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

TL;DR

This paper introduces PAtt, a lightweight attention-based neural network that effectively captures spatio-temporal traffic patterns for improved ETA prediction, outperforming existing models on real-world datasets.

Contribution

We propose a novel attention mechanism that models spatio-temporal dependencies for ETA prediction, enhancing accuracy while maintaining computational efficiency.

Findings

Outperforms baseline models on real-world datasets

Effectively integrates historical speed profiles with real-time data

Maintains lightweight architecture suitable for scalable deployment

Abstract

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.