Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 7, Pages 7910-7918Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3074522
Keywords
Near-accident event; pedestrian behavior; Poisson process; logistic regression
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Funding
- JSPS KAKENHI [18K04006]
- Grants-in-Aid for Scientific Research [18K04006] Funding Source: KAKEN
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This paper proposes a statistical framework to assess the risk of passing a non-signalized intersection for vehicles, establishing intensity and probability models for near-accident events and pedestrian intentions. The models are evaluated using residual analysis and used to create a predictive risk metric for pedestrian intentions.
This paper proposes a statistical framework to assess the risk of passing a non-signalized intersection for vehicles. First, an intensity model of the near-accident event is established by regarding the near-accident event as a non-homogeneous Poisson process. The non-homogeneous Poisson process is defined on the sigma-algebra of the 2-dimension plane of vehicle velocity and distance to the intersection instead of in the time axis. On the other hand, the pedestrian intention is defined as a binary variable with I as passing through the crosswalk and 0 as stopping. Logistic function is applied to model the probability of pedestrian intention. The proposed statistical models are evaluated by the residual analysis-based model checking method. Besides, based on the two models, the pedestrian-aware risk model is established to give a predictive risk metric quantitatively when pedestrian appears.
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