Abstract
Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.
Highlights
Highways provide the basis for safe and efficient driving
Random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. erefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes
Previous research studies focused on the contributing factors of road geometry or operating speed characteristics separately, and the sight characteristics determined by the road geometry alignments were rarely studied. is study takes the geometric, speed, and sight characteristics into consideration simultaneously under a single condition and adopts the random-parameter approach to reduce the interactions between various variables to acquire a higher dimension and discover the contributing factors. e traditional negative binomial and Poisson models used in previous research studies become complicated when analyzing crash types
Summary
Many methodological approaches, such as the multivariate Poisson (lognormal) model, zero-inflated negative binomial model, and Poisson lognormal spatial and/or temporal model typically address the crash rate considering the number of crashes occurring over a roadway segment or at an intersection [36,37,38]. In statistics researches on crash frequency, Poisson regression is a generalized linear model analysis used to model crash data. E response variable Y is assumed yielding to Poisson distribution, with the expected value modeled by a linear combination of unknown parameters. Negative binomial (NB) regression is a common generalization of Poisson regression including a gamma noise variable [43]. E negative binomial regression model (NBRM) meets the equation. When the crash data contain excess zero-count values in the model, the wellknown zero-inflated Poisson model is adopted. Pr Y yi (1 − π) yi! , yi 1, 2, 3, . . . , where yi is the nonnegative integer value; λ is the ith expected Poisson count; and π is the probability of extra zeros
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