  The dynamic monitoring of commuting flows is crucial for improving transit
systems in fast-developing cities around the world. However, existing
methodology to infer commuting originations and destinations have to either
rely on large-scale survey data, which is inherently expensive to implement, or
on Call Detail Records but based on ad-hoc heuristic assignment rules based on
the frequency of appearance at given locations. In this paper, we proposed a
novel method to accurately infer the point of origin and destinations of
commuting flows based on individual's spatial-temporal patterns inferred from
Call Detail Records. Our project significantly improves the accuracy upon the
heuristic assignment rules popularly adopted in the literature. Starting with
the historical data of geo-temporal travel patterns for a panel of individuals,
we create, for each person-location, a vector of probability distribution
capturing the likelihood that the person will appear in that location for a
given the time of day. Stacked in this way, the matrix of historical
geo-temporal data enables us to apply Eigen-decomposition and use unsupervised
machine learning techniques to extract commonalities across locations for the
different groups of travelers, which ultimately allows us to make inferences
and create labels, such as home and work, on specific locations. Testing the
methodology on real-world data with known location labels shows that our method
identifies home and workplaces with significant accuracy, improving upon the
most commonly used methods in the literature by 79% and 34%, respectively. Most
importantly, our methodology does not bear any significant computation burden
and is easily scalable and easily expanded to other real-world data with
historical tracking.
