Prevent Road Accidents Research Articles

Italians’ public opinion on road roundabouts: A web based survey

Roundabouts have been shown to provide significant safety and operational benefits, but a better understanding of public perceptions of roundabouts is critical in order to assess the contribution of the driving behavior of the users on their safety. Therefore, research on user behavior and preferences is a helpful tool in improving road safety and accident prevention. The aim of this study is to increase knowledge about Italian users’ public opinion on road roundabouts. The authors estimated cross-national differences, and assessed correlations with personal variables, such as age, gender, travel mode most frequently used, perception of roundabout safety, etc. In order to collecting data, the instrument used is a national web based survey, the total sample comprised 1.728 participants. The data collected were analyzed by both Ordinal regression method and Multiple Correspondence Analysis.The results of this study show that the overall opinion that the respondents expressed against roundabouts is favorable. There are no substantial differences between the judgment of male drivers and that of females. Although for some categories of users the roundabouts are not considered safe and have no advantages in terms of functionality, their general opinion is always positive. The categories of respondents who expressed "Favorable" opinion are: women, those who travel daily with the private car or with public transport and who is a pedestrian. The categories of respondents who expressed "Very favorable" opinion are: men, and those who use the motorcycle as their main means of transport.Moreover, this study showed that web-based surveys provide an efficient means of collecting detailed public opinion data.

Road accident prevention model involving two-wheeled vehicles

Recently, in Russia, it has become relevant to conduct “audits of black spots”. It is a type of an independent audit and evaluation of the traffic safety level in the location under survey, determination of patterns in road accident causes and corresponding measures for their prevention. Traffic safety can be evaluated based on various criteria, but the reduction of the number of road accidents, especially those with injuries, has always been the most important criterion of traffic safety. In this paper, an analysis of factors that can affect elements of subsystems and the traffic safety system, in general, is conducted. Practical recommendations are provided.

A Cognitive Contemplation of Road Accident Predicton Through Deep Learning

The research based on the vehicle accidents step to collect and structure a progressive secure transportation unfortunately vehicle crashes were unavoidable. The accident prediction related with the risky environment data collection and arrangements based on the high priority of reality of accidents. The social activity and roadway structures are useful in the progression of traffic security control approach. We believe that to secure the best possible setback decline impacts with limited budgetary resources, it is basic that measures be established on coherent and objective studies of the explanations behind mishaps and seriousness of wounds. A survey based on the different algorithms able to predict the road accidents prevention methods. This paper demonstrates a couple of models to predict the reality of harm that occurred in the midst of car accidents using three artificial intelligent approaches (AI). The proposed scheme contributes a neural systems prepared utilizing choice trees and fluffy c implies bunching strategy for division.

An Identification Framework to Determine Drowsy Drivers and Help in the Prevention of Road Accidents

There is less concentration to a possibly risky driving conduct - driving with fatigue. There has been a progression of innovation overtime to help drivers. In this paper, we propose a framework to check consciousness of a driver whether the person is an abled condition to drive. In this model, the individual's face is recorded by a camera with face detection, and segmentation to segment eye and mouth features accurately. We utilize the discovery of face eyes and mouth and apply behavioral measures, for example, eye conclusion and yawning to recognize the tiredness of the driver. The spotlight set on planning a framework that will precisely screen the eye developments. This check can identify sufficiently early to evade road accidents.

Voice Controlled Wiper for Smart Helmets

Intelligent transportation system is one of the major focus in today’s era. Several sensor integrated devices and unmanned autonomous vehicles, not only enhance the comfort of drivers but also help in safe driving. Specifically, for motorcyclists, helmets embedded with sensors are lifesaving gadgets. Researchers have focused on developing low power low cost smart helmets to prevent road accidents. However, the existing smart helmets are not season friendly. This paper proposes an extension for smart helmets that can assist riders to ride the motorcycle with great ease during heavy rains. The hardware unit consists of wiper integrated to helmet which turns ON and OFF based on the voice commands issued by the motorcyclists.

