With new cities increasingly expanding vertically, there is a pressing need to shed light on human vertical mobility, which can readily be achieved with existing sensor technology. To date, the methodology to track and identify vertical movement from large-scale unstructured data sets is lacking. Here, we design and develop such a framework to accurately and systematically identify the sparse human vertical displacement activity that is typically buried into the predominantly horizontal mobility. Our framework uses sensor data from barometer, accelerometer and Wi-Fi scanner coupled with an extraction step involving a combination of feature engineering and data segmentation. This methodology is subsequently integrated into a machine-learning-based classifier to automatically distinguish vertical displacement activity from its horizontal counterpart. We confirm the high accuracy of this approach by a thorough validation and testing showing a 98% overall accuracy and a 92% F1-score in classifying vertical displacement activity.We illustrate the potential of the developed framework by applying it to an unstructured large-scale data set associated with over 16,000 participants going about their daily activity in the city-state of Singapore. This gives us access to all the vertical movements of this large population, and we investigate the statistical distribution of vertical activity, both in terms of number of events and size of vertical jumps, and their temporal heterogeneity across the day. The approach developed here could be used in massive human experiments to uncover the hidden patterns of human vertical mobility. This new knowledge would have significant ramifications for the architectural design of vertical cities.
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