NDLI: Short-term prediction of traffic flow using a binary neural network

Content Provider	Springer Nature Link
Author	Hodge, Victoria J. Krishnan, Rajesh Austin, Jim Polak, John Jackson, Tom
Copyright Year	2014
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 whether 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 set-up 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.
Starting Page	1639
Ending Page	1655
Page Count	17
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	25
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-06-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Binary neural network Associative memory k-nearest neighbour Time series Spatiotemporal Prediction Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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