  How do pedestrians choose their paths within city street networks?
Researchers have tried to shed light on this matter through strictly controlled
experiments, but an ultimate answer based on real-world mobility data is still
lacking. Here, we analyze salient features of human path planning through a
statistical analysis of a massive dataset of GPS traces, which reveals that (1)
people increasingly deviate from the shortest path when the distance between
origin and destination increases, and (2) chosen paths are statistically
different when origin and destination are swapped. We posit that direction to
goal is a main driver of path planning and develop a vector-based navigation
model that is a statistically better predictor of human paths than a model
based on minimizing distance with stochastic effects. Our findings generalize
across two major US cities with different street networks, hinting to the fact
that vector-based navigation might be a universal property of human path
planning.

Neurons and Cognition;Applications