Identifying psychological and socio-economic factors affecting motorcycle helmet use

Sixty percent of motorcyclist fatalities in traffic accidents of Iran are due to head injuries, but helmet use is low, despite it being a legal requirement. This study used face-to-face interviews to investigate the factors associated with helmet use among motorcycle riders in Mashhad city, the second largest city in Iran. Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA) were used for data reduction and identification of consistent features of the data. Ordered and multinomial logit analyses were used to quantify the influences on helmet use and non-use. The data show that 47% of the sample used a helmet use, but a substantial proportion of these did not wear their helmet properly. In addition, 5% of motorcyclists believed that helmets reduced their safety. Norms, attitudes toward helmet use, risky traffic behavior and awareness of traffic rules were found to be the key determinants of helmet use, but perceptions of enforcement lacked influence. Duration of daily motorcycle trips, riding experience and type of job also affected helmet use. Results indicate that motorcyclist training, safety courses for offending motorcyclists and social programs to improve social norms and attitudes regarding helmet use are warranted, as are more effective law enforcement techniques, in order to increase proper use of helmets in Iranian motorcyclists. In addition, special safety courses should be considered for motorcyclists who have committed traffic violations

Automatic Detection of Helmets on Motorcyclists Using Faster - RCNN

Motorcycles have been a popular choice for a go-to daily means of transportation due to its lower price, making it affordable for high to low-class citizens. Helmets are required for every motorcycle owner so that the rider’s head is protected from accidents. However, not many people follow the rules and tend to not wear helmets and plenty of them underestimate the usage of helmets. For this, it is necessary to implement a system that can detect which rider wears the helmet or not by applying deep learning techniques. This paper aims to implement one of the deep learning techniques, which is Faster R – CNN to detect the helmets and the motorcyclists. After training 400 images using different learning rates, the mean average precision (mAP) achieved the highest with 87% using the learning rate of 0.000

Motorcycle safety research project: Interim summary report 3: training and licensing interventions for risk taking and hazard perception for motorcyclists

Motorcycle trauma is a serious road safety issue in Queensland and throughout Australia. In 2009, Queensland Transport (later Transport and Main Roads or TMR) appointed CARRS-Q to provide a three-year program of Road Safety Research Services for Motorcycle Rider Safety. Funding for this research originated from the Motor Accident Insurance Commission. This program of research was undertaken to produce knowledge to assist TMR to improve motorcycle safety by further strengthening the licensing and training system to make learner riders safer by developing a pre-learner package (Deliverable 1), and by evaluating the QRide CAP program to ensure that it is maximally effective and contributes to the best possible training for new riders (Deliverable 2). The focus of this report is Deliverable 3 of the overall program of research. It identifies potential new licensing components that will reduce the incidence of risky riding and improve higher-order cognitive skills in new riders

Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network

One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet

Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders and passengers on motorbikes, identifying whether the detected person is wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each for 20 seconds. Our study demonstrated the applicability of DL models to accurately detect helmet regulation violators even in challenging lighting and weather conditions. We employed several data augmentation techniques in the study to ensure the training data is diverse enough to help build a robust model. The proposed model was tested on 100 test videos and produced an mAP score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The use of deep learning techniques for image classification tasks, such as identifying helmet-wearing riders, has enormous potential for improving road safety. The study shows the potential of deep learning models for application in smart cities and enforcing traffic regulations and can be deployed in real-time for city-wide monitoring

Assessment of Motorcycle Noise Exposure Levels (LAeq, dBA) at Various Noise Standards and Speeds

Motorcyclists (n=26) average noise exposure levels (LAeq) were found substantially different for OSHA-HC (85 dBA), OSHA-PEL (78 dBA) and ACGIH/NIOSH (87 dBA) noise standards. A significant difference in LAeq, (p=.027) and engine capacity usage (p=.045) was found amongst gender. However, no observable association was found between the LAeq and motorcycle engine capacity (p= .462) and completion of a ride (p= .695). Thus, female riders were inclined to use lower motorcycle capacities, rode at lower speeds which resulted in lower noise exposure levels, in concurrence with longer ride durations. Overall, motorcyclists’ noise exposure level functions with the increasing speed (80km/h: 88 dBA). Keywords: Motorcycle noise; Dosimeter; Speed; Noise standards eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.245

