Artificial Intelligence meeting
High-tech Real-world Sensing

One Core, Multiple Services

Combining data-driven ML models for vehicular energy and emissions estimation with AI-powered travel-mode detection algorithms, trip chains of transport agents are precisely analyzed. Quantitative reports will be provided for users at different temporal scales, which will be later translated into customized eco-driving guidelines using a complementary ML model. A common core serves fleets, transportation consulting and authorities, and even passengers and individual drivers to guide them step-by-step towards mitigation of transport emissions footprints.

State-of-the-Technology Sensors

Advanced Time-Series Models

Thanks to the huge amount of data we have been continuously collecting since 2017, advanced vehicle-specific time-series models are trained for micro- and meso-scale energy and emissions footprint estimation. The spatial and temporal domain of models' applications are then extended using ensemble learning and Generative Adversarial Network (GAN) algorithms. The quantitative estimates of base models are finally translated to human-understandable language using our AI-powered recommender engines for eco-driving guidance and generating optimization strategies.