# Vector-based Pedestrian Navigation in Cities

arXiv: https://arxiv.org/abs/2103.07104 · HTML: https://mlsanity.com/paper/2103.07104/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [Map Induction: Compositional spatial submap learning for efficient exploration in novel environments](https://mlsanity.com/paper/2110.12301/) (2021)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [Cognitive maps are generative programs](https://mlsanity.com/paper/2504.20628/) (2025)
- [Biologically Inspired Neural Path Finding](https://mlsanity.com/paper/2206.05971/) (2022)
- [Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus](https://mlsanity.com/paper/2606.07426/) (2026)
- [Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes](https://mlsanity.com/paper/2607.15808/) (2026)

## Abstract (arXiv)

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.