A Multitask Learning Model for Traffic Flow and Speed Forecasting

Kunpeng Zhang,Jiang Deng,Zhaoju Zhu,Lan Wu

doi:10.1109/access.2020.2990958

Abstract

Intelligent Transportation Systems (ITS) research and applications benefit from accurate short-term traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the performance of the MTL-GRU, feature engineering is introduced to select the most informative features for the forecasting. Then, based on real-world datasets, numerical results show that the MTL-GRU can well estimate traffic flow and speed simultaneously, and performs better than other counterparts. Experiments also show that the deep learning based MTL-GRU model can overpower the bottleneck caused by enlarging training datasets and continue to gain benefits. The results suggest the proposed MTL-GRU model with residual mappings is promising to forecast short-term traffic state.

Highlights

In Intelligent Transportation Systems (ITS), short-term traffic state forecasting aims at anticipating traffic conditions based on historical observations
To cast light on those problems, this paper probes in the following strategies: (1) With residual mappings [31], the deep learning based multitask learning Gated Recurrent Units (MTL-GRU) model is proposed to forecast traffic flow and traffic speed simultaneously with a deeper network structure; (2) With the help of statistical tools, this paper conducts feature engineering to extract the most informative features for the proposed multitask learning (MTL)-GRU model; (3) This study investigates the impact of the size of training data on model performance
It can be found that the MTL-GRU model achieves the best performance when forecasting traffic flow and traffic speed for six stations

Summary

INTRODUCTION

In Intelligent Transportation Systems (ITS), short-term traffic state forecasting aims at anticipating traffic conditions based on historical observations. Traffic state forecasting is crucial for the planning and development of the traffic management and control system [1]–[3]. In the transportation research area, deep learning is increasingly presented in traffic state forecasting and achieves attractive performances [7], [8]. To the best knowledge of the authors, the following questions have not been well addressed: (1) Given that multitask learning (MTL) is gaining remarkable performance in other domains [9], [10], how can traffic state forecasting stand to benefit from MTL? To address the abovementioned problems, this paper proposes a deep learning based multitask learning framework with Gated Recurrent Units [11] (MTL-GRU) to forecast traffic flow and traffic speed simultaneously. Zhang et al.: Multitask Learning Model for Traffic Flow and Speed Forecasting

LITERATURE REVIEW

MULTITASK LEARNING

FEATURE ENGINEERING WITH STATISTICAL INTERPRETATION

EXPERIMENTS

Findings

CONCLUSION