The assessment of driving behavior, vital for ensuring passenger safety and optimizing resource utilization in transportation systems, faces challenges due to inherent unpredictability and complexity. This study addresses these challenges by introducing innovative methodologies for the extraction, classification, and prediction of diverse driving patterns, utilizing data from "On Board Diagnostics" (OBD) ports in modern vehicles. In this approach, a comprehensive suite of advanced Machine Learning (ML) and Deep Learning (DL) stechniques, including Convolutional Neural Networks (CNNs), Optimized Spectral Neural Classification (OSNCA), and Fuzzy Logical Feature Selection (FLFS), are employed. These techniques are instrumental in overcoming limitations of previous models, enhancing accuracy in driving behavior evaluation. The utilization of FLFS in conjunction with OSNCA represents a novel method in driver behavior analysis. By applying these techniques, driver characteristics and behaviors are systematically categorized into distinct classes, facilitating a nuanced understanding of driving dynamics. The integration of these advanced methodologies not only furthers the analysis of driver behavior but also significantly improves classification and prediction capabilities. This research contributes to the development of safer, more efficient transportation networks by offering a refined approach to the analysis, categorization, and prediction of driver behavior, thereby advancing the field of driving behavior analysis.
This study evaluates the influence of brands listed in the Borsa Istanbul (BIST) Sustainability 25 Index on fostering sustainable consumption behaviors, a critical issue in contemporary society. An analysis was conducted on the sustainability-related content from these brands' websites and Instagram accounts. The BIST Sustainability Index, which serves as a benchmark for companies in Türkiye to develop policies related to environmental, social, and corporate governance (ESG) risks, was utilized to select the sample. This index plays a pivotal role in informing responsible investors about corporate sustainability practices. The investigation primarily focused on how these brands communicate sustainability on their Instagram accounts through detailed content analysis. It was observed that, while comprehensive information on sustainability initiatives is presented on corporate websites, this communication is not adequately reflected on Instagram platforms. Given the mandatory disclosure of sustainability activities by companies listed in the BIST Sustainability 25 Index, the importance of effective communication on social media, in addition to website information dissemination, is underscored. Among the brands, Arçelik was identified as the most active in sharing sustainability-related posts on Instagram. Although these posts received a considerable number of likes, they garnered minimal user engagement in terms of comments. The study reveals a discrepancy between the intensity of sustainability activities undertaken by these indexed companies and their representation on social media channels. Consequently, it is recommended that these businesses place a greater emphasis on incorporating sustainability themes within their social media marketing communications. This study underscores the need for a more robust digital media strategy to reflect sustainability efforts accurately, thereby contributing to the broader discourse on sustainable consumption and the efficacy of digital marketing.
Open Access
Journal Metrics
Journal Overview
Associated Journals