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The dynamic monitoring of commuting flows is crucial for improving transitsystems in fast-developing cities around the world. However, existingmethodology to infer commuting originations and destinations have to eitherrely on large-scale survey data, which is inherently expensive to implement, oron Call Detail Records but based on ad-hoc heuristic assignment rules based onthe frequency of appearance at given locations. In this paper, we proposed anovel method to accurately infer the point of origin and destinations ofcommuting flows based on individual's spatial-temporal patterns inferred fromCall Detail Records. Our project significantly improves the accuracy upon theheuristic assignment rules popularly adopted in the literature. Starting withthe historical data of geo-temporal travel patterns for a panel of individuals,we create, for each person-location, a vector of probability distributioncapturing the likelihood that the person will appear in that location for agiven the time of day. Stacked in this way, the matrix of historicalgeo-temporal data enables us to apply Eigen-decomposition and use unsupervisedmachine learning techniques to extract commonalities across locations for thedifferent groups of travelers, which ultimately allows us to make inferencesand create labels, such as home and work, on specific locations. Testing themethodology on real-world data with known location labels shows that our methodidentifies home and workplaces with significant accuracy, improving upon themost commonly used methods in the literature by 79% and 34%, respectively. Mostimportantly, our methodology does not bear any significant computation burdenand is easily scalable and easily expanded to other real-world data withhistorical tracking.

Profiling presence patterns and segmenting user locations from cell phone data