e-space
Manchester Metropolitan University's Research Repository

A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow

Bartlett, Zoe, Han, Liangxiu ORCID logoORCID: https://orcid.org/0000-0003-2491-7473, Nguyen, Trung Thanh and Johnson, Princy (2019) A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow. IEEE Access, 7. pp. 153533-153541.

[img]
Preview
Published Version
Available under License Creative Commons Attribution.

Download (630kB) | Preview

Abstract

Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-term patterns on road traffic flow prediction. Providing more temporal contextual information through the use of different temporal data segments could improve prediction results. In this paper, we have investigated different magnitudes of temporal patterns, such as short-term and long-term, through the use of different temporal data segments to understand how contextual temporal data can improve prediction. Furthermore, to learn temporal patterns dynamically, we have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features, and during online learning, the updating scheme dynamically determines how useful a temporal data segment (short and long-term temporal patterns) is for prediction, and weights it accordingly for use in the regression model. Therefore, the framework can include short-term and relevant long-term patterns in the regression model leading to improved prediction results. We have conducted a thorough experimental evaluation with a real dataset containing daily, weekly, monthly and yearly data segments. The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved predication results by 10.8% when compared with a deep gated recurrent unit model.

Impact and Reach

Statistics

Activity Overview
6 month trend
147Downloads
6 month trend
178Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

View Item View Item