Modeling directed weighted network based on event coincidence analysis and its application on spatial propagation...

Area of Science:

• Network Science
• Data Analysis
• Communication Systems

Background:

• Quantifying synchronicity of extreme events is crucial for understanding spatial propagation.
• Existing methods lack directional correlation analysis for event sequences.

Purpose of the Study:

• To develop a network-based framework for measuring and analyzing the spatial propagation of extreme traffic events.
• To explore directional correlations and spatial characteristics of event sequences.

Main Methods:

• Event coincidence analysis to measure synchrony of traffic extreme events.
• Construction of a directed weighted network to analyze topology and correlations.
• Comparison of precursor and trigger event coincidence methods.

Main Results:

• The study successfully quantifies synchronicity and spatial propagation (area, influence, aggregation) of extreme traffic events.
• A directed network model effectively captures event sequence correlations.
• Differences in synchrony measurement extent were observed between precursor and trigger event coincidence.

Conclusions:

• The proposed network modeling framework quantifies extreme event propagation characteristics, aiding prediction.
• The framework is particularly effective for events occurring in time aggregation.
• Findings offer insights applicable to extreme climatic events analysis.