Extracting Commuters from Automated Road Traffic Counters: A Gaussian Mixture Approach

Assessing traffic patterns is important for many applications such as rush hour traffic management, cross-border commuting statistics, transportation disruption assessment, and crisis management. We present a method for detecting commuting patterns from time-detailed traffic sensor data. Our method uses Gaussian mixture models to identify morning peaks that also exhibit expected variation patterns over weekends and holidays as corresponding to commuting. We apply the method to detect the variation in commuting between countries in the Nordics during the disruptions caused by the COVID-19 pandemic. Results show that the commuting traffic experienced a smaller decrease (42–71%) than the total traffic (87–92%) during the pandemic. For Finland and Sweden, both types of traffic have in 2023 returned to approximately the same level as before the pandemic, while the traffic between Norway and Sweden has only recovered to about 73% of the pre-pandemic level. Our methods can be applied in real-time to provide useful information for applications.