A Study on Tiredness Measurement using Computer Vision

In this paper, we studied tiredness measurement based on several different detection methods in real time. We know that the driver tiredness is one of the major causes of traffic accidents. So tiredness detection can play a vital role for preventing road accidents. By developing an automatic solution for alerting drivers of tiredness before an accident occurs, this could reduce the number of traffic accidents. The Haar-cascade classifier is exploited based on Haar-like features to find the eyes. The main purpose of the Haar-cascade classifier is to classify closed or open state of the eyes. If we can notice that the eyes are closed for a predefined span of time, we consider the state of the eyes can be closed. Based on this closed-state of the eye, a notification (like alarm) is initiated to alert. We have detected only right eye for saving processing load on the system. The reason is that when a person closes his eyes he usually does not close one eye, but both eyes at the same time. Several steps are taken into account for this system; we first capture the frame from the webcam. Then we need to detect face as well as eye. To detect blinks, we process ROI (region of image) of pupil area. Our result is found to be satisfactory.

Embedded System Based on Obstacle Detector Sensor to Prevent Road Accident by Lane Detection and Controlling

This paper represents an economical accident prevention embedded system based on obstacle detection IR (infra-red) sensor, which will detect the lane indicator lines on the road. Receiving signals from these sensors, a microcontroller performs various functions like monitoring, motivation and control to prevent accidents due to overtaking and wrong lane driving. An RF (radio frequency) transmitter and receiver is applied to communicate with adjacent vehicles in a relevant range, which will help a driver to take decision in case of emergency overtake. A new model is also proposed by removing the use of external RF transmitter control, located at public zone (school, hospital) and when the vehicle receiver enter into the transmitter coverage area, controls the speed of vehicle. In lieu of this model our proposed model ensures that, the vehicle can recognize itself in a public zone and control its speed automatically by detecting two horizontal lines at a specific distance on the road effectively.

Actitud del docente en estrategias de enseñanza en promoción de la salud en seguridad vial

Alrededor del mundo mueren cerca de 1, 25 millones de personas por accidentes viales, y entre 20 y 50 millones sufren traumatismos no mortales. En Venezuela 90% de los accidentes viales se deben a la pobre educación vial traducida en la adopción de conductas riesgosas al circular. En la prevención de accidentes viales la actitud del docente hacia las estrategias que emplea para enseñar seguridad vial resultan claves, especialmente porque de este aspecto se tiene escasa o nula información. Se determinó la actitud del docente de educación primaria frente a las estrategias de enseñanza para la seguridad vial, en una Parroquia del estado Trujillo, Venezuela, con importantes cifras de accidentes viales. Estudio descriptivo, transversal y de campo, la muestra estuvo conformada por 20 docentes, la información se recopiló a través de un cuestionario con escala tipo Likert. Las docentes perciben de alto valor la enseñanza de la seguridad vial como importante para promover la salud, sin importar la estrategia pedagógica que se emplee, en tanto que los varones restan importancia al hecho.

Road Accident Analysis and Prevention in Nigeria: Experimental and Numerical Approaches

Road Accident Analysis and Prevention in Nigeria: Experimental and Numerical Approaches

Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications

Vehicular ad hoc networks (VANETs) are a recent class of peer-to-peer wireless networks that are used to organize the communication and interaction between cars (V2V), between cars and infrastructure (V2I), and between cars and other types of nodes (V2X). These networks are based on the dedicated short-range communication (DSRC) IEEE 802.11 standards and are mainly intended to organize the exchange of various types of messages, mainly emergency ones, to prevent road accidents, alert when a road accident occurs, or control the priority of the roadway. Initially, it was assumed that cars would only interact with each other, but later, with the advent of the concept of the Internet of things (IoT), interactions with surrounding devices became a demand. However, there are many challenges associated with the interaction of vehicles and the interaction with the road infrastructure. Among the main challenge is the high density and the dramatic increase of the vehicles’ traffic. To this end, this work provides a novel system based on mobile edge computing (MEC) to solve the problem of high traffic density and provides and offloading path to vehicle’s traffic. The proposed system also reduces the total latency of data communicated between vehicles and stationary roadside units (RSUs). Moreover, a latency-aware offloading algorithm is developed for managing and controlling data offloading from vehicles to edge servers. The system was simulated over a reliable environment for performance evaluation, and a real experiment was conducted to validate the proposed system and the developed offloading method.

