AbstractEmissions from road traffic contribute to climate change. One approach to reducing the carbon footprint is providing eco-driving feedback so that drivers adapt their driving style. Research about the impact of eco-feedback on energy consumption is the basis for designing a mobile eco-driving feedback information system that supports drivers in reducing fuel consumption. This work develops design knowledge from existing knowledge. Subsequently, we implement a prototypical instantiation based on the derived knowledge. Insights from a field study suggest that our design artifact allows most drivers to decrease fuel consumption by 4% on average. The paper’s theoretical contribution is a set of design principles and an architecture of the proposed mobile eco-driving feedback information system. One recommendation is to provide normative feedback that compares drivers with each other. This feedback appears to encourage drivers to decrease their fuel consumption additionally. The design knowledge may support researchers and practitioners in implementing efficient eco-driving feedback information systems.
Abstract
Artificial Intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.
Examination of Man-Hours Quality Adjustment Method for System Development Based on Design Knowledge
