Prevention Of Traffic Accidents Research Articles

Classification of traffic accidents’ factors using TrafficRiskClassifier

The TrafficRiskClassifier model introduced in this study adopts an innovative approach that incorporates migration learning, image classification, and self-supervised learning, aiming to significantly improve the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data of traffic accidents to achieve a higher level of risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model proficiently extracts pivotal features from intricate traffic scenarios and formulates initial assessments of accident risks. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through the analysis of various polynomial functions, the model achieves enhanced accuracy in classifying disparate risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.

Assessment of Its Implementation on Safety on Yamuna Expressway

With the promise of fast connectivity between Greater Noida and Agra, the Yamuna Expressway represents modern infrastructure in India. Amidst all this splendor, though, is a grave worry: security. In order to reduce hazards and improve safety standards, this article investigates the complex network of obstacles that has been encountered while implementing safety measures on the highway. Numerous accidents, including fatalities, have been a recurring problem on the Yamuna Expressway since its opening. The expressway's relatively straight and smooth construction makes it easy for drivers to speed and act irresponsibly, which is the main cause of these incidents. Drivers disregard safety protocols and speed limits with complete disregard for the consequences, which is worsened by the lack of effective enforcement of traffic regulations. The absence of necessary safety elements is one of the primary obstacles to guaranteeing safety on the Yamuna Expressway. Poor lighting, inadequate signage, and restricted emergency services are some of the initial shortcomings that have greatly impacted drivers' ability to navigate safely, especially in bad weather or at night. There have been ongoing attempts to resolve safety problems, but the results have been mixed. The expressway's safety infrastructure has been strengthened by the implementation of measures such as crash barriers, better signage, expanded CCTV surveillance, and more police patrolling. Unfortunately, problems with corruption within law enforcement agencies, a lack of staff, and limited resources make it difficult to apply these restrictions effectively. The Yamuna Expressway can only be made safer with a comprehensive plan. A culture of safety among drivers can be greatly enhanced by public awareness programs that promote responsible driving and emphasize the repercussions of reckless driving. Intelligent transportation systems (ITS) integrate cutting-edge tech to improve traffic management and accident prevention via tools like speed cameras, automatic toll collecting, and real-time traffic monitoring. The Yamuna Expressway is an impressive example of India's infrastructure, yet it will remain unfulfilled until the critical problem of safety is resolved. The Yamuna Expressway has the potential to become a model of safe and efficient transportation for the millions of people who use it every day if we recognize the problems, find lasting solutions, and encourage everyone to do their part to keep everyone safe.

Autonomous Vehicle Traffic Accident Prevention using Mobile-Integrated Deep Reinforcement Learning Technique

When it concerns autonomous traffic management, the most effective decision-making reinforcement learning methods are often utilized for vehicle control. Surprisingly demanding circumstances, however, aggravate the collisions and, as a consequence, the chain collisions. In order to potentially offer guidance on eliminating and decreasing the danger of chain collision malfunctions, we first evaluate the main types of chain collisions and the chain events typically proceed. In an emergency, this study proposes mobile-integrated deep reinforcement learning (DRL) for autonomous vehicles to control collisions. Three essential influencing substances are completely taken into consideration and ultimately achieved by the offered strategy: accuracy, efficiency, and passenger comfort. Following this, we investigate the safety performance currently employed in security-driving solutions by interpreting the chain collision avoidance problem as a Markov Decision Process problem and offering a decision-making strategy based on mobile-integrated reinforcement learning. All of the analysis's findings have the objective of aid academics and policymakers to appreciate the positive aspects of a more reliable autonomous traffic infrastructure and to smooth out the way for the actual adoption of a driverless traffic scenario.

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution.

Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.

