It is a spatio-temporal model for traffic volume prediction at stations with medium range traffic counts.
It can handle situations with limited data availability thus avoiding the excessive number of parameters required by multivariate time series models.
This model was tested for several case studies in the City of Toronto. The result clearly showed that generated traffic volumes have a magnitude and variation similar to observed traffic counts.
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