# PAtt: A Pattern Attention Network for ETA Prediction Using Historical Speed Profiles

arXiv: https://arxiv.org/abs/2601.13793 · HTML: https://mlsanity.com/paper/2601.13793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Meta-Gradient Reinforcement Learning](https://mlsanity.com/paper/1805.09801/) (2018)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision](https://mlsanity.com/paper/1802.02147/) (2018)
- [DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps](https://mlsanity.com/paper/2208.06979/) (2022)
- [DeeprETA: An ETA Post-processing System at Scale](https://mlsanity.com/paper/2206.02127/) (2022)
- [Seamless Multimodal Transportation Scheduling](https://mlsanity.com/paper/1807.09676/) (2018)
- [Integrating Parcel Deliveries into a Ride-Pooling Service -- An Agent-Based Simulation Study](https://mlsanity.com/paper/2205.04718/) (2022)
- [The Share-a-Ride Problem with mixed ride-hailing and logistic vehicles](https://mlsanity.com/paper/2403.11944/) (2024)

## Abstract (arXiv)

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.