Exploring truck driver-injury severity at intersections considering heterogeneity in latent classes: A case study of North Carolina

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Exploring truck driver-injury severity at intersections considering heterogeneity in latent classes: A case study of North Carolina

فهرست مطالب مقاله:


The fatal rate of truck-involved crashes is increasing and crashes become more severe than       ۲۷ passenger vehicles in  recent years. Much research has been dedicated to exploring the       ۲۸ truck crash factors while scarce research focused on  the intersection scenarios. This  study       ۲۹ investigates the factors that affect the severity level of truck-involved crashes at cross- and       ۳۰

T-intersections. Due  to the unobserved heterogeneity inherent in  crash data, latent class       ۳۱



ters. Considering the ordinal feature of the severities, general ordered logit models are  sub-       ۳۳ sequently developed to further explore the specific factors within each cluster. This  study       ۳۴ uses the North Carolina’s truck-involved crash at intersection data during 2005 to 2017       ۳۵


Truck-involved crashes


Intersection, severity analysis


Latent class analysis


Ordered logit

from the Highway Safety Information System (HSIS). The  estimated parameters and asso-




ciated marginal effects are  combined to interpret the impact of  the significant variables



within  specific clusters. Many factors are   found to contribute to the severities, and T-



intersection is  found to be  safer than cross-intersection. For  driving behaviors, followed



too   closely, disregarded signs, disregarded signals, failed to yield, and exceeded speed



are   found to be  top five  factors that increase the crash severity at intersections. These



results indicate that distraction and speed limits violation always result in  severe injury



for  humans involved in  the truck crashes at the intersections. The  results of this research



provide more reliable analysis for the impact factors of truck-involved crashes at intersec-



tions to engineering practitioners and researchers.



  ۲۰۲۰ Tongji University and Tongji University Press. Publishing Services by  Elsevier B.V.



This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/










بخشی از متن مقاله:
  1. Introduction

The  truck-involved crashes have suffered more severe injury compared to  passenger vehicles crashes in  recent years.

53         According to   U.S.  Department of  Transportation  (USDOT)  statistics,  from 2016  to   ۲۰۱۷, the  number  of  large truck-

54         involved fatal crashes increased 9.6 % from 4251 to 4657, while passenger vehicle in fatal crashes has  decreased 1.4%. Mean-

55         while, the fatal crash rate per  ۱۰۰  million vehicle miles involving large trucks reached 1.42  and increased 5.2% from 2016 to

56         ۲۰۱۷, and it was  ۱٫۳۸  times compared to the passenger vehicle (USDOT, 2019). In this case,  a large number of research stud-

ies have been dedicated to investigations into the factors that impact the truck-involved crash severity. In considering that

58         different crash scenarios were a result of different influence factors, truck-involved crashes were specifically drawn into the

59         circumstances of human characteristics (Bernard and Mondy, 2016; Osman et al., 2018), roadway attributes (Ahmed et al.,

60         ۲۰۱۸; Wu et al., 2019a), location (Khorashadi et al., 2005; Uddin and Huynh, 2017), crash characteristics (Azimi  et al., 2020),

61          vehicle characteristics (Uddin and Huynh, 2018), time (Anderson and Dong, 2017; Behnood and Mannering, 2019) and envi-

62         ronment (Uddin and Huynh, 2017). Intersections have more complex traffic circumstances and may cause more severe and

63         frequent crash injury compared to roadways (FHWA, 2004; Zhu and Srinivasan, 2011). Some  studies mentioned intersection/

64         non-intersection (Wu  et al., 2019b; Zhu and Srinivasan, 2011) or signal/non-signal control (Anderson and Dong, 2017; Chen

65         and Chen,  ۲۰۱۱) as location or control type variables. However, scarce research on  truck-involved crashes that specifically

66         considers the cross- and T-intersection scenarios has  been conducted. Hence, it is important to explore the factors that con-

67         tribute to  the truck-involved severity at cross- and T-intersections.

68               Even though many studies investigated the truck-involved crashes under specific conditions, there still  remain many un-

69         observed factors that impact the crash severity and result in heterogeneity within the dataset. Research neglected the data’s

70         heterogeneity might generate wrong parameter estimations and conclusions (Song  et al., 2020). Recently, many clustering

71        methods,  such as  k-means method (Mohamed et al., 2013), support vector method (Chen et al., 2016; Mokhtarimousavi,

72         ۲۰۱۹) and latent  class   analysis  (Li  and Fan,  ۲۰۱۹; Liu  and David,   ۲۰۲۰; Mohamed et  al.,  ۲۰۱۳; Sivasankaran and

73         Balasubramanian, 2020), were used to  minimize the heterogeneity within the crash severity dataset. Mohamed et al.

