Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

Yin Hei Chan; Andrew Kwok Fai Lui; Sin Chun Ng

doi:10.1109/SMC52423.2021.9658993

Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

Yin Hei Chan
, Andrew Kwok Fai Lui
, Sin Chun Ng

Hong Kong Metropolitan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Pages	857-862
Number of pages	6
ISBN (Electronic)	9781665442077
DOIs	https://doi.org/10.1109/SMC52423.2021.9658993
Publication status	Published - 2021
Event	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia Duration: 17 Oct 2021 → 20 Oct 2021

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Conference

Conference	2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/Territory	Australia
City	Melbourne
Period	17/10/21 → 20/10/21

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10.1109/SMC52423.2021.9658993

Cite this

@inproceedings{bd87da9324e64a909dcfcb2b6800357e,

title = "Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit",

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.",

author = "Chan, \{Yin Hei\} and Lui, \{Andrew Kwok Fai\} and Ng, \{Sin Chun\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 ; Conference date: 17-10-2021 Through 20-10-2021",

year = "2021",

doi = "10.1109/SMC52423.2021.9658993",

language = "English",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

pages = "857--862",

booktitle = "2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021",

}

Chan, YH, Lui, AKF & Ng, SC 2021, Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit. in 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 857-862, 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021, Melbourne, Australia, 17/10/21. https://doi.org/10.1109/SMC52423.2021.9658993

Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit. / Chan, Yin Hei; Lui, Andrew Kwok Fai; Ng, Sin Chun.
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021. 2021. p. 857-862 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

AU - Chan, Yin Hei

AU - Lui, Andrew Kwok Fai

AU - Ng, Sin Chun

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85124276960

U2 - 10.1109/SMC52423.2021.9658993

DO - 10.1109/SMC52423.2021.9658993

M3 - Conference contribution

AN - SCOPUS:85124276960

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 857

EP - 862

BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021

T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021

Y2 - 17 October 2021 through 20 October 2021

ER -

Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

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