncertainty factors on knowledge reasoning results. Then, a two-way GRU (Gated Recurrent Unit) neural network with attention mechanism is used to discover semantic relations among the rule bases of ontologies. Therefore, the association of multiple ontology rule bases is realized, and the rule base of knowledge reasoning is expanded by acquiring some unspecified rules. To the best our knowledge, this work is the first one to leverage deep learning in reasoning with multiple ontologies. In order to verify the effectiveness of our method, we apply it in a real traffic safety monitoring application by relating rule bases of a vehicle ontology and a traffic management ontology, and achieve effective knowledge reasoning.

Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment

Pedestrian detection has become one of the hottest topics in intelligent traffic system because of its potential applications in driver assistance and automatic driving. In this study, a fast pedestrian detection and dynamic tracking method within vehicle-to-vehicle (V2V) cooperative environment is proposed. A dynamic tracking-by-detection framework for real-time pedestrian detection is developed. First, a cascade classifiers, based on selected Haar-like features, is trained to detect pedestrian. Then, CamShift algorithm combined with extended Kalman filtering is used to pedestrian dynamic tracking. Finally, with the crowdsourcing detected information, a smartphone-based V2V cooperative warning system is developed to share useful detection results within blind spots. The experiment results show that the proposed method has a real-time and accurate performance, which can provide a reference for road traffic safety monitoring technology.

Characterization of crash-prone drivers in Saudi Arabia – A multivariate analysis

This study conducted a survey of traffic crashes with the data collected from police stations in the three cities of Saudi Arabia involving different features related to crashes, drivers, vehicles, and understanding of traffic signs. Among the chauffeurs, drivers at fault and not at fault were separated and investigated through factor analysis for 19 parameters related to their background and knowledge of traffic signs. The data show that a particular age group and time of the day may provide more insights to characterize the overall crashes in these cities. The factor analysis shows that the drivers at fault and not at fault may have distinguishable profile. Logit models were developed to quantify the effects of these variables. The models show that driver’s experience and knowledge of traffic signs for chauffeurs has positive impact on reducing faulty behavior of drivers. Approximately, 68%–74% of the original variables are required to characterize chauffeurs, indicating the possibility of data reduction in traffic safety monitoring program. This study may assist in profiling the chauffeurs involved in crashes and reducing the parameters to be monitored for traffic safety program. The recommendations of this study may be considered beneficial in making policy for licensing and hiring of chauffeurs.

Towards an analytical information system of traffic accidents in the function of traffic safety monitoring

Quality monitoring, analysis and evaluation of traffic safety situation is the first and most important strategic step in establishing a traffic safety protection system. With the goal of establishing the protection system and traffic safety management system, it is necessary to use quality data from traffic accident databases. Apart from quality, with respect to different quality criteria, it is also necessary for the database to be both available and up-to-date. Database availability and up-to-datedness is extremely important for scientific and professional research, because that research helps define certain dependencies that can affect the choice of traffic safety improvement measures. The quality of the data which are in the database also has to be very high. This can be achieved by establishing an information system and information flow by relevant subjects that possess information on traffic accidents towards the central database. For that purpose, the data coming from the police, insurance companies, health authorities and other relevant authorities need to be processed and adapted to the form of the data from the central database. This method produces comprehensive and quality data on traffic accidents, compared to when the database is ‘filled’ by only one source, for example the police. The existing traffic accident database in Serbia is owned by the Ministry of Interior, and that database is not suitable for research and analysis, it is not public and the procedure for obtaining data is long and uncertain. The new Road Traffic Safety Law in Serbia gives different users the opportunity to access traffic safety data, but it is somewhat restricted or limited. With that in mind, there was a need to define and establish a new traffic accident database. This paper presents the possibilities of traffic safety analysis based on the integrated traffic accident database model, which was primarily compiled for the needs of the City of Belgrade within the BERTAAD Project(Belgrade Road and Traffic Accident and Analitical Database). Possibilities of the database are reflected in different levels of access to the database, depending on the user, different levels of data detail that can be accessed; they are also easier to work with because the database is user-oriented. Key words: traffic safety, analysis, estimate, database model, access.