74         (۲۰۱۳) combined multinomial logit  and ordered probit model with k-means and latent class  analysis method, and their

75         results confirmed that clustering the crash severity dataset into homogeneous clusters helps better identify factors that

76         would otherwise have been hidden without data segmentation. Since  latent class  analysis (LCA) is a model-based method

77         which could guarantee the homogeneity within the cluster based on statistical criterions, many traffic crash severity studies

78         recently implemented LCA for data segmentation (Fountas et al., 2018; Liu and David,  ۲۰۲۰). Hence, this paper uses latent

79        class  analysis to  separate the dataset into groups which have the largest homogeneity within the same group.

80               For truck-involved crash severities, which are  typically discrete in nature, a variety of ordered or unordered logit/probit

81         methods were implemented to conduct severity impact factors analysis, such as binary/multinomial logit  model (Khorashadi

82        et al., 2005), Bayesian binary/multinomial  logit  model (Dong  et al., 2017), mixed logit  model (Anderson and Dong,  ۲۰۱۷;

۸۳        Chen  and Chen,  ۲۰۱۱; Hou  et al.,  ۲۰۱۹), ordered logit   models (Osman et al.,  ۲۰۱۶), ordered probit model (Uddin and

84         Huynh, 2018), partial proportional odds model (Li and Fan,  ۲۰۱۹) and random parameter ordered logit   model (Azimi

85        et al., 2020).

86               For unordered model, Dong  et al. (2017) used Bayesian multinomial logit  and negative binomial model to investigate fac-

87         tors that affect large truck–involved crash frequency and severity based on  ۲۰۰۶ to  ۲۰۱۰ Tennessee data. Seat  belt usage,

88         light condition, and terrain type were found to  have significant effects only  on  the crash severity. In order to  explore the

89         heterogeneity within the data, Anderson and Dong  (۲۰۱۷) applied a mixed logit  model to estimate heavy-vehicle crash fac-

90         tors based on  ۲۰۰۴ to 2014 Minnesota data from HSIS database. Results showed that time-of-week needs to be considered

91         separately for safety analyses. Uddin and Huynh (2017) employed a mixed logit  model to study the impacts of different light-

92        ing conditions on truck-involved crash severity in Ohio’s rural and urban areas during 2009 to 2013 from HSIS. The hetero-

93         geneous results indicated the importance of dividing data into different scenarios for more specific and accurate results.

94               The ordinal nature of crash severity (which usually increases from non-injury to fatal) violates the independence assump-

95         tion of the response variable for unordered model (Derr,  ۲۰۱۳). Hence, many ordinary models were applied for better inves-

96         tigating the impact of the severity level.  Chen  et al. (2015) developed a hierarchical Bayesian random intercept model to

97         analyze the factors affecting rural truck-involved crash severity in  New  Mexico from 2010 to  ۲۰۱۱٫ Results indicated the

98         existence of cross-level interaction effects between severity levels. Hassan et al. (2015) developed ordered probit and struc-

99         tural equation models to  investigate truck crash severity’ factors and to  study the impact of truck road based on  the Abu

100         Dhabi data between 2007 and 2013. Results indicated that the likelihood of truck crashes involving fatalities was  ۳۵% higher

101        on truck roads than that on mixed-vehicle roads. Azimi  et al. (2020) constructed a random parameter ordered logit  model to

102         detect potential sources of heterogeneity within large truck rollover crashes based on  Florida’s 2007 to  ۲۰۱۶ data. Results

103          showed significant variation within observations and the heterogeneous impacts on the severities. Osman et al. (2016) com-

104         pared ordered models with unordered models to analyze truck crash severity in work zones of Minnesota from HSIS. Results

105         showed that ordered logit  has  better model fitness than unordered models. By considering the ordinal feature of the crash

106         severities in the ordered model and investigating the heterogeneity characteristics of the crash data, this paper combines the

107        LCA with ordered logit  model to  further explore the factors that affect the truck-involved crash severity.



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