Experimental evaluation on noise reduction performance of a motorcycle helmet

Motorcycle helmets are generally thought to be only protection of motorcycle drivers against head injuries as well as loud noise in traffic. While there have been several papers on noise elimination capabilities of motorcycle helmets, no controlled study has been reported to compare different types of test conditions in literature. The purpose of this study is to assess noise reduction capacities of a motorcycle helmet under different types of acoustical loadings as well as environments and to identify better test condition. Firstly, a Head & Torso simulator with and without the motorcycle helmet in a built acoustical cabinet was exposed to digitally generated sound to investigate insertion loss values. Besides, the Head & Torso simulator was fixed onto a motorcycle to simulate actual driving conditions. Sound pressure levels were captured at the ear level to obtain insertion loss values in case of motorcycle noise for different engine speeds. By comparing calculated insertion losses, it was revealed that considerable differences existed between tests for different conditions. Beneficial interpretations were deduced and thus, a practical solution was presented for accurate measurements acoustic performance of the motorcycle helmets in laboratory conditions

Assessment of Impacts of Repealing the Universal Helmet Law in South Carolina

The National Highway Traffic Safety Administration (NHTSA) statistics shows that South Carolina is one of the state with the highest number of the motorcycle fatalities and the fatal crashes. South Carolina repealed its Universal Helmet Law in 1980 which might be one of the reason for the increment of crashes and fatalities in South Carolina. Head and facial injuries are main causes of death in case of motorcycle fatal crashes. Helmet use can save number of lives and reduce the head and facial injuries. The thesis focuses on the advantages of the helmet use and necessity of Universal Helmet Law in South Carolina. The crash data for South Carolina from 1975 to 2015 was collected from Fatality Analysis Reporting System (FARS) to identify the various factors affecting the likelihood of the helmet using the logistic regression. The socio-economic data for South Carolina from 2002 to 2012 was collected from US Census Bureau. The motorcycle crash data from 2002 to 2015 was obtained from the SCDOT database. The roadways for South Carolina was obtained from the RIMS database. The CPI (cost price index) data from Department of Labor\u27s Bureau of Labor\u27s Statistics and fatal crash data was used to identify the effectiveness of helmet use in South Carolina. The social media and newsfeed were collected from twitter and various news channel and word cloud was created to characterize the opponents and ad vocative\u27s viewpoint. The results show that helmet use before the repeal of the Universal helmet law is three times more than after the repeal of the Universal helmet law. Also, the fatality trend from 1975 to 1980 is decreasing whereas the trend is increasing from 1981 to 2015. Among the various factors age, alcohol consumption and Universal helmet law are the significant factors in determining the likelihood of helmet use in South Carolina using the logistic regression. Universal Helmet Law is one of the major factor in determining the likelihood of helmet use. The total motorcycle crash data of South Carolina from 2013 to 2015 shows that the unhelmeted motorcyclists are twice the helmeted motorcyclists in the total motorcycle crashes. The percentage of helmeted motorcyclist with blood alcohol level \u3e0.08 is five times less than the percentage of the unhelmeted motorcyclists. People with the higher college degree education are more likely to wear the helmet than the people with diploma. Higher household income motorcycle riders are more likely to wear the helmet than the motorcycle riders of lower household income are. Also, the freeways, minor arterial and local roads are more prone to the motorcycle crashes. Highways like Kings Highway, Cleo Chapman Highway and other highways that passes through the urban areas are more prone to the motorcycle crashes. Local roads around the Columbia city and the Greenville city have more chances of having the higher motorcycle crashes. The KDE and Kriging analysis shows that the hot spots vary with and without the normalization of crashes by population. Charleston, which is a hotspot without normalizing the crashes, changes to be cold spot when the crashes are normalized with the population of that area. The text analysis in the study shows that the opponents focus on having freedom of choosing their own safety. The proponents are more concerned about the public safety, which they support with the facts and researches. The reinstatement of Universal helmet law in South Carolina is particularly recommended. Various educational programs should be conducted to educate the people regarding the use and effectiveness of helmet. Enforcement should be done in the Highways and local roads that are prone to motorcycle crashes