Chaotic Structure Test and Predictability Analysis on Traffic Time Series in the City of Istanbul

摘要

Empirical studies suggest that traffic flow generally exhibits irregular and complex behavior. Modeling of traffic flow characteristics is difficult and needs new techniques. In this study, we analyzed chaotic structure in traffic time series data collected from an urban arterial in Istanbul over a period of about 1 week. Nonlinear techniques (correlation dimension and metric entropy) are used to identify chaotic structure. After detecting chaotic characteristics, the predictability of time series data was examined. It is found that the traffic flow at the main road of Ikitelli - Mahmutbey location displayed a periodicity close to 24 hrs, and a 100 minute long prediction interval which is indicative of low dimensional chaos as found from the computed metric entropy. Traffic time series data included speed, occupancy rates, and volume at each lane on the main road of Ikitelli - Mahmutbey on the European side.