1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
Choice Sets and Smart Card Data In Public Transport Route Choice Models: Generated vs. Empirical Sets
Georges Sfeir, Filipe Rodrigues, Ravi Seshadri +1
This study evaluates path sets generation for route choice models in multimodal public transportation networks, using both conventional (network algorithms) and empirical (smart ca…
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…