Classification and prediction of traffic flow based on real data using neural networks

Teresa Pamuła

doi:10.2478/v10174-012-0032-2

Authors

Teresa Pamuła Faculty of Transport, Silesian University of Technology, Krasinskiego 8, 40-019 Katowice, Poland, Author

DOI:

https://doi.org/10.2478/v10174-012-0032-2

Keywords:

traffic flow, classification of traffic flow, prediction of traffic flow, traffic management systems

Abstract

This paper presents a method of classification of time series of traffic flow, on the section of the main road leading into the city of Gliwice. Video detectors recorded traffic volume data was used, covering the period of one year in 5-minute intervals - from June 2011 to May 2012. In order to classify the data a statistical analysis was performed, which resulted in the proposition of splitting the daily time series into four classes. The series were smoothed to obtain hourly flow rates. The classification was performed using neural networks with different structures and using a variable number of input data. The purpose of classification is the prediction of traffic flow rates in the afternoon basing on the morning traffic and the assessment of daily traffic volumes for a particular day of the week. The results can be utilized by intelligent urban traffic management systems.

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