An Intelligent Vehicle Control System for Enhancing Road Safety Using Optimal Visible Light Communication Network

Abstract A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it provides the low-cost high data-rate transmission to multiple users simultaneously in indoor environments. VLC has been recently introduced as a secure directional data transmission in vehicle to vehicle to provide an intelligent vehicle control system. However, the performance of this system is mostly affected by the collision of data transmission between different users. In this paper, we introduce an optimal visible light communication (OVLC) network that allows vehicles which have provides collision aware data transmission to improve the chance of transmitting information successively according to the network condition. Firstly, the next forwarding node is selected by the chaotic fish swarm optimization (CFSO) algorithm with the help of vehicle information’s such as intensity of light, the distance and speed of neighboring vehicles. The second contribution is to illustrate the congestion control (CC) system for avoiding extra time due to the control packets exchange process. The optimal result is then forward to the source vehicle equipped device, which helps the driver to make a healthy to control vehicle and efficiently avoid or prevent road accidents under different circumstances. The results show that the proposed OVLC network performs very efficient than existing network in terms of quality metrics, such as throughput, delay, packet loss rate, energy consumption and fairness index.

An IoT Based Novel Approach for Prevention of Road Accidents due to Extreme Weather Conditions using L-BOT

As more and more highways are being developed by the government, cities are getting connected and more people are commuting through highways; due to which there are fair chances of increase in the number of accidents on such highways, especially in extreme weather conditions (fog and heavy rain). From the statistics of the Ministry of Road Transport and Highways, India, it can be said that almost 50 people die, and 200 people get injured everyday due to accidents occurring due to fog or heavy rain. There is an urgent need of time to have cost-effective and accurate solutions which avoid/reduce the accidents caused due to fog or heavy rain. Ensuring road safety is utmost important and our solution for preventing road accidents occurring on the highways due to extreme weather conditions will lead the world towards smart and safe transport. This paper discusses various possible approaches to address the said issue with their pros and cons. In this paper, we are proposing a robust, cost-effective and energy-efficient framework for prevention of road accidents on highways due to extreme weather conditions. The proposed framework includes the design of the L-BOT device to get the location of the vehicle and communicate with the server. Getting the location of nearby vehicles using GPS and displaying them on the screen in real time manner can solve the problem to a greater extent with less cost and more accuracy. The proposed framework will be implemented and tested in a real-world environment to measure the performance parameters like delay, location accuracy, power consumption and server algorithm efficiency. KEYWORDS : Network protocols, wireless networks, connected vehicles, location sharing, nearby vehicles, L-Bot (Location Bot), cyberphysical system, GPS (Global Positioning System) Cite this Article Tushar Champaneria, Maitrik Shah, Sunil Jardosh. An IoT Based Novel Approach for Prevention of Road Accidents Due to Extreme Weather Conditions Using L-BOT. Journal of Web Engineering & Technology . 2019; 6(1): 15–24p.

Road Accident Prevention and Collision Avoidance at Intersection In VANET

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Artificial Intelligence to Prevent Road Accidents

Due to increasing demand in urban mobility and modern logistics sector, the vehicle population has been growing progressively over the past several decades. A natural consequence of the vehicle population growth is the increase in traffic congestion which in turn will lead to more accidents. Accident prediction is one of the most vital aspects of road safety. An accident can be predicted before it occurs, and precautionary measures can be taken to avoid it. Artificial Intelligence (AI) can help in improved awareness of road conditions, driving behaviour of the people and can avoid accidents with the help of improved active safety and improved traffic condition. Drug impaired driving is becoming a serious cause of accidents as the days go by. Moreover, it is more difficult to detect drivers who are under the influence of drugs than drivers who are the influence of alcohol. So the purpose of this research is to study and review the literature & industry reports and put in the approaches for detecting the unsafe driving pattern and also maintaining the health of the car to avoid accidents.

Impact of Road Safety and Accidents Prevention in Cameroon

Road Traffic Crash is a major public health issue experienced in the world today. This has therefore led to a significant damage to property, loss of lives thereby constituting an alarming problem. Road safety are measures put in place to prevent or reduce the risk of an accident occurring. The main problem identified by the researcher was the problem of numerous and fatal accidents on the highway in Cameroon. It is against this background that the main endeavor of this research is to identify the impact of achieving long lasting improvement on road accident prevention. safety and The role of road safety and accident prevention is very vital in Cameroon, since way. it reduces and preserved human life and properties in the high This paper sought to find out the influence of road safety and accident prevention in Cameroon. The study was purely based on literature review. From the review of several theories and emp irical studies it was established that accidents were as a result of more of human factors like speed, overloading, non respect of road signs. After analysing the data, the ensuring findings revealed that in spite of the results it still has a lot of setba cks coming from the methodology used, time, respondents and geographical limitations. Therefore, the researcher is recommending that if ICT tools like Geographical Positioning System, Transport Management System is been used, infrastructure of roads is imp roved, constant checks by road stakeholders, it will help to reduce accidents on our highways in our economy. This paves the necessity of way for the reader to have an indepth knowledge about road safety and accident prevention in Cameroon gives opportunities to bring out further research on this topic.

