Transformers \`a Grande Vitesse
Farid Arthaud, Guillaume Lecoeur, Alban Pierre

TL;DR
This paper introduces a transformer-based method for real-time train delay prediction across entire rail networks, capturing delay propagation phenomena and outperforming existing techniques.
Contribution
It presents a novel transformer architecture with pre-trained embeddings for large-scale, real-time train delay forecasting, addressing complex network interactions.
Findings
Effective delay prediction for over 3000 trains during peak hours
Captures delay propagation phenomena in railway networks
Outperforms existing prediction methods
Abstract
Robust travel time predictions are of prime importance in managing any transportation infrastructure, and particularly in rail networks where they have major impacts both on traffic regulation and passenger satisfaction. We aim at predicting the travel time of trains on rail sections at the scale of an entire rail network in real-time, by estimating trains' delays relative to a theoretical circulation plan. Predicting the evolution of a given train's delay is a uniquely hard problem, distinct from mainstream road traffic forecasting problems, since it involves several hard-to-model phenomena: train spacing, station congestion and heterogeneous rolling stock among others. We first offer empirical evidence of the previously unexplored phenomenon of delay propagation at the scale of a railway network, leading to delays being amplified by interactions between trains and the network's…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Railway Systems and Energy Efficiency · Railway Engineering and Dynamics
MethodsEmirates Airlines Office in Dubai
