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Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft +1
Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations. Passenger demand is hierarchical: origin--destination (OD) flows aggregate…
Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis
Javier Yanes-Pulido, Filipe Rodrigues
Accurate forecasting of transportation dynamics is essential for urban mobility and infrastructure planning. Although recent work has achieved strong performance with deep learning…
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…
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes
Xiaoyi Wu, Ravi Seshadri, Filipe Rodrigues +1
Tradable credit schemes (TCS) are an increasingly studied alternative to congestion pricing, given their revenue neutrality and ability to address issues of equity through the init…
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…