AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Giovanni Buroni,Gianluca Bontempi,Bertrand Lebichot

doi:10.1109/access.2021.3083412

Giovanni Buroni, Gianluca Bontempi + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3083412

Copy DOI

Abstract

Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models.

Highlights

Road transport is one of the main concerns in modern cities
We extend previous studies by proposing AST-multi-task learning (MTL), a novel MTL strategy based on a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to take advantage of the information shared across traffic flow and speed
AND DISCUSSION we present the results of the proposed model and compare them with the other counterparts for the Belgian freeway system and the road network of the Bruxelles-Capital Region

Summary

Introduction

Road transport is one of the main concerns in modern cities. This sector is responsible for different severe problems, such as pollution, road congestion, long journey times through the city, and so forth. These have negative social, environmental, and economic impacts affecting the life of citizens [1]. A lot of research has been devoted to the traffic forecasting field. Traffic forecasting has been a vibrant field of research in both the academia and private sector

Methods

Results

Conclusion