Environmental Effects of Driver Distraction at Traffic Lights: Mobile Phone Use

Alemdar, Kadir Diler; Kayacı Çodur, Merve; Codur, Muhammed Yasin; Uysal, Furkan

doi:10.3390/su152015056

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Driver behavior, such as acceleration patterns, speed, and gear-shifting practices, also significantly affects fuel consumption. For example, in the study of Alemdar et al [11] an additional behavioral factor is examined-driver distraction, particularly through mobile phone use at traffic lights. This study demonstrated that distractions lead to delayed responses when lights turn green, causing unnecessary idling, increased fuel consumption, and higher emissions.…”

Section: Factors Affecting Fuel Consumptionmentioning

confidence: 99%

Strategies for Reducing Automobile Fuel Consumption

Romero,

Correa,

Ariza Echeverri

et al. 2024

Applied Sciences

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In recent times, the significance of advancing road transportation technologies has notably increased. This is mainly driven by the escalating need for road transportation systems that are not only safe but also environmentally sustainable. Moreover, enhancing fuel efficiency in road vehicles (i.e., automobiles) holds the potential to contribute significantly to the reduction of a country’s economic vulnerability (i.e., improved energy security), by reducing the reliance on energy imports. While research reports and scientific journals provide valuable information on automobile fuel consumption, it is essential to continually update our understanding of the factors that influence it. This document aims to review and describe the various factors, both direct and indirect, that play a role in an automotive’s overall energy efficiency and fuel consumption.

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Section: Factors Affecting Fuel Consumptionmentioning

confidence: 99%

Strategies for Reducing Automobile Fuel Consumption

Romero,

Correa,

Ariza Echeverri

et al. 2024

Applied Sciences

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show abstract

“…Furthermore, the examination of individual traits revealed that education levels and prior accident experiences had the most effects on the self-reported aggressive driving behavior (SADB) and the subjective assessment of others' aggressive driving behavior OADB). In another study, the effects on fuel consumption and emissions of drivers being distracted by their phones at traffic signals were studied by Alemdar, Kayacı Çodur [31]. Roadside observations at crossroads in Turkey's Erzurum Province were used for the study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, the study discovered that at the chosen crossings, these diversions led to an annual fuel use of roughly 177,025 L and emissions of 0.294 kg of NO x and 251.68 kg of CO 2 . The study emphasizes the necessity of education and actions to lessen the negative effects that certain driving practices have on the environment and the economy [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Cross-Cultural Behaviors: A Comparative Analysis of Driving Behaviors in Pakistan and China

Yousaf,

2024

Sustainability

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When analyzing road safety across cultural boundaries, driver behavior is a crucial component to consider. Given that driver behavior directly affects both the likelihood of accidents and the severity of their implications, it is crucial to comprehend and analyze it. The present study examined the differences in dangerous, aberrant, and positive driving behaviors across China and Pakistan. The effects of these behaviors on road traffic accidents were also considered. In the study, 1253 respondents completed a questionnaire package consisting of the Dula Dangerous Driving Index (DDDI), Aberrant Driving Behavior Questionnaire (DBQ), Positive Driving Behavior Scale (PDBS), and items related to demographics. Generalized linear models were utilized to compare and determine the factors responsible for dangerous driving behaviors. Mean scores for DBQ and PDBS items were compared. Finally, binary logistic regression models were used to find the factors responsible for traffic accidents across both countries. The results indicated that aggressive and risky driving predicted traffic accidents in both countries, followed by errors and violations, which also predicted traffic accidents significantly. Positive driving behaviors predicted accidents negatively in both samples. Furthermore, it was found that Chinese drivers compared to Pakistani drivers are less aggressive and risk-taking and commit fewer violations and errors while driving. To increase road safety in Pakistan, traffic laws must be strictly enforced uniformly, and violations must result in severe penalties, i.e., demerit points or cancellation of a driver’s license. Programs for road safety awareness and education must be expanded. Employing national culturally concentrated road safety strategies may be a more effective way to encourage safe driving behaviors.

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“…In general, driver distraction detection is of great significance for improving road safety, preventing traffic accidents, and promoting the development of intelligent transportation systems [4]. By reducing driver distraction, we can create a safer, more efficient, and sustainable road transportation environment [5].…”

Section: Introductionmentioning

confidence: 99%

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Zhang,

Yang,

2024

Vehicles

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Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.

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Environmental Effects of Driver Distraction at Traffic Lights: Mobile Phone Use

Cited by 5 publications

References 38 publications

Strategies for Reducing Automobile Fuel Consumption

Strategies for Reducing Automobile Fuel Consumption

Cross-Cultural Behaviors: A Comparative Analysis of Driving Behaviors in Pakistan and China

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

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