Traffic time series analysis by using multiscale time irreversibility and entropy.

Area of Science:

• Complex systems analysis
• Transportation engineering
• Time series analysis

Background:

• Urban traffic systems involve complex mechanisms across multiple scales.
• Traditional methods struggle with the multiscale nature of traffic time series data.
• Investigating multiscale analytical methods for traffic time series is crucial.

Purpose of the Study:

• Introduce a novel method: multiscale time irreversibility, for traffic time series analysis.
• Analyze the complexity of traffic volume time series from Beijing's Ring roads.
• Compare the new method's results with the established multiscale entropy method.

Main Methods:

• Application of multiscale time irreversibility analysis.
• Analysis of traffic volume time series data (August 18, 2012 - October 26, 2012).
• Comparison with multiscale entropy (MSE) method.

Main Results:

• The multiscale time irreversibility method provides insights into traffic time series.
• A higher asymmetry index correlates with a higher level of traffic congestion.
• Results are consistent with those obtained using the multiscale entropy method.

Conclusions:

• Multiscale time irreversibility is a valuable tool for understanding traffic dynamics.
• The asymmetry index can serve as an indicator of traffic congestion levels.
• The proposed method complements existing multiscale analysis techniques for traffic systems.