Abstract
This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation. These ML models tend to be very complex and non-linear in nature, making it challenging to achieve fast inferences and are computationally heavy for real-time signal control implementation. In this paper, a simple yet accurate hybrid modeling method is proposed to predict traffic delay one-step ahead with the model made suitable for real-time implementation to control traffic flow. Since real-time road-side measurements are recorded in unstructured form, the paper also discusses other issues related to data extraction and the pre-processing process. Finally, a simple signal control loop is developed to demonstrate the proposed modeling approach, which has shown advantages in model accuracy and computation efficiency compared against several existing modeling methods.
Original language | English |
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Pages (from-to) | 1384-1394 |
Number of pages | 11 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 24 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2023 |
Funding
This work was supported by the U.S. Department of Energy (U.S DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office's funding DE-LC-000L084, and in part by the National Science Foundation Major Research Instrumentation award 1920304. This manuscript has been authored by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the U.S. DOE.
Funders | Funder number |
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National Science Foundation | 1920304, DEAC05-00OR22725 |
U.S. Department of Energy | |
Wind Energy Technologies Office | DE-LC-000L084 |
Keywords
- AI-based signal control
- Traffic prediction
- hybrid neural networks
- recurrent neural networks
- signalized intersection