A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception (2025)


Wang, Y., Srinivasan, A. R., Jokinen, J. P., Oulasvirta, A., & Markkula, G. (2025). Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception. Transportation Research Part C: Emerging Technologies, 171, Article 104963. https://doi.org/10.1016/j.trc.2024.104963


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatWang, Yueyang; Srinivasan, Aravinda Ramakrishnan; Jokinen, Jussi P.P.; Oulasvirta, Antti; Markkula, Gustav

Lehti tai sarjaTransportation Research Part C: Emerging Technologies

ISSN0968-090X

eISSN1879-2359

Julkaisuvuosi2025

Ilmestymispäivä11.12.2024

Volyymi171

Artikkelinumero104963

KustantajaElsevier

JulkaisumaaBritannia

Julkaisun kielienglanti

DOIhttps://doi.org/10.1016/j.trc.2024.104963

Linkki tutkimusaineistoonhttps://github.com/YYWang98/Pedestrian-crossing-decisions.git

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/99515

Rinnakkaistallenteen verkko-osoite (pre-print) https://doi.org/10.48550/arXiv.2402.04370


Tiivistelmä

This paper presents a model of pedestrian crossing decisions based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive constraints. While previous models of pedestrian behaviour have been either ‘black-box’ machine learning models or mechanistic models with explicit assumptions about cognitive factors, we combine both approaches. Specifically, we mechanistically model noisy human visual perception and model reward considering human constraints in crossing, but we use reinforcement learning to learn boundedly optimal behaviour policy. The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle’s speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle. Notably, our findings suggest that behaviours previously framed as ’biases’ in decision-making, such as speed-dependent gap acceptance, might instead be a product of rational adaptation to the constraints of visual perception. Our approach also permits fitting the parameters of cognitive constraints and rewards per individual to better account for individual differences, achieving good quantitative alignment with experimental data. To conclude, by leveraging both RL and mechanistic modelling, our model offers novel insights into pedestrian behaviour and may provide a useful foundation for more accurate and scalable pedestrian models.


YSO-asiasanatjalankulkijatsuojatietpäätöksentekovahvistusoppiminenliikennesuunnittelulaskennallinen tiede

Vapaat asiasanatpedestrian behaviour; computational rationality; noisy perception; reinforcement learning


Liittyvät organisaatiot


OKM-raportointiKyllä

VIRTA-lähetysvuosi2025

Alustava JUFO-taso3


Viimeisin päivitys 2025-25-01 klo 20:05