Abstract
The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns.
Highlights
Traffic congestion is increasingly becoming a serious problem for densely populated cities throughout the world
Because of the shortage of studies that consider the transition characteristic between traffic patterns in the evolution process on urban traffic network, the main objective of this study is to investigate the temporal characteristics and distinct regularity in the traffic evolution process from the viewpoint of the whole network
Define the internal transition times of traffic state pattern Pi as TIPi = ∑M j=i1 ωPji, where ωPji is the weight of each incoming edge of Pi, and Mi is the in-degree of Pi
Summary
Traffic congestion is increasingly becoming a serious problem for densely populated cities throughout the world. Recent studies that use differing analytical approaches have found useful characteristics and patterns in dynamical traffic evolution on urban traffic network. In these studies, the time series is widely introduced to represent the traffic state in a region by a multidimensional traffic flow vector F(t). Analysis of real-world traffic data shows that the method can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously Another method [7], under the assumption that various time series representing daily cycles of traffic may be nonlinear, uses smooth-transition regression (STR) models to characterize distinct regimes for free flow, congestion, and asymmetric behavior in the transition phases from free flow to congestion and vice versa. We conclude with an elaboration on the implications of our research and suggestions for a future agenda
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