Abstract
Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections.
Highlights
Signalized intersections are crucial components in road networks, where traffic accidents occur frequently [1]. 15,188 vehicles are involved in fatal crashes at intersections and more than 50 percent of all crashes occurred in intersections according to the statistics on the National Highway Traffic Safety Administration (NHTSA) in 2017 and the Federal Highway Administration (FHWA) in 2018 [2, 3]
Semiparametric models, like Cox’s proportional hazards model, are suitable for modeling duration data with one or more covariates observed and only minimal assumptions about the underlying distribution. is kind of model is more specific than the nonparametric model undoubtedly, but due to its limited flexibility, it cannot deal with heterogeneity among objectives. erefore, for further study, more specific and flexible models, parametric models are developed for analyzing survival data
To analyze the different factors that affect the driver’s stopping behavior, the timing variable T is defined as the stopping time, which has a probability function f(t), survival function S(t), and hazard function h(t). e survival function is defined as the probability that an individual survives longer than a certain time point t and the hazard function of survival time T gives the conditional failure rate, which is defined as the probability of failure during a small time interval. e specific functions and their relationships are as follows: S(t) P(T ≥ t), P(t < T ≤ t + Δt) h(t) lim f(t) h(t)
Summary
Signalized intersections are crucial components in road networks, where traffic accidents occur frequently [1]. 15,188 vehicles are involved in fatal crashes at intersections and more than 50 percent of all crashes occurred in intersections according to the statistics on the National Highway Traffic Safety Administration (NHTSA) in 2017 and the Federal Highway Administration (FHWA) in 2018 [2, 3]. Methods like random parameters model and latent class model have been combined to develop a mixed logit model and probit model to reflect these differences in driver decision-making behavior [42,43,44], but these studies mainly focus on unobserved heterogeneity, few studies explore group heterogeneity in stopping behavior at intersections. Considering the group heterogeneity among driver group, the shared frailty survival model is developed to predict stopping time approaching intersections. The results of the model can be used to make effective traffic management and operational strategies, which will reduce the accidents caused by improper stopping behavior at intersections
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
