Search Results for author:
Found 4 papers, 1 papers with code
Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction
We identify inherent task features and STL characteristics that can help us to predict whether a group of tasks should be learned together using MTL or if they should be learned independently using STL.
Artificial Intelligence for Smart Transportation
Second, we provide an overview of how AI can aid decision-making with a focus on transportation.
Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service
Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs.
Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets
In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
