Latest revision as of 12:12, 16 February 2021

Abstract

Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combination of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt–Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work. Keywords: Short-term traffic forecasting, Non-parametric regression, Gaussian maximum likelihood, Double seasonal Holt–Winters exponential smoothing, Entropy weighting method, Shapley value weighting method

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1016/j.jtte.2018.01.002 under the license http://creativecommons.org/licenses/by-nc-nd/4.0

• http://hdl.handle.net/11585/649184

• https://doi.org/10.1016/j.jtte.2018.01.002 under the license cc-by-nc-nd

• http://www.sciencedirect.com/science/article/pii/S209575641730301X,

https://doaj.org/toc/2095-7564

• https://api.elsevier.com/content/article/PII:S209575641730301X?httpAccept=text/xml,

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http://dx.doi.org/10.1016/j.jtte.2018.01.002 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S209575641730301X,

https://academic.microsoft.com/#/detail/2830219012