New York, NY--November 19, 2018--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.
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).
"With the popularity of sensors everywhere, from our pockets to our cars, we can now trace individuals in terms of where they go, at what time, and what activity they may perform--essentially, where you go tells who you are, and vice versa," says Di, who is also a member of the Data Science Institute.
"What we've learned from our analysis of the Michigan data will help us utilize future data collected from New York City's connected vehicles testbed to understand mobility patterns in the city and help relieve traffic congestion."
Because people tend to visit the same places for daily activities such as work, shopping, and dining, everyday mobile traces tend to be repetitive, but random events create deviations.
Because most existing studies use just a single day or a few days of a smaller subset of people's mobile traces, they do not accurately or fully capture their longer term travel routines.