Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Moomen, Milhan; Rezapour, Mahdi; Raja, Mustaffa N.; Ksaibati, Khaled

doi:10.1016/j.iatssr.2019.09.002

Cited by 11 publications

(7 citation statements)

References 46 publications

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“…The results from Kaiyang Freeway research in China suggested significant spatial correlations in the crash severity data [4]. In the United States, the reported occurrence of log trucks involved in a fatal crash increased by 41% between 2011 and 2015 [5], and road deaths, injuries, and property damage place a huge burden on the economy of most nations [6]. In particular, the gradually increasing truck proportion will inevitably affect freeway traffic volumes and lead to different levels of traffic safety [7].…”

Section: Previous Literaturementioning

confidence: 99%

“…To consider the site correlation between single-vehicle and multiple-vehicle crashes, a bivariate negative binomial conditional autoregressive model (BNB-CAR) was developed by Wang [19]. Moomen used a random parameter negative binomial regression model to evaluate and predict the impact of geometric variables on crash frequency [6], and the parametric and nonparametric negative binomial model (NB) Poisson was used for crash prediction on freeways in some Wyoming mountains, USA [20]. Furthermore, since the dataset contained a large number of zero truck crashes, a zero-inflated negative binomial (ZINB) model was used to predict crash characteristics in the mountains [21].…”

Section: Previous Literaturementioning

confidence: 99%

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Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

2023

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With the growing demand for transportation and cargo between cities, the proportion of heavy vehicles in freeway traffic has been increasing in Iran and worldwide during the past decade. The impact of heavy vehicles on crash severity has long been a concern in the crash analysis literature for the prevalence of crashes in freeway traffic. The purpose of this study is to investigate the contribution of heavy vehicles to freeway crashes and uncover other causal factors. Using the comprehensive crash and traffic data from the Qazvin–Tehran freeway in Iran, from 2013 to 2018, 1350 crashes involving heavy vehicles were extracted regarding the weather conditions, weekday, main cause of the crash, driver gender, and culprit side. Considering crash severity calculation, the applied coefficient weights in this study for a person were considered as 3 for an accident resulting in injury and 5 for a fatal crash. A binary logit model was estimated using the data to determine if there was a significant correlation between recognized factors and the likelihood of the crash. The logit modeling results clearly illustrate important relationships between various risk factors and occupant injury, in which heavy vehicles were recognized as one of the most important factors in this study. Other variables associated with crash severity were weather conditions and driver attention. Results indicate that the number of crashes is simultaneously dependent on the total vehicle volume and average speed of heavy vehicles.

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Section: Previous Literaturementioning

confidence: 99%

Section: Previous Literaturementioning

confidence: 99%

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

2023

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“…Various measures of truck crashes such as frequency, and EPDO, and also truck crashes, in different circumstances, such as truck crashes on downgrade areas, have been considered in the literature, which the next few paragraphs highlight few of them. For instance, the random parameter negative binomial model for modeling downgrade truck‐related crashes was studied in the past study 10 . The study focused on various geometric and traffic characteristics of only downgrade crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, the random parameter negative binomial model for modeling downgrade truck-related crashes was studied in the past study. 10 The study focused on various geometric and traffic characteristics of only downgrade crashes. It was found that various geometric characteristics, such as segment length, and vertical grade are factors contributing to truck crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Contributory factors to truck crashes considering enforcement types, accounting for the enforcement endogeneity

Rezapour¹,

Ksaibati²

2022

Engineering Reports

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The U.S. economy depends on trucking industry for timely delivery of goods and product. However, the performance of the industry has been crippled by the tremendous costs of crashes. Despite the efforts in studying factors leading to truck crashes, a comprehensive study considering enforcement efforts on the safety of the trucking industry is very limited. Thus, this study contributes to the body of knowledge by studying underling factors causing truck crashes, while considering various types of citations. To account for subjectivity of the highway patrol in allocating their resources to specific locations, such as adjustment of their locations to better capture the violators, we considered the interaction terms between citation types and various geometric characteristics. That is especially important as the majority of past studies ignored the endogeneity of highway patrol in allocating the resources to particular locations. Also, to account for both aspects of truck crashes, including severity and frequency, equivalent property damage only were used as the response. The results highlighted, for instance, that interaction terms between commercial motor vehicle citations and presence of horizontal curve, or the interaction between the curve length and speed‐related citations are factors impacting the safety of truck‐related crashes. The results provided evidence that indeed highway patrol allocates their resources to locations with particular geometric characteristics, and ignoring those interaction terms is likely to result in biased point estimates. We discuss the implications of the findings that solely highlighting that the enforcement resources are negatively or positively correlated with crashes could not provide much information, and the inclusion of particular enforcement types with related interaction term could help highway patrol to amend their performance based on their discretion.

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“…Poisson regression model and Negative binomial regression model (NB) are mostly used. The disadvantage of this type of model is that it assumes that the model parameters are fixed and does not take into account the differences of variables under different road conditions, causing that road conditions to not affect the number of conflicts, which is out of line with reality [2,3]. Existing studies have shown that taking into account the differences in the effects of variables and assuming that the variables obey a certain distribution, the heterogeneity of unobserved variables can be captured to a certain extent [4], and the discreteness of traditional traffic conflict models can be overcome.…”

Section: Introductionmentioning

confidence: 99%

Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

Xue¹,

Ge²,

Zeng³

et al. 2022

Computers, Materials &Amp; Continua

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To study riding safety at intersection entrance, video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method. It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection, the vehicle-bicycle soft isolation form of the entrance lane of intersection, the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles, the speed of right-turning motor vehicles, and straight-going non-motor vehicles, and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles. Due to the traditional statistical methods, to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors, the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established. The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects; The width of non-motorized lanes, the form of vehicle-bicycle soft isolation, the traffic volume of right-turning motor vehicles, and the coefficients of straight traffic volume obey a normal distribution. Among them, the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts. The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts. Peak periods and flat periods, the average speed of right-turning motor vehicles, and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

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Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Cited by 11 publications

References 46 publications

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

Contributory factors to truck crashes considering enforcement types, accounting for the enforcement endogeneity

Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

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