In this paper, we propose an ETA model (Estimated Time of Arrival) that leverages an attention mechanism over historical road speed patterns. As autonomous driving and intelligent transportation systems become increasingly prevalent, the need for accurate and reliable ETA estimation has grown, playing a vital role in navigation, mobility planning, and traffic management. However, predicting ETA remains a challenging task due to the dynamic and complex nature of traffic flow. Traditional methods often combine real-time and historical traffic data in simplistic ways, or rely on complex rule-based computations. While recent deep learning models have shown potential, they often require high computational costs and do not effectively capture the spatio-temporal patterns crucial for ETA prediction. ETA prediction inherently involves spatio-temporal causality, and our proposed model addresses this by leveraging attention mechanisms to extract and utilize temporal features accumulated at each spatio-temporal point along a route. This architecture enables efficient and accurate ETA estimation while keeping the model lightweight and scalable. We validate our approach using real-world driving datasets and demonstrate that our approach outperforms existing baselines by effectively integrating road characteristics, real-time traffic conditions, and historical speed patterns in a task-aware manner.

翻译：本文提出了一种利用历史道路速度模式注意力机制的到达时间（ETA）预测模型。随着自动驾驶和智能交通系统日益普及，对准确可靠的ETA预测需求不断增长，这在导航、出行规划和交通管理中发挥着至关重要的作用。然而，由于交通流的动态性和复杂性，ETA预测仍是一项具有挑战性的任务。传统方法通常以简单方式结合实时和历史交通数据，或依赖复杂的基于规则的计算。尽管近期深度学习模型展现出潜力，但它们往往需要高昂的计算成本，且未能有效捕捉对ETA预测至关重要的时空模式。ETA预测本质上涉及时空因果关系，我们提出的模型通过利用注意力机制提取并利用沿路径每个时空点累积的时间特征来解决这一问题。该架构在保持模型轻量化和可扩展性的同时，实现了高效准确的ETA估计。我们使用真实世界驾驶数据集验证了所提方法，结果表明该方法通过以任务感知的方式有效整合道路特征、实时交通状况和历史速度模式，性能优于现有基线模型。