Wu, Y., Ye, Y., Zeb, A. et al. (2 more authors) (2023) Adaptive Modeling of Uncertainties for Traffic Forecasting. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050
Abstract
Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for traffic speed or travel time. However, single-point predictions fail to account for prediction uncertainty that is critical for many transportation management scenarios, such as determining the best-or worst-case arrival time. We present, a generic framework to enhance the capability of an arbitrary DNN model for uncertainty modeling. requires little human involvement and does not change the base DNN architecture during deployment. Instead, it automatically learns a standard quantile function during the DNN model training to produce a prediction interval for the single-point prediction. The prediction interval defines a range where the true value of the traffic prediction is likely to fall. Furthermore, develops an adaptive scheme that dynamically adjusts the prediction interval based on the location and prediction window of the test input. We evaluated by applying it to five representative DNN models for traffic forecasting across seven public datasets. We then compared against six uncertainty quantification methods. Compared to the baseline uncertainty modeling techniques, with base DNN architectures delivers consistently better and more robust performance than the existing ones on the reported datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Traffic prediction, uncertainty qualification, quantile model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Oct 2023 17:19 |
Last Modified: | 29 Nov 2023 15:22 |
Status: | Published online |
Publisher: | IEEE |
Identification Number: | 10.1109/TITS.2023.3327100 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204677 |