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

Seunghyeon Lee, Dong Ngoduy, Fang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationAI 2021
Subtitle of host publicationAdvances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Proceedings
EditorsGuodong Long, Xinghuo Yu, Sen Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages417-427
Number of pages11
ISBN (Print)9783030975456
DOIs
StatePublished - 2022
Event34th Australasian Joint Conference on Artificial Intelligence, AI 2021 - Virtual, Online
Duration: 2 Feb 20224 Feb 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13151 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference34th Australasian Joint Conference on Artificial Intelligence, AI 2021
CityVirtual, Online
Period2/02/224/02/22

Keywords

  • Deep learning
  • Incremental queue accumulations
  • Lane-based control delay
  • Long short-term memory
  • Queue-length estimation

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