44 citations · 109 across the 7 of their papers we have counts for
7 papers
Physics-informed neural networks for parameter learning of wildfire spreading
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +1
Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, interve…
Multi-gated perimeter flow control for monocentric cities: Efficiency and equity
Ruzanna Mat Jusoh, Konstantinos Ampountolas
A control scheme for the multi-gated perimeter traffic flow control problem of cities is presented. The proposed scheme determines feasible and optimally distributed input flows fo…
Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems
Theocharis Apostolakis, Konstantinos Ampountolas
This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotiv…
Energy-based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons
Theocharis Apostolakis, Michail A. Makridis, Anastasios Kouvelas +1
Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comforta…
The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise Control Systems
Konstantinos Ampountolas
This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) sy…
An interpretable wildfire spreading model for real-time predictions
Konstantinos Vogiatzoglou, Costas Papadimitriou, Konstantinos Ampountolas +3
Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trus…
Short term prediction of demand for ride hailing services: A deep learning approach
Long Chen, Piyushimita, Thakuriah +1
As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and re…