Vector-based Pedestrian Navigation in Cities

Christian Bongiorno (), Yulun Zhou, Marta Kryven, David Theurel, Alessandro Rizzo, Paolo Santi, Joshua Tenenbaum and Carlo Ratti
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Christian Bongiorno: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay, MIT - Massachusetts Institute of Technology
Yulun Zhou: CUHK - City University of Hong Kong [Hong Kong]
Marta Kryven: MIT - Massachusetts Institute of Technology
David Theurel: MIT - Massachusetts Institute of Technology
Alessandro Rizzo: Polito - Politecnico di Torino = Polytechnic of Turin
Paolo Santi: MIT - Massachusetts Institute of Technology, CNR PISA - Consiglio Nazionale delle Ricerche [Pisa]
Joshua Tenenbaum: MIT - Massachusetts Institute of Technology
Carlo Ratti: MIT - Massachusetts Institute of Technology

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Abstract: How do pedestrians choose their paths within city street networks? Human path planning has been extensively studied at the aggregate level of mobility flows, and at the individual level with strictly designed behavioural experiments. However, a comprehensive, individual-level model of how humans select pedestrian paths in real urban environments is still lacking. Here, we analyze human path planning behaviour in a large dataset of individual pedestrians, whose GPS traces were continuously recorded as they pursued their daily goals. Through statistical analysis we reveal two robust empirical discoveries, namely that (1) people increasingly deviate from the shortest path as the distance between origin and destination increases, and (2) individual choices exhibit direction-dependent asymmetries when origin and destination are swapped. In order to address the above findings, which cannot be explained by existing models, we develop a vector-based navigation framework motivated by the neural evidence of direction-encoding cells in hippocampal brain networks, and by behavioural evidence of vector navigation in animals. Modelling pedestrian path preferences by vector-based navigation increases the model's predictive power by 35%, compared to a model based on minimizing distance with stochastic effects. We show that these empirical findings and modelling results generalise across two major US cities with drastically different street networks, suggesting that vector-based navigation is a universal property of human path planning, independent of specific city environments. Our results offer a simple, unified explanation of numerous findings about human navigation, and posit a computational mechanism that may underlie the human capacity to efficiently navigate in environments at various scales.

Date: 2021-10-18
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Published in Nature Computational Science, 2021, 1 (10), pp.678-685. ⟨10.1038/s43588-021-00130-y⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03168957

DOI: 10.1038/s43588-021-00130-y

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