Complete street systems integrate a wide range of users in the same space, with unequal risks and responsibilities. This makes driver attention a critical factor in assuring the safety of vulnerable users. The Conditioned Anticipation of People psychological model of driver attention proposes that drivers reflexively reengage their metacognitive processes when they anticipate visually interacting with the human face or form due to the neurological priority that the brain places on human recognition. To test this model, an eye-tracking tabulation was generated from the SHRP2 Naturalistic Driving Study that measured midsegment percent of time on-task and multitasking behavior for 200 sites in Tampa, Florida and Seattle, Washington. This attention data was statistically analyzed for the impacts of a wide range of context variables using single variable ANOVA and various multivariate models such as ordered probit fractional split and ordered probit models. Context features with a strong correlation to vulnerable user presence that support driver's visual recognition of that presence were also strongly correlated with driver attention. Features like corridor width, block length, doorway density, and sense of enclosure had the largest impact. Features that did not have an impact on the potential visual connection with street users, like lane width, right of way width, onstreet parking, functional classification, or Walkscore had no impact on driver attention or weak effect sizes, despite strong correlations with vulnerable user presence. Crash history was evaluated in conjunction with the variables most sensitive to driver attention with mixed results. Many of the features that increase the potential for drivers to see and interact with people also contribute to increases in vehicle to vehicle conflicts. A decrease in crash rate with increasing sidewalk width implies that the CAP effect can have some impact on crashes. Implications for complete streets and community design are discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1774 |
| Date | 01 January 2021 |
| Creators | Tice, Patricia |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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