Effects of repetition of a car-driving pedal maneuver and neural output in older adults

Understanding the ability of older adults to control pedal position angle and investigating whether this ability can be enhanced through practice may contribute to the prevention of traffic accidents. This study aimed to investigate repetitive effects on variability of the pedal position and neural drive during car-pedal operation in older adults. Thirteen older and 11 young adults performed 105 (21 sets × 5 repetitions) pedal angle control tasks with plantar flexor contraction. High-density surface electromyograms were recorded of triceps surae muscles. A cumulative spike train as a neural drive was calculated using continuously active motor unit activities. The coefficient of variation of the angle was higher in older (1.47 ± 1.06 %) than young (0.41 ± 0.21 %) adults in the first sets, and improved to 0.67 ± 0.51 % in the final sets in older adults only. There was no significant difference in neural drive variability between older and young adults. Our results suggest that repetition improves angular steadiness in older adults. However, this effect could not be explained by neural output which is estimated from lower threshold motor units that are continuously active.

Self-management of Risk for the Prevention of Traffic Accidents from a Health Perspective: A Qualitative Study.

Introduction: In the world, deaths and injuries caused by traffic collisions have been considered a public health problem. In Colombia, 7.238 fatalities were recorded in 2021, with motorcycle riders representing the largest group of victims at 59.7%. Methods: The aim of this qualitative phenomenological study is to describe the risky experiences and deliberate actions of diverse road users that influence the self-management of the risk of traffic collisions. Results: Data were obtained from 22 participants: motorists, pedestrians and drivers. The content analysis describes various human conditions that affect self-management of the risk of traffic accidents, such as unsafe behaviors, non-compliance with traffic regulations by the different road actors, competitive culture among drivers, eagerness, among others. Additionally, factors related to care were determined: healthy recreational activities, promoting the value of one's own life and that of others, adequate time management and preventive behaviors by some road users. Conclusion: This research provides information on social and cultural aspects, experiences and risky behaviors of different road actors that influence the incidence of traffic accidents in Colombia.

Effectiveness of educational interventions in minimizing reactance in traffic accident prevention

IntroductionClassical mechanics concepts are vital for understanding traffic safety, encompassing speed, acceleration, stopping distance, impact speed, deformation, kinetic energy, and their effects on vehicle occupants. Statistical physics principles are also employed to study multiple vehicles as a many-particle system. This study examines the effectiveness of using a damaged car and structured instruction in road safety initiatives.MethodsParticipants engaged with a damaged car and discussed deformation caused by excessive speed. They participated in role simulations, envisioning and solving risky traffic scenarios, while considering the potential boomerang effect resulting from excessive fear appeals within the campaign.ResultsCombining emotional appeals with solution-focused, action-oriented, and self-esteem-enhancing interventions is essential to mitigate the boomerang effect and encourage safety-compliant behavior. Educational institutions must implement tailored follow-ups to emotionally charged prevention campaigns. Using Design-Based Research, we developed effective methods for knowledge acquisition and transfer, drawing from cognitive behavioral therapy and established traffic safety programs.DiscussionThis publication explores non-standardized social training within focus groups, focusing on negative emotional states following an emotionalizing campaign similar to “Crash Kurs NRW,” targeting upper-level courses in North Rhine-Westphalian schools. Methods from cognitive behavioral therapy and social learning initiated, observed, and documented changes in participants’ norms, values, and attitudes, discussed herein. Additionally, we investigated whether the presence of police officers in prevention campaigns influences reactive behavior. Comparing pre- and post-surveys confirmed reactance after emotionalizing campaigns, and our structured post-survey effectively influenced stage-event-induced behavior. Surprisingly, police officers’ presence did not significantly impact reactive behavior. Furthermore, our follow-up tool extends its applicability to similar emotionalizing prevention campaigns, broadening its potential reach. Our future research will delve into students’ physics literacy during traffic space analysis of accident hotspots, advancing traffic safety initiatives.

Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques

Malaysia ranks third among ASEAN countries in terms of deaths due to accidents, with an alarming increase in the number of fatalities each year. Road conditions contribute significantly to near-miss incidents, while the inefficiency of installed CCTVs and the lack of monitoring system algorithms worsen the situation. The objective of this research is to address the issue of increasing accidents and fatalities on Malaysian roads. Specifically, the study aims to investigate the use of video technology and machine learning algorithms for the car detection and analysis of near-miss accidents. To achieve this goal, the researchers focused on Penang, where the MBPP has deployed 1841 CCTV cameras to monitor traffic and document near-miss accidents. The study utilised the YOLOv3, YOLOv4, and Faster RCNN algorithms for vehicle detection. Additionally, the study employed image processing techniques such as Bird’s Eye View and Social Distancing Monitoring to detect and analyse how near misses occur. Various video lengths (20s, 40s, 60s and 80s) were tested to compare the algorithms’ error detection percentage and test duration. The results indicate that Faster RCNN beats YOLOv3 and YOLOV4 in car detection with low error detection, whereas YOLOv3 and YOLOv4 outperform near-miss detection, while Faster RCNN does not perform it. Overall, this study demonstrates the potential of video technology and machine learning algorithms in near-miss accident detection and analysis. Transportation authorities can better understand the causes of accidents and take appropriate measures to improve road safety using these models. This research can be a foundation for further traffic safety and accident prevention studies.

Deformation Gated Recurrent Network for Lane-Level Abnormal Driving Behavior Recognition

As a significant part of traffic accident prevention, abnormal driving behavior recognition has been receiving extensive attention. However, the granularity of existing abnormal driving behavior recognition is mostly at road-level, and these methods’ high complexity leads to high overhead on training and recognition. In this article, we propose a deformation gated recurrent network for lane-level abnormal driving behavior recognition. Firstly, we use conditional random field model to calculate the lane change necessity of the vehicle, which helps us to distinguish whether the lane-changing behavior is reasonable. Secondly, we propose deformation gated recurrent network (DF-GRN) and trajectory entropy to capture the implicit relationship between trajectories and shorten recognition time. Finally, we get classified results including aggressive, distracted and normal driving behavior from the network. Distracted and aggressive behavior will be marked as anomaly. The effectiveness and real-time nature of the network are verified by experiments on Hangzhou and Chengdu location datasets.

New Law Enforcement Impact on the Prevention of Road Accidents in Kosovo

The strengthening of law enforcement is one aspect of traffic accident prevention initiatives that is not to be overlooked. Studies have shown that increasing law enforcement can have an impact of up to 8% (a case study in Sweden). Since the Law on Traffic Safety was replaced in 2016 with the Law on Traffic Regulations, we have been examining the Kosovo instance to determine the effects of stepping up law enforcement. The impact will be measured using statistical data from prior years and the time frame after the new law's implementation. Only those traffic fines that were directly related to the causes of accidents were used in the analysis, which compared the increase and decline of these numbers over time. The objective is to determine the ultimate actions or modifications that will have the greatest impact by measuring the impact in real values. Given that the trend of accidents by causes may be tracked for all kinds of road accidents, the high number of traffic fines classified according to the causes of accidents is a new development in the field of studies on the relationship between measures and the number of accidents. Doi: 10.28991/CEJ-2023-09-12-011 Full Text: PDF

Driver behaviour prediction and enhanced ad hoc on‐demand distance vector routing protocol in VANET

SummaryMobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and accident prevention. For this reason, human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. Here, an image‐based behaviour prediction model is performed to achieve the highly accurate driving state of the driver. A warning message is forwarded to the neighbouring vehicles based on the driver's behaviour. Due to the dynamic properties of real‐time vehicle mobility, a faster data transmission model is achieved using the ad hoc on‐demand distance vector routing protocol. To achieve faster data transmission and nullified retransmission, here a weighted location‐based routing model is framed. The optimization problem in the location‐aided routing protocol is solved using the vector algorithm's weighted mean. As a result, the proposed method improved the throughput of ASHLOSR to 8.1% and AODV to 7.6%.

Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate.

