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cs.MA2026
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
Åukasz Gorczyca, Kacper Drozd, MichaÅ Bujak +1
Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand patterns. While they successf…
cs.MA2025
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
Anastasia Psarou, Ahmet Onur Akman, Åukasz Gorczyca +3
Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, with human drivers potent…
cs.MA2025
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
Ahmet Onur Akman, Anastasia Psarou, Åukasz Gorczyca +3
RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient…