Hodge, Victoria J. orcid.org/0000-0002-2469-0224, Krishnan, Rajesh, Austin, Jim orcid.org/0000-0001-5762-8614 et al. (2 more authors) (2014) Short-term prediction of traffic flow using a binary neural network. Neural computing & applications. 1639–1655. ISSN 0941-0643
Abstract
This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time-series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine if the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be setup for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Natural Computing Applications Forum 2014. This is an author produced version of a paper accepted for publication in Neural Computing and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Associative memory,Binary neural network,k-nearest neighbour,Prediction,Spatiotemporal,Time series |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 04 Dec 2015 09:30 |
Last Modified: | 19 Mar 2025 00:06 |
Published Version: | https://doi.org/10.1007/s00521-014-1646-5 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1007/s00521-014-1646-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89459 |