"I did not see that coming": A latent variable structural equation model for understanding the effect of road predictability on crashes along horizontal curves. (July 2023)

• Record Type: Journal Article
• Authors: Afghari, Amir Pooyan; Vos, Johan; Farah, Haneen; Papadimitriou, Eleonora
• Is Part Of: Accident analysis and prevention. Volume 187(2023)
• Publication Date: 2023-07
• Subjects: Anticipation -- Predictability -- Crash risk -- Horizontal curve -- Structural equation modelling; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2023.107075
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Driver anticipation and road geometry are linked via road "predictability". Road predictability is defined as a new latent variable in a crash count model. The latent variable is measured by driving behaviour and predicted by road geometry. Higher road predictability is associated with decreased likelihood of crashes. Predictability is influenced by the difference in geometry of consecutive segments. Abstract: Driver anticipation plays a crucial role in crashes along horizontal curves. Anticipation is related to road predictability and can be influenced by roadway geometric design. Therefore, it is essential to understand which geometric design elements can influence anticipation and cause the road to be (un)predictable. This exercise, however, is not straightforward because anticipation is individual-specific whereas road geometric design is location-specific; anticipation is latent and measuring it may not be trivial; anticipation may have several stages from the preceding tangent until the midst of the curve; and not all drivers anticipate in the same way and thus there may well be unobserved heterogeneity in the effect of anticipation on crash risk. Despite methodological advancements in crash risk modelling, there is no econometric model that can adequately explain the above complexities. This study aims to fill this gap by developing an econometric model with a new latent variable, named 'predictability' that is measured by individual-specific driving behaviour indicators and predicted by location-specific road geometric factors. The model is specified with random parameters to account for unobserved heterogeneity and is empirically tested by a unique dataset including detailed geometric design and driver behaviour data obtained for 156 curves in the Netherlands. Results indicate that higher exposure and uphill vertical grade are associated with increased likelihood of vehicle crashes along horizontal curves, whereas adequate superelevation and higher predictability are associated with decreased likelihood of those crashes. Pavement friction influences this likelihood too but it has varied effects. Road predictability is influenced by the differences in angle of horizontal curves, vertical grades, and width of consecutive road segments.