Eco-efficient driving pattern evaluation for sustainable road transport based on contextually enriched automotive data

Abstract

The transportation sector is a significant contributor to greenhouse gas emissions' accumulation in the atmosphere. With the support of information and communication technology and data science, we can better understand the driving patterns that influence eco-efficiency. The key component for impactful research in this domain is a high-quality source of contextually enriched automotive data, enabling interdisciplinary study from environmental sustainability, automotive engineering, behavioural science, telecommunications and transportation science perspectives. To facilitate the creation of such a data set, this paper presents a framework for the collection and contextual enrichment of automotive data. The framework utilizes smartphones to collect the vehicle's On-Board Diagnostics (OBD) or Controller Area Network (CAN) data and enriches it with Internet-based and built-in smartphone sensor data. A data collection experiment was performed using the established framework with 9 drivers collecting more than 90 h of driving data, forming a contextually enriched automotive data set. A special emphasis in the analysis of the collected data was placed on deriving and calculating a metric for ranking the drivers according to their eco-efficient driving patterns, a valuable insight that can be used to improve the transportation sustainability.