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 toimittajat: Wang, Yueyang; Srinivasan, Aravinda Ramakrishnan; Jokinen, Jussi P.P.; Oulasvirta, Antti; Markkula, Gustav
Lehti tai sarja: Transportation Research Part C: Emerging Technologies
ISSN: 0968-090X
eISSN: 1879-2359
Julkaisuvuosi: 2025
Ilmestymispäivä: 11.12.2024
Volyymi: 171
Artikkelinumero: 104963
Kustantaja: Elsevier
Julkaisumaa: Britannia
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.trc.2024.104963
Linkki tutkimusaineistoon: https://github.com/YYWang98/Pedestrian-crossing-decisions.git
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain 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-asiasanat: jalankulkijat; suojatiet; päätöksenteko; vahvistusoppiminen; liikennesuunnittelu; laskennallinen tiede
Vapaat asiasanat: pedestrian behaviour; computational rationality; noisy perception; reinforcement learning
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2025
Alustava JUFO-taso: 3