title: Traffic time series analysis by using multiscale time irreversibility and entropy.
abstract: Traffic systems, especially urban traffic systems, are regulated by different kinds of interacting mechanisms which operate across multiple spatial and temporal scales. Traditional approaches fail to account for the multiple time scales inherent in time series, such as empirical probability distribution function and detrended fluctuation analysis, which have lead to different results. The role of multiscale analytical method in traffic time series is a frontier area of investigation. In this paper, our main purpose is to introduce a new method-multiscale time irreversibility, which is helpful to extract information from traffic time series we studied. In addition, to analyse the complexity of traffic volume time series of Beijing Ring 2, 3, 4 roads between workdays and weekends, which are from August 18, 2012 to October 26, 2012, we also compare the results by this new method and multiscale entropy method we have known well. The results show that the higher asymmetry index we get, the higher traffic congestion level will be, and accord with those which are obtained by multiscale entropy.
authors.affiliation: Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
authors.forename: Xuejiao
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
authors.forename: Pengjian
authors.initials: P
authors.lastname: Shang
authors.affiliation: School of Software Engineering, Beijing Jiaotong University, Beijing 100044, People's Republic of China.
authors.forename: Jintang
authors.initials: J
authors.lastname: Fang