We address a long standing hypothesis as to what is the fundamental nature of the laws that drive human interactions in a crowd. Here, we take a "big-data" approach to resolve this question, performing the largest meta-study of existing crowd data to date. The resulting analysis reveals a simple, universal power law governing pedestrian behavior. Applications of this law include simulating crowd flows and predicting pedestrian behaviors, which could potentially lead to safer buildings, improved pedestrian tracking, and ultimately new ways to understand what drives human behaviors.
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