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.
Original language | English |
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Title of host publication | AI 2021 |
Subtitle of host publication | Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings |
Editors | Guodong Long, Xinghuo Yu, Sen Wang |
Publisher | Springer |
Pages | 417-427 |
Number of pages | 11 |
ISBN (Print) | 9783030975456 |
DOIs | |
Publication status | Published - 2022 |
Event | Australasian Joint Conference on Artificial Intelligence 2021 - Online, Sydney, Australia Duration: 2 Feb 2022 → 4 Feb 2022 Conference number: 34th https://link.springer.com/book/10.1007/978-3-030-97546-3 (Website) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13151 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Australasian Joint Conference on Artificial Intelligence 2021 |
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Abbreviated title | AI 2021 |
Country/Territory | Australia |
City | Sydney |
Period | 2/02/22 → 4/02/22 |
Internet address |
Keywords
- Deep learning
- Incremental queue accumulations
- Lane-based control delay
- Long short-term memory
- Queue-length estimation