The short-term prediction of traffic parameters: a review of parametric and nonparametric approaches

Document Type : Review Article

Authors

1 Department of motor vehicles, Technical University of Berlin, Berlin, Germany

2 Department of transportation planning engineering, Tarbiat Modares University, Tehran, Iran

3 Department of civil and environmental engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Over the past few decades, there has been a lot of interest in short-term traffic prediction which is a crucial part of transportation systems. One of the major considerations that travelers evaluate when making travel plans is information regarding the near future. Transportation planners have investigated a number of methods to produce more reliable travel time predictions in the future. However, there have not been enough in-depth and comprehensive surveys in this area yet. In this paper, a comprehensive review of the literature has been conducted, and various traffic parameter prediction algorithms have been investigated. The methods can be divided into two main groups: parametric, and nonparametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions. In contrast to nonparametric procedures, parametric approaches have a distribution with a defined number of parameters. The parametric approaches are related to statistical methods like time-series, while the nonparametric approaches are related to machine learning methods like neural networks. Predicting flow, volume, speed, density, and occupancy are the main emphasis of the majority of the literature. A detailed methodology is first presented for each of the techniques mentioned in this article, and then the outcomes of numerous articles that have used the technique are discussed.

Keywords

Main Subjects

References

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AUT Journal of Civil Engineering
Volume 6, Issue 3
September 2022
Pages 369-386

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