Adaptive Real-Time Prediction Model for Short-Term Traffic Flow Uncertainty
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
In order to promote the accuracy of short-term traffic flow forecasting, an adaptive real-time model consisting of two important stages is proposed. The first stage encloses a novel online sequence extreme learning machine with forgetting factor (FFOS-ELM) that effectively averts the influence of early data on model accuracy induced by the time variability of short-term traffic flow and adaptively corrects the model parameters. In the second stage, based on the optimal estimation on the particle filter system, optimized real-time forecasting of future traffic volume is accomplished by filtering out the noise in the original traffic volume. Finally, the validity and feasibility of the proposed model are verified by a case study. Microwave data from the main road of a city in China was selected to extract the traffic volume as the model data set, and the accuracy of the proposed model is compared with five traditional offline algorithm models and two online algorithm models. Forecasting results indicate that the two-stage adaptive model produces more accurate and stable predictions and shows potential in forecasting the short-term traffic flow under uncontainable conditions.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Key R&D Program of China (Grant Nos. 2018YFE0120100 and 2018YFB1600900) and the Less Developed Regions of the National Natural Science Foundation of China (Grant No. 71861006).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 24, 2019
Accepted: Mar 2, 2020
Published online: May 31, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 31, 2020
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