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The short-term prediction of traffic parameters: a review of parametric and nonparametric approaches | ||
| AUT Journal of Civil Engineering | ||
| دوره 6، شماره 3، آذر 2022، صفحه 369-386 اصل مقاله (993.52 K) | ||
| نوع مقاله: Review Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2023.22199.5821 | ||
| نویسندگان | ||
| Amirhossein Taheri1؛ Reza Moghadasi2؛ Arash Rasaizadi* 3 | ||
| 1Department of motor vehicles, Technical University of Berlin, Berlin, Germany | ||
| 2Department of transportation planning engineering, Tarbiat Modares University, Tehran, Iran | ||
| 3Department of civil and environmental engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Traffic prediction؛ parametric models؛ nonparametric models؛ statistical methods؛ machine learning methods | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 474 تعداد دریافت فایل اصل مقاله: 396 |
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