Sustainable transportation through data science: Case studies from the automotive industry

Abstract

The automotive industry is undergoing transformative changes propelled by the progress in technology, considerations for the environment, and the evolving tastes of consumers. This quantitative research endeavors to investigate the impact of data-driven advancements in the automotive sector. The research methodology employed a purposive sampling technique, targeting diverse stakeholders within the Chinese automotive industry. A structured questionnaire served as the primary data collection tool. Through direct interactions and visits, 900 questionnaires were distributed over three days, yielding a robust response of 850 returned surveys. Following the removal of invalid responses, the study culled valid data from 800 participants. The collected data underwent analysis using SPSS statistical software. Findings reveal significant trends in the industry, such as the increasing adoption of electric vehicles, evolving customer preferences for advanced features, and the potential impact of ride-sharing and car-sharing services on individual car ownership. Furthermore, the investigation identifies the crucial role of data analysis, predictive analytics, IoT devices, and big data in shaping various aspects of the automotive sector. The study’s novelty lies in its quantitative approach, providing objective insights into demographic characteristics, industry trends, and participants’ perspectives. The study’s exploration of data-driven design processes and their role in fostering innovation and user-friendly vehicles adds a distinctive layer to understanding the transformative impact of data science on automotive development. Overall, this research contributes valuable knowledge for industry practitioners, policymakers, and scholars interested in the intersection of data science and automotive advancements.