nehSgnaiL/LPA
Improving Next Location Prediction with Inferred Activity Semantics in Mobile Phone Data
This project helps urban planners, transportation analysts, or mobility researchers accurately predict where people will go next, based on their past movement patterns. It takes mobile phone location data, infers the activities people were engaged in (like commuting, shopping, or leisure), and uses this to predict their subsequent location. The output is a more precise prediction of future movements, which can inform city planning and service placement.
Use this if you need to understand and predict human movement patterns in a city, and want to improve prediction accuracy by incorporating the 'why' behind people's trips.
Not ideal if your primary interest is not in mobile phone location data, or if you do not have access to the necessary detailed movement histories.
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Language
Python
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Last pushed
Feb 06, 2026
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