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

• URL: http://arxiv.org/abs/2501.15493v1
• Date: Sun, 26 Jan 2025 11:49:34 GMT
• Title: RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning
• Authors: Zhihan Zheng, Haitao Yuan, Minxiao Chen, Shangguang Wang,
• Abstract summary: En Route Travel Time Estimation aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. Existing methods ignore the complexity and dynamism of real-world traffic systems, resulting in significant gaps in efficiency and accuracy in real-time scenarios. This paper proposes a novel framework that redefines the path implementation of ER-TTE to achieve highly efficient and effective predictions.
• Score: 5.4674463400564886
• License:
• Abstract: En Route Travel Time Estimation (ER-TTE) aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. However, existing methods ignore the complexity and dynamism of real-world traffic systems, resulting in significant gaps in efficiency and accuracy in real-time scenarios. Addressing this issue is a critical yet challenging task. This paper proposes a novel framework that redefines the implementation path of ER-TTE to achieve highly efficient and effective predictions. Firstly, we introduce a novel pipeline consisting of a Decision Maker and a Predictor to rectify the inefficient prediction strategies of current methods. The Decision Maker performs efficient real-time decisions to determine whether the high-complexity prediction model in the Predictor needs to be invoked, and the Predictor recalculates the travel time or infers from historical prediction results based on these decisions. Next, to tackle the dynamic and uncertain real-time scenarios, we model the online decision-making problem as a Markov decision process and design an intelligent agent based on reinforcement learning for autonomous decision-making. Moreover, to fully exploit the spatio-temporal correlation between online data and offline data, we meticulously design feature representation and encoding techniques based on the attention mechanism. Finally, to improve the flawed training and evaluation strategies of existing methods, we propose an end-to-end training and evaluation approach, incorporating curriculum learning strategies to manage spatio-temporal data for more advanced training algorithms. Extensive evaluations on three real-world datasets confirm that our method significantly outperforms state-of-the-art solutions in both accuracy and efficiency.

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