Considerations on the role of modernizing the road infrastructure in the prevention of road accidents

The realizing and improvement of road infrastructure, of modern road networks provides normal, safe and pleasant road traffic conditions and also help prevent road accidents. The road network, with its constructive characteristics, has to offer optimal conditions for the movement of vehicles, pedestrians and other categories of participants in the road traffic. Starting from the case study of a road sector with heavy road traffic, the current paper analyzes the increase in road safety in Romanian localities along European and national roads through the implementation of specific measures such as setting up sidewalks, installing New Jersey median barriers, expanding the road sectors with 2+1 lanes, replacing normal pedestrian crossings with elevated crossings or with pedestrian crossing with mid-road waiting areas etc.

Drivers perceptions of road accidents: A Qualitative Study

Background: Road accidents is a particular public health problem that is endangering the lives of many people every year in the world. Iran is one the most dangerous countries in terms of road accidents. Human factors and driving performance are the most important causes of this car accidents. This qualitative study was conducted to determine perceptions of 12 drivers in Shiraz city about road accidents, hoping to lead to effective interventions to inform and reduce the incidence of them. Methods: 12 drivers aged 24–60 years from Shiraz participated in qualitative, semi-structured, interviews about their perceptions of road accidents. The social ecological model informed interview question development, and data were examined using thematic analysis. Then the early categories formed. All interviews were recorded, categorized and analyzed. Results: The results of the study in five main themes included the sense of responsibility for driving regulations, the causes of driving hazardous behaviors, participation in modifying these behaviors, the requirements for preventive performance and the best practices for modifying behavior were emerged. Participants also believed that with more education, incentive behaviors would be more effective than punishment, accountability to their lives, occupants and people. They all opposed the increase in fines and noted that the authorities should be more concerned with measures, such as foundation of highways, the construction of parking sites, the construction of equipped centers for repair of vehicles and rest for the prevention of road accidents. Conclusion: According to the results of this study, the best and most cost-effective method is to reduce the incidence of injuries and improve the performance of drivers by using participatory and cooperative behaviors and avoiding of violence in law cases. More studies are recommended in this regard.

A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy.

Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

The Influence of Organizational Factors on Road Transport Safety.

Road transport safety is a major concern across Europe due to the human and socio-economic costs associated with work-related traffic accidents. Traditional approaches have adopted regulatory and technical measures to prevent road accidents leaving aside the organizational factors that might contribute to road transport safety. However, contemporary sociotechnical systems theory acknowledges the need to take into account organizational factors. This study adopts a sociotechnical approach and it examines the relationship between a number of organizational factors (organizational learning and training) and road traffic accidents in the organizations under study. Our sample was composed of 107 road transportation organizations from Spain. Binary logistic regression analyses were carried out to test our hypotheses. Organizational size and type of transport (goods or passengers) were included in the model as control variables. Results showed that in those organizations where organizational learning was supported, the occurrence of traffic accidents was less likely. Unexpectedly, the relationship between training and the occurrence of traffic accidents was not significant. Thus, findings partially supported the formulated hypothesis. Future research should shed light on the relationship between training and traffic accidents taking into account potential intervening variables.

Driver Drowsiness Detection Based on Face Feature and PERCLOS

Driving vehicles are complex and require undivided attention to prevent road accidents. Fatigue and distraction are a major risk factor that causes traffic accidents, severe injuries, and a high risk of death. Some progress has been made for driver drowsiness detection using a contact-based method that utilizes vehicle parts (such as steering angle and pressure on the pedal) and physiological signals (electrocardiogram and electromyogram). However, a contactless system is more potential for real-world conditions. In this study, we propose a computer vision based method to detect driver’s drowsiness from a video taken by a camera. The method attempts to recognize the face and then detecting the eye in every frame. From the detected eye, iris regions for left and right eyes are used to calculate the PERCLOS measure (the percentage of total time that eye is closed). The proposed method was evaluated based on public YawDD video dataset. The results found that PERCLOS value when the driver is alert is lower than when the driver is drowsy.