Estimating travel demand in a city is a critical tool for urban planners to understand traffic patterns, predict traffic congestion, and plan ahead for transportation infrastructure maintenance and replacement. For years, researchers have used the classic practice of multiplying the number of trips per day per person for different demographic groups to model activity-based travel demand. But because this method was developed before the current era of ubiquitous sensors -- GPS devices, smartphones, cameras on light poles, and connected vehicles, among them -- researchers have found it difficult to validate their estimates in real-world situations.

Mining data to analyze tracking patterns, Sharon Di, assistant professor of civil engineering and engineering mechanics at Columbia Engineering, has discovered that she can infer the population travel demand level in a region from the trajectories of just a portion of travelers. She took data collected from the world's first and largest connected vehicle testbed in Ann Arbor, led by University of Michigan Transportation Institute (UMTRI), and analyzed 349 vehicles' continuous one-year mobile traces (19,130 travel activities). She found three distinct groups and inferred their demographics based on their travel patterns:

More disturbing so-called "data mining," IMO. To read more, click here.