An eco-aware framework for AI-based analysis of contextually enriched automotive trip data

The transport sector is one of the main contributors to gas emissions and the greenhouse effect. Mobility as a Service seeks to combine different means of transport (public transport, bicycles, cars, etc.) in an integrated way to solve this problem. Raising drivers' awareness and advising them to drive more efficiently and economically can help reduce environmental impact. Ultimately, eco-driving can help save fuel, money and provide higher passenger satisfaction during the trip. The research uses a contextually enriched automotive data set collected via OBD, during passenger transportation in private cars. A multilevel analysis was conducted using a variety of approaches. With AI-based analysis, using unsupervised learning, we identified five groups of similarities as a function of speed and acceleration. The efficiency of each driver was examined and speed and acceleration/deceleration were noted as the most influential factors. Also, passengers' perception of driving quality is negatively affected by sudden acceleration and deceleration. Passengers have a poor perception of the driving efficiency and additional efforts should be made to educate them about the environmental aspect of driving. Finally, the main contribution is the eco-aware framework, which covers and describes the entire process from data collection to analysis. The collected data are publicly available and include 110 trips represented by more than 65,000 data points.