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RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning

Summary: RLER-TTE introduces a Decision Maker–Predictor pipeline for ER-TTE, triggering the expensive predictor only when beneficial. An RL-based online agent solves the MDP with attention-based spatio-temporal encoding and curriculum learning, delivering end-to-end training and strong real-world performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7064
Venue
SIGMOD
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,390 | 27.72%
DOI
10.1145/3709721

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