HyBrid bUs geoSpatial Data and Network Analysis (BusDNA)
Start date: 01/01/2019
End date : 31/12/2020
HyBrid bUs geoSpatial Data and Network Analysis (BusDNA) aims at designing and validating new methods allowing understanding, predicting and improving performance of electrified buses in reference to specific operational conditions.
The goal of this project is to design and validate new methods allowing to understand, predict and improve performance of e-buses in reference to specific operational conditions. These methods will replace currently used rules of thumb based on average operational characteristics. Novel, data-driven visual analytics methods, currently employed in the field of biomedicine and clinical research, will be used on the new type of event-based data, generated by ebuses during real-time operations. This will enable experts to explore complex datasets and gain deep insight into their content and structure, allowing to determine causes of inefficiencies and to proactively react when the conditions occur. The combination of large amounts of collected data with precise geocomputational city models, applied visual analytics and incorporation of machine learning methods will be used to predict the impact of new operation strategies. This will allow for optimal organization of existing fleet operations, improvement of electrical-to-fuel drive strategies, and projection to new usecase scenarios in other cities.
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg
- Luxembourg Institute of Science and Technology (LIST)
- Dr Sune Nielsen