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Vector-based pedestrian navigation in cities

Nature Computational Science volume 1, pages 678–685 (2021)Cite this article

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Abstract

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; the resulting trajectories, which we have termed pointiest paths, are 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.

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Fig. 1: Differences between human paths and shortest paths.
Fig. 2: Asymmetric human paths.
Fig. 3: Model design.
Fig. 4: DPF for different OD separations.

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Data availability

Due to privacy constraint policies and a signed data usage agreement, we are not allowed to share the full GPS tracks considered in this work. For this reason, we generated a small sample of 100 trajectories for Boston. We also make available the pre-processed pedestrian street networks for Boston and San Francisco. The sample dataset and street network data can be accessed at Zenodo60. Figures 1c, 2a and 3a used basemap from Open Street Map (https://www.openstreetmap.org) under an Open Database license (https://www.openstreetmap.org/copyright). Figure 3b uses Google Map data (2021) under fair-use guidelines (https://about.google/brand-resource-center/products-and-services/geo-guidelines/#general-guidelines-copyright-fair-use). Source data are provided with this paper.

Code availability

The version of PedNav package used in this study and a guide to reproducing the results is available through GitHub under a GNU GPL-3.0 license (https://github.com/cbongiorno/pednav). The specific version of the package used to generate the results in the current study is available at Zenodo60. A pseudo-code description of the algorithms used for human navigation based on stochastic distance minimization and vector navigation is reported in Supplementary Section 4.

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Acknowledgements

P.S. and C.R. thank the Amsterdam Institute for Advanced Metropolitan Solutions, Enel Foundation, DOVER and all of the members of the MIT Senseable City Laboratory Consortium for supporting this research. This material is based on work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award no. CCF-1231216. C.B. and A.R. acknowledge support from the MISTI/MITOR fund. A.R. acknowledges support from Compagnia di San Paolo. This work was performed using HPC resources from the ‘Mésocentre’ computing center of CentraleSupélec and Ecole Normale Supérieure Paris-Saclay supported by CNRS and Région Île-de-France. Y.Z. acknowledges B. Huang and the Chinese University of Hong Kong for supporting his academic visit at the MIT Senseable City Laboratory.

Author information

Authors and Affiliations

  1. Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Christian Bongiorno, Yulun Zhou, Paolo Santi & Carlo Ratti

  2. Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, France

    Christian Bongiorno

  3. Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong, China

    Yulun Zhou

  4. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Marta Kryven & Joshua Tenenbaum

  5. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    David Theurel

  6. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Alessandro Rizzo

  7. Office of Innovation, New York University Tandon School of Engineering, Six MetroTech Center, New York, NY, USA

    Alessandro Rizzo

  8. Istituto di Informatica e Telematica del CNR, Pisa, Italy

    Paolo Santi

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Contributions

A.R. and P.S. conceived and supervised the research. C.B. and Y.Z. led and performed the exploratory data analysis. C.B. designed the asymmetry testing procedure, the stochastic modeling, the validation method and performed the simulations. Y.Z. processed the data, conducted statistical analyses and drafted the manuscript. M.K. and D.T. carried out exploratory modeling converging on the presented model. M.K. drafted the introduction, content related to cognitive science and neuroscience, as well as cognitive science research and methodology. C.R. contributed to conceptualize and design the research. J.T. provided conceptualization and feedback. All authors contributed to writing and revising the manuscript.

Corresponding author

Correspondence to Paolo Santi.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Nora Newcombe, Steven Weisberg, Daniel Montello and Laura Alessandretti for their contribution to the peer review of this work. Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Tables 1–3 and additional analyses.

Supplementary Data 1

Statistical source data for Supplementary Fig. 4.

Supplementary Data 2

Statistical source data for Supplementary Fig. 5.

Supplementary Data 3

Statistical source data for Supplementary Fig. 6.

Supplementary Data 4

Statistical source data for Supplementary Fig. 7.

Supplementary Data 5

Statistical source data for Supplementary Fig. 8.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Bongiorno, C., Zhou, Y., Kryven, M. et al. Vector-based pedestrian navigation in cities. Nat Comput Sci 1, 678–685 (2021). https://doi.org/10.1038/s43588-021-00130-y

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