Optimizing Road Transportation Big Data: A Novel Approach for Feature Selection through Optimization Techniques

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this research work, the model involves two main phases (i) Feature Selection and (ii) Classification. Since the length of feature vector tends to high, optimal feature selection technology is included, from which the most relevant features are selected by the Lion-based Firefly Algorithm which is referred as Optimization based Feature Selection Method (OFSM). The main objective of this paper is projected on minimizing the correlation between the selected features, which results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called Neural Network (NN) is adopted, which can classify the data in an effective manner with the selected features.

Characterization of road surfaces using three near-infrared diode lasers

Rapid road network expansion has heightened the importance of surface condition information for traffic accident prevention and route optimization. This article introduces a laser diode-based sensor that identifies seven surface conditions and accurately measure ice, water, and snow film thicknesses on roads. An optical module was developed to detect weak optical signals based on the characteristic absorption spectrum of the target surface. The module used three laser diodes (1310, 1440, and 1550 nm wavelengths) as light sources. Additionally, a road classification algorithm that is adaptable to foggy weather was developed using a multi-wavelength processing protocol. The sensor was subjected to numerous calibration and performance verification experiments. During thick foggy measurements, the processed spectra displayed a maximum variation of 2.372% across a 600 to 25,000 m visibility range with a relative standard deviation of only 0.328%. This demonstrated effective weakening of the effects of visibility variations. During winter field testing, the sensor classified road conditions effectively and accurately measured ice, snow, and water film thicknesses, with a correlation coefficient of 0.97444. The accuracy of the measurements was less than 0.5 mm. The sensor’s effectiveness for long-term field-based road testing has been verified.

IDT: An Integration of Detection and Tracking Toward Low-Observable Multipedestrian for Urban Autonomous Driving

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident prevention. However, it is a challenging task in urban autonomous driving, since the weak backscattered signals from pedestrians with small radar cross-section may be submerged in strong background clutters, especially under adverse weather conditions. On this account, this article presents an integration of detection and tracking (iDT) toward multipedestrian with a low signal-to-noise ratio (SNR). In particular, in contrast to conventional methods, in which the detection and tracking are treated as two separate processes, we address them jointly to ensure the accuracy of continuous detection and tracking in low SNR conditions. Another distinguishing element is that to accommodate the time-varying number of targets, the Bayesian framework is tailored by augmenting the state vector with a multipedestrian evolutional indicator. The advantage is that all targets can be tracked simultaneously by searching the global likelihood ratio of a spectrum once, rather than assigning an individual tracker to each target in conventional methods. Furthermore, through the proposed integrated framework, the data association problem is circumvented because there is no explicit measurement-target assignment process in our approach. In addition, a commercial automotive multiple-input-multiple-output millimeter-wave radar sensor is employed to validate the proposed method. Consequently, numerous simulation and experiment results turn out that iDT shows unique advantages in low-observable multipedestrian tracking compared with traditional methods.

Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network

Accidents typically occurred on roads, resulting in significant societal losses. Road accidents are a worldwide issue that result in the loss of precious human lives and property. The purpose of this paper is to create an intelligent system-based on Machine Learning model for avoiding road accidents, as well as a system that effectively reduces road accidents severities. The Artificial Neural Network (ANN) algorithm, along with others such as Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nave Bayes (NB), Stochastic Gradient Descent (SGD), Random Forest (RF), Gradient Boosting (GB), and AdaBoost, is used to create an intelligence system. Many driving collaborator procedures, installed in a few vehicles, assist drivers in avoiding vehicle crashes by providing early cautioning messages. The intelligence road crash avoidance system model is built on dataset of 29 columns and 1048575 rows. Preprocessing, feature selection, and feature extraction performed with the help of heat map and correlation matrix are used to select features. Linear Discriminant Analysis (LDA) is used for feature extraction. The testing dataset revealed that the proposed ANN method outperforms other algorithms with an accuracy of 0.856. Intelligent systems aid in the prevention of traffic accidents, which aids police officers and researchers in developing new policies.

