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

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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.

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Abbreviations

Variable:

One feature of a traffic data vector, for example the flow value from a sensor

Attribute:

One time slice of one variable, for example the flow value from a sensor 5 min ago

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Acknowledgments

The work reported in this paper forms part of the FREEFLOW project, which is supported by the UK Engineering and Physical Sciences Research Council, the UK Department for Transport and the UK Technology Strategy Board. The project consortium consists of partners including QinetiQ, Mindsheet, ACIS, Kizoom, Trakm8, City of York Council, Kent County Council, Transport for London, Imperial College London and University of York.

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Authors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK

    Victoria J. Hodge, Jim Austin & Tom Jackson

  2. Centre for Transport Studies, Imperial College London, London, UK

    Rajesh Krishnan & John Polak

Authors
  1. Victoria J. Hodge
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  2. Rajesh Krishnan
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  3. Jim Austin
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  4. John Polak
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  5. Tom Jackson
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Corresponding author

Correspondence to Victoria J. Hodge.

Appendix

Appendix

1.1 WEKA MLP configuration

Settings in italic were changed from the WEKA defaults but not changed between runs, and settings in bold/italic were varied for each run to tune the MLP.

gui

false

autoBuild

true

debug

false

decay

true

hiddenLayers

t (number of hidden layers = numAttribs + numClasses)

learningRate

0.4

momentum

0.3

nominalToBinaryFilter

true

normaliseAttributes

true

normaliseNumericClass

true

reset

true

seed

0

trainingTime

500

validationSetSize

30

validationThreshold

20

1.2 WEKA SVM configuration

Settings in italic were changed from the WEKA defaults but not changed between runs, and settings in bold/italic were varied for each run to tune the SVM.

complexity

50.0

debug

false

fileType

Normalise training data

kernel

RBFKernel -C 250007 -G 0.01

regOptimizer

RegSMOImproved -L 0.0010 -W 1 -P 1.0E−12 -T 0.0010 -V

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Hodge, V.J., Krishnan, R., Austin, J. et al. Short-term prediction of traffic flow using a binary neural network. Neural Comput & Applic 25, 1639–1655 (2014). https://doi.org/10.1007/s00521-014-1646-5

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  • DOI: https://doi.org/10.1007/s00521-014-1646-5

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