Real-time prediction of crash risk on freeways under fog conditions

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۴٫۲۷۶ در سال ۲۰۲۰

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۲۶ در سال ۲۰۲۱

شاخص SJR

۰٫۹۰۱ در سال ۲۰۲۰

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Q1 در سال ۲۰۲۰

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Real-time prediction of crash risk on freeways under fog conditions

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The  study aimed to develop a real-time crash risk  prediction model on  freeways under fog conditions. The  data used include traffic surveillance data, fog-related crashes data, road geometry data, and visibility data of  Interstate-5 (I-5),  Interstate-10  (I-10), Interstate-15 (I-15) and Interstate-405 (I-405) in  California, United States. The  random forests method was applied for  variable selection to identify and rank the most important variables. And then, the Bayesian logistic regression model was employed to develop the real-time crash risk  prediction model. The  model estimation results show that the explanatory variables contributing to crash risk  are  different in  different time slices before crashes. There are common features: (a) visibility is negatively-correlated with the real-time crash risk  under fog condition, and (b)  average and standard deviation of vehicle count at upstream station are  positively-correlated with crash risk.  The  model of time slice 3 (interval between 10  to

15 min prior to a crash time) performs best with the lowest false alarm rate and the highest overall accuracy and the largest area under the receiver operating characteristic (ROC) curve. And  it can  identify over 72% of fog-related crashes with the pre-specified threshold of 0.2.

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  1. Introduction

Adverse weather conditions are  known as an unsafe driving environment and can  dramatically increase crash rates. The visibility reduction due to  fog is one  of the major concerns of safety management. Although the percentage of fog related crashes is  small, there  are   still   about  ۳۰۰–۴۰۰ fog  related  fatal crashes  happening  every year in  the  United States (Hamilton et al.,  ۲۰۱۴). Crashes are  often more severe in  foggy  weather, involving a  large number of  vehicles (Wang et al., 2017).

About the cause of fog-related crashes, Chengcheng et al. (2013) found that crashes under low  visibility are  highly related to the speed difference between the upstream and downstream; Peng  et al. (2017)indicated that crash risk  would increase significantly as  the visibility and mean of headway decrease, while it would increase significantly as  the mean speed and volume increase; Wu  et al. (2018)suggested that the ‘‘Crash Risk Increase Indicator” worked well  in evaluating the increase of crash risk  under fog condition, and the crash risk  was  prone to  increase at ramp vicinities in fog conditions.

For  aspect of  active traffic safety management, some researchers proposed real-time crash risk  prediction models to unveil the crash precursors based on the advanced traffic surveillance system. Madanat and Liu (1995)developed a prototype system for real-time incident likelihood prediction based on the two-binary logit.  Oh et al. (2005) used the Bayesian classifier to  identify traffic flow  that may lead to  traffic crashes. Lee et al. (2002, 2003) investigated the potential for  crashes, and refined the log-linear model. Abdel-Aty and Pande (2004) employed the matched case–control logistic regression modeling technique to predict freeway crashes based on loop  detector data. And machine learning algorithms for crash risk prediction are  also  introduced by Abdel-Aty and Pande. Different real-time  crash risk  prediction models are  developed using the prob- abilistic  neural  network  (Abdel-Aty et  al.,   ۲۰۰۴),  multi-layer  perceptron   neural  network  (Pande  and  Abdel-Aty,

2006a,2006b), normalized radial basis function neural network (Pande and Abdel-Aty, 2006a,2006b), and support vector machine (Xu et al., 2017; Yu and Abdel-Aty, 2013), and so on. There is a new trend that combines multiple methods to pre- dict  crash risk.  For example, random forests method, classification and regression trees (CART) (Hossain and Muromachi,

2011) or random effect logit  model (Xu et al., 2016) was  applied to  identify important factors, and then Bayesian network

(Sun  and Sun,  ۲۰۱۵; Hossain and Muromachi, 2012) was  used to  develop the real-time crash risk  prediction model.

However, there were few studies that investigated the relationship between real-time traffic parameters and crash occur- rences under fog conditions. Abdel-Aty et al. (2012) developed crash risk  prediction models for low  visibility, Chengcheng et al. (2013) developed separate crash risk  prediction models for different weather conditions, and Wu  et al. (2018) propose a new algorithm to evaluate the rear-end collision risk under fog conditions considering reduced visibility. However, in those models, low  visibility is a categorical variable rather than a continuous variable.

The  primary objective of this study is  to  identify significant variables leading to  fog-related crashes on  freeways and develop a crash likelihood prediction model under fog conditions. Fig. 1 shows the flow chart of real-time prediction of crash risk  under fog conditions. Random forests method was  applied to identify and rank the most important variables via Mean Decrease Accuracy (MDA). And then the real-time crash risk  prediction model on freeways under fog conditions was  devel- oped using the Bayesian logistic regression model. Historical crash data, traffic flow  data, and weather data are  used to per- form  variable  selection, model  building, and  model  estimation.  Real-time  data  is   detected  by   loop   detectors  and climatological stations on the freeway and it is input into the established model to obtain real-time traffic accident risk  pre- diction. The research results will promote a better understanding of the relationship between traffic flow  characteristics and crash risk  under fog conditions. It will  help transportation managers to develop better crash prevention measures under fog conditions.

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