1 citations · 2 across the 5 of their papers we have counts for
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Khadidja Kadem, Mostafa Ameli, Carlos Lima Azevedo +2
In multimodal transportation systems, shared mobility services (SMSs) are promoted for their potential to enhance flexibility and reduce congestion. However, SMS demand is often co…
Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study
Dang Viet Anh Nguyen, Carlos Lima Azevedo, Tomer Toledo +1
Reinforcement learning-based traffic signal control (RL-TSC) has emerged as a promising approach for improving urban mobility. However, its robustness under real-world disruptions…
Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume
Silke K. Kaiser, Filipe Rodrigues, Carlos Lima Azevedo +1
Graph Neural Networks have shown strong performance in traffic volume forecasting, particularly on highways and major arterial networks. Applying them to urban settings, however, p…
Multi-Graph Inductive Representation Learning for Large-Scale Urban Rail Demand Prediction under Disruptions
Dang Viet Anh Nguyen, J. Victor Flensburg, Fabrizio Cerreto +4
With the expansion of cities over time, URT (Urban Rail Transit) networks have also grown significantly. Demand prediction plays an important role in supporting planning, schedulin…