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Found 8 papers, 1 papers with code
Less Is More - On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP
Furthermore, we propose ensembles of different sparsification levels allowing models to focus on the most promising parts while also allowing information flow between all nodes of a TSP instance.
Integrated Charging Scheduling and Operational Control for an Electric Bus Network
We propose a hierarchical control framework to solve this problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer.
Controlled Descent Training
Dynamic label augmentation is proposed within the framework of gradient descent training where the convergence of training loss is controlled.
Conflict-free Charging and Real-time Control for an Electric Bus Network
The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks.
A Predictive Chance Constraint Rebalancing Approach to Mobility-on-Demand Services
We then formulate a SMPC for the autonomous ride-hailing service and integrate demand predictions with uncertainty bounds into a receding horizon MoD optimization.
Short-term traffic prediction using physics-aware neural networks
This algorithm is based on a physics-aware recurrent neural network.
Constrained Policy Gradient Method for Safe and Fast Reinforcement Learning: a Neural Tangent Kernel Based Approach
This paper presents a constrained policy gradient algorithm.
Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
This system is formulated as a chemical reaction network where road cells are interpreted as compartments, the transfer of vehicles from one cell to the other is seen as a chemical reaction between adjacent compartment and the density of vehicles is seen as a concentration of reactant.
