Traffic conduction analysis model with time series rule mining

• We model the traffic density conduction situation intuitively.
• We use temporal-spatial time related rule mining methods with conduction rules.
• The obtained model chooses route avoiding the road have congestion in future, and aims to avoid the congestion conduction.

The traffic density situation in a traffic network, especially traffic congestion, exhibits characteristics similar to thermodynamic heat conduction, e.g., the traffic congestion in one section can be conducted to other adjacent sections of the traffic network sequentially. Analyzing this conduction facilitates the forecasting of future traffic situation; therefore, a navigation system can reduce traffic congestion and improve transportation mobility. This study describes a methodology for traffic conduction analysis modeling based on extracting important time-related conduction rules using a type of evolutionary algorithm named Genetic Network Programming (GNP). The extracted rules construct a useful model for forecasting future traffic situations and analyzing traffic conduction. The proposed methodology was implemented and experimentally evaluated using a large scale real-time traffic simulator, SOUND/4U.