Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

Chan, Yin-Hei and Lui, Andrew Kwok-Fai and Ng, Sin-Chun (2022) Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia.

[img]
Preview
Text
Accepted Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview
Official URL: http://dx.doi.org/10.1109/smc52423.2021.9658993

Abstract

Traffic flow prediction is an important component of a modern intelligent transport system. Building an effective model for short term traffic flow prediction model is challenging. Traffic is spatial temporal in nature. A traffic flow prediction model should consider an appropriate scope of neighbourhood of traffic. To address the needs of a dynamic scope of neighbourhood. We introduce a novel gated recurrent unit variant call Neighbor Selecting Gated Recurrent Unit(NSGRU). NSGRU feature a learn-able spatial kernel with distance based K-nearest neighbor trimming scheme. Embedded external traffic knowledge are used to aid with the learning of spatial kernel. The NSGRU was evaluated with a quantized real world dataset and observed consistent improvement over baseline models.

Item Type: Conference or Workshop Item (Paper)
Keywords: Traffic flow prediction, Intelligent transport system, Knowledge engineering, Visualization, Roads, Predictive models, Logic gates, Kernel, Conferences
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 09 Jun 2022 10:52
Last Modified: 09 Jun 2022 10:52
URI: https://arro.anglia.ac.uk/id/eprint/707670

Actions (login required)

Edit Item Edit Item