ECONOMIC AND LEGAL MEANS OF PREVENTING ROAD ACCIDENTS IN FOREIGN COUNTRIES

The aim of this article is to study the economic and legal means of preventing road accidents abroad, and to determine the possibility and feasibility of incorporating positive foreign experiences into national legislation and legal practice. The subject of research is the economic and legal means of preventing road accidents abroad. The methodology of the publication is based on an organic combination of philosophical, general scientific and specific legal research methods. Taking into account the subject matter, the methodology of comparative jurisprudence is used to a greater extent. Results. The study of foreign experience in the application of economic and legal means of traffic accident prevention gives an idea of the general trends in strengthening the role of economic means of encouraging drivers to observe traffic rules, namely: legal regulation of the dependence of the amount of the fine on the amount of the offender's wages and other income; increasing insurance payments for drivers who systematically violate traffic rules; economic incentives for drivers who observe certain rules during the year; systematic investments in the development of road infrastructure, innovative AI means of traffic regulation, development of public transport; expansion of the network of toll motorways to relieve public roads. It is emphasized that borrowing the experience of foreign countries in the specific field of public relations without scientific substantiation and without taking into account the peculiarities of national traditions, legal consciousness and legal culture cannot solve the key problems of ensuring road safety and preventing road accidents in Ukraine.

Traffic Accident Prevention Through Acceptance of Advanced Driver Assistance System (ADAS) among Urban People

Traffic Accident Prevention Through Acceptance of Advanced Driver Assistance System (ADAS) among Urban People

A Review of Road Traffic Accident Prediction Methods

With the continuous development of urban traffic and the acceleration of urbanization, traffic accidents have become an important issue for urban safety and social stability. In order to prevent and reduce the occurrence of traffic accidents, traffic accident prediction technology has gradually become a hot spot for research. This paper analyzes road traffic accident prediction techniques from articles included in relevant English journals and provides a detailed introduction to the road traffic accident prediction techniques that are already in existence. This paper introduces the current status of research on traffic accident prediction techniques, including traditional statistical analysis methods, machine learning methods, neural network methods, time series analysis methods and techniques based on spatio-temporal data mining, and analyses the advantages and disadvantages of each road traffic accident prediction method. These methods are able to analyse the influencing factors of traffic accidents, build prediction models, improve prediction accuracy and provide strong support for road traffic accident prevention effects for urban traffic safety. Finally, the main difficulties faced in road traffic accident prediction and the future development trend of road traffic accident prediction is discussed. The work done in this paper can provide necessary theoretical support for relevant researchers and save the time needed for literature review.

ROAD ACCIDENTS AND THE NATIONAL ECONOMY: LEGAL ANALYSIS

The present publication aims to conduct a legal analysis of the mutual influence of road accidents and the national economy, which is manifested at different levels of relevant public relations. The research subject is road accidents and the national economy. The publication’s methodology relies on an organic combination of well-known laws and techniques of dialectics (the transition of quantitative changes into qualitative, unity and struggle of opposites, and the negation of negation), general scientific methods of research (techniques of the logical method, comparative method, techniques of the sociological method, systemic and structural-functional methods), and special legal methods (legal statistics, formal-legal method, methods of comparative jurisprudence and legal modeling). Results. Road safety is inextricably linked with economic processes taking place in a particular state. In addition, as economic indicators affect the number of road accidents so the dynamics and consequences of road accidents significantly affect the national economy. The most important economic factors affecting the number of road accidents are as follows: underdeveloped road infrastructure, outdated vehicle fleet, poor financial and logistic support of law enforcement agencies, insufficient number of traffic enforcement cameras, and other innovative means of preventing road accidents. The priority in traffic accident prevention is the maximum automation of means ensuring road safety: coverage of all key traffic intersections with traffic enforcement cameras, equipment of modern cars with automatic speed limit systems and alcohol intoxication testers, which automatically block the ignition system, the development of a fleet of AI-based self-driving cars and automated traffic control systems. Other economic and administrative means of road accident prevention are equally important, i.e., financial incentives for compliance with traffic rules, development and improvement of public transport efficiency, and investments in multi-level road infrastructure to reduce traffic congestion in available highways and transport interchange.