3 citations · 4 across the 3 of their papers we have counts for
3 papers
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
Kai Jungel, Dario Paccagnan, Axel Parmentier +1
When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this back…
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows
Léo Baty, Kai Jungel, Patrick S. Klein +2
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same…
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
Kai Jungel, Axel Parmentier, Maximilian Schiffer +1
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and tr…