Data driven exploration of traffic network system dynamics using high resolution probe data

This paper, for the first time, explores the use of data-driven method to model the traffic dynamics of an interstate highway system with high resolution probe data. The dynamic mode decomposition and spatio-temporal pattern network were used to analyze traffic patterns on a 290 mile interstate highway across Iowa. The results show the data-driven methods can effectively detect the changes in traffic system dynamics during different time of day. Traffic dynamics during morning peak hours, evening peak hours and off-peak were very different on the studied road. In contrast, the trends over multiple months were similar during the same time periods. The study also found that inclement weather had a significant impact on the system dynamics. In future, the proposed methodology can be used to gain insights in the system dynamics of a traffic network. These models will be instrumental in optimal traffic control, traffic sensor placement and other policy decisions affecting the capacity of the network.