Real-Time Prediction of the Lane-Based Delay for Group-Based Adaptive Traffic Operations Using Long Short-Term Memory

Lee, S; Ngoduy, D; Chen, F

Real-Time Prediction of the Lane-Based Delay for Group-Based Adaptive Traffic Operations Using Long Short-Term Memory

Lee, S

Ngoduy, D Chen, F

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Publisher:: SPRINGER INTERNATIONAL PUBLISHING AG
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 417-427
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lee, S https://orcid.org/0000-0003-2356-3677
dc.contributor.author	Ngoduy, D
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.editor	Long, G
dc.contributor.editor	Yu, X
dc.contributor.editor	Wang, S
dc.date	2022-02-02
dc.date.accessioned	2023-03-11T07:52:11Z
dc.date.available	2023-03-11T07:52:11Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 417-427
dc.identifier.isbn	9783030975456
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/167047
dc.description.abstract	This study proposes a deterministic real-time lane-based control delay model for traffic operations based on Long Short-Term Memory (LSTM). Our proposed framework includes a model-based approach to compute the control delay in an individual lane for a single cycle and a data-driven approach to predict the queueing profiles and adjustment factors used in the future control delay formula. This framework not only secures an excellent performance of the proposed model under a wide range of data availability but also guarantees a lower computational burden for a real-time non-linear optimisation process in adaptive control logic. The modified deep learning method has three primary components in the proposed architecture of the lane-based control delay model cycle-by-cycle. First, the data-driven and model-based approaches are integrated to improve the reliability and the accuracy of the control delay predictive formula. Second, the novel LSTM network is constructed to predict a cycle-based control delay in an individual lane while minimising inherent errors in the algorithm. Third, the predicted queue lengths at inflection points and adjustment factors are used to construct the delay polygons in the future cycle. Numerical simulations are set up using both synthetic and real-world data to give insights into the proposed model's performance compared to the existing models.
dc.language	en
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	34th Australasian Joint Conference on Artificial Intelligence (AI)
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-3-030-97546-3_34
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Real-Time Prediction of the Lane-Based Delay for Group-Based Adaptive Traffic Operations Using Long Short-Term Memory
dc.type	Conference Proceeding
utslib.citation.volume	13151 LNAI
utslib.location.activity	Univ Technol Sydney, ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-11T07:52:09Z
pubs.finish-date	2022-02-04
pubs.publication-status	Published
pubs.start-date	2022-02-02
pubs.volume	13151 LNAI

Abstract:

This study proposes a deterministic real-time lane-based control delay model for traffic operations based on Long Short-Term Memory (LSTM). Our proposed framework includes a model-based approach to compute the control delay in an individual lane for a single cycle and a data-driven approach to predict the queueing profiles and adjustment factors used in the future control delay formula. This framework not only secures an excellent performance of the proposed model under a wide range of data availability but also guarantees a lower computational burden for a real-time non-linear optimisation process in adaptive control logic. The modified deep learning method has three primary components in the proposed architecture of the lane-based control delay model cycle-by-cycle. First, the data-driven and model-based approaches are integrated to improve the reliability and the accuracy of the control delay predictive formula. Second, the novel LSTM network is constructed to predict a cycle-based control delay in an individual lane while minimising inherent errors in the algorithm. Third, the predicted queue lengths at inflection points and adjustment factors are used to construct the delay polygons in the future cycle. Numerical simulations are set up using both synthetic and real-world data to give insights into the proposed model's performance compared to the existing models.

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http://hdl.handle.net/10453/167047