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Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets
Journal of Safety Research 71 (2019) 163–171
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Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets Amirarsalan Mehrara Molan a,⇑, Milhan Moomen b, Khaled Ksaibati b a
Department of Civil and Environmental Engineering, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407 Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E. University Avenue, Dept. 3295, Laramie, WY 82071, United States b
a r t i c l e
i n f o
Article history: Received 18 April 2019 Received in revised form 23 August 2019 Accepted 1 October 2019 Available online 15 November 2019 Keywords: Median traffic barrier Traffic barrier dimensions Crash severity Random parameters ordered logit Wyoming
a b s t r a c t Introduction: Despite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads. Method: Geometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity. Results: Crashes involving cable barriers with a height between 3000 and 4200 were less severe than other traffic barrier types, while concrete barriers with a height shorter than 3200 were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers. In terms of variables impacting crash frequency, Wbeam and box beam barriers located on frontslopes had higher crash frequency. Practical applications: The results show that using flare barriers should reduce the number of crashes compared to parallel barriers. Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.
1. Introduction Traffic barrier crashes include a considerable portion of crashes in the United States. Based on the American Association of State Highway and Transportation Officials (AASHTO) Roadside Design Guide (AASHTO RDG, 2011), traffic barriers have been involved in 8% of fatal fixed-object crashes in the United States. In Wyoming, between 2008 and 2017, over 7600 traffic barrier crashes occurred with a total estimated crash cost of about $900 million based on the Wyoming Department of Transportation (WYDOT) crash cost estimation worksheet (Wyoming Department of Transportation. (2019) (2019), 2019). Based on statistics, traffic barrier crashes had the highest crash frequency among all fixed-object crashes ⇑ Corresponding author. E-mail addresses: [email protected] (A. Mehrara Molan), mmoomen@uwyo. edu (M. Moomen), [email protected] (K. Ksaibati). https://doi.org/10.1016/j.jsr.2019.10.001 0022-4375/Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.
with a percentage of 37% of whole fixed-object crashes reported in Wyoming (Mehrara Molan, Rezapour & Ksaibati, 2019a). Also, a constant ratio of 6.5% of all crashes was involved with traffic barriers annually between 2008 and 2017 in Wyoming. Meanwhile, the ratio of high-severity (fatal and incapacitating injury) barrier crashes had an increase from about 4.5% to near 6% in the same period (Ksaibati & Mehrara Molan, 2018). This fact shows that traffic barriers pose a higher risk in crashes compared to past years in Wyoming. As the first step to decrease the risk of traffic barrier crashes, it is essential to identify traffic barriers that are not operating efficiently in crashes as they may even be a more serious threat than the hazards and objects they are supposed to protect road users from. Reviewing past studies indicated that there are still big gaps and reservations regarding evaluating the performance of traffic barriers in crashes. For example, despite the numerous studies conducted using simulation tools or statistical methods, the effect of
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variables like traffic barrier height has not been investigated considering a comprehensive statewide actual crash dataset yet. This study aimed at filling this gap by evaluating the effect of the variables related to traffic barriers’ geometric dimensions (height, post-spacing, sideslope, lateral offset, etc.) on traffic barrier crashes. Finally, a statistical model was presented for the severity of crashes involving median traffic barriers on interstate roads in Wyoming. This study targeted the interstate roads because most traffic barrier crashes have been involved with this road functional class. Based on the Critical Analysis Reporting Environment (CARE) package, 45% of traffic barriers are located on interstate roads in Wyoming, while approximately accounting for 70% of traffic barrier crashes.
2. Literature review Simulation models have been in widespread use in various aspects of past traffic barrier assessments (Albuquerque et al., 2015; Atahan, 2016; Hampton & Gabler, 2013; Julin, Pajouh, Stolle, & Reid, 2017; Schmidt et al., 2015; Teng et al., 2016; Tan, Tan, & Wong, 2008). Albuquerque et al. (2015) investigated whether designers can design a shorter length for traffic barriers than the recommended length by the RDG (AASHTO, 2011) when the sideslope is flat. Research by Julin et al. (2017) examined the maximum traffic barrier height for the Midwest guardrail systems (MGS). They concluded that a 36-inch height could be considered as the maximum height that does not create a threat to vehicles in terms of underride crashes. Another focus of past simulation efforts (Atahan, 2016; Hampton & Gabler, 2013; Tan et al., 2008; Teng et al., 2016) was on evaluating the reaction of traffic barriers when vehicles hit them during crashes. Using simulation modeling, a new V-profile barrier was proposed and tested by Tan et al. (2008). Based on the results, the proposed design performed better than the traditional W-beam barriers in absorbing the energy in traffic barrier crashes. The research by Hampton and Gabler (2013) found that removing a post could increase the maximum deflection up to 25% in W-beam barriers. A few studies included a field survey for their safety analysis. Wiebelhaus et al. (2013) conducted a field survey on 68 W-beam barrier systems in Kansas, U.S., to recommend cost-effective improvements using Roadside Safety Analysis Program (RSAP). Mehrara Molan and Ksaibati (2018) created a new model called ‘‘barrier condition index (BCI)” to evaluate the existing condition of six traffic barrier systems compared with recommendations provided in RDG (AASHTO, 2011). Conducting actual crash analysis on historical crashes was another method used in past traffic barrier studies. From a general point of view, there are two study categories for previous actual crash data evaluations: (1) studies that conducted a before-after study in a specific location after installing new traffic barrier systems, and (2) studies that evaluated traffic barriers’ performance using statistical analysis on historical crashes. Villwock, Blond, and Tarko (2009) evaluated the effect of installing new median cable barriers in a before-after study. The study found that new cable barriers caused more crashes on wide medians; however, the crashes were less severe than before. In another before-after study (Chimba, Emaasit, Allen, Hurst, & Nelson, 2013), installing new cable barriers resulted in approximately 82%, and 76% fewer fatal and incapacitating injury crashes, respectively. According to Li et al. (2017), the severity of traffic barrier crashes could be reduced by 50% in highway departures with traffic barrier systems in comparison to the similar locations without any traffic barrier. Cafiso, Agostino, and Persaud (2017) estimated a crash modification factor (CMF) of 0.78 for upgrading old traffic barriers based on new standards in Europe. Russo and Savolainen (2018)
conducted a safety analysis on crashes involving median traffic barriers from 2009 through 2013 in Michigan. Among the median barriers studied by Russo and Savolainen (2018), cable barriers were least likely to cause a high-severity crash. A possible reason for this finding was that cable median barriers resulted in fewer vehicle re-direction back to the highway. This means that the possibility of having a secondary crash could be less in cable median barriers in comparison to the other barrier types. Research by Zou, Tarko, Chen, and Romero (2014) investigated the effective parameters on the severity of over 2000 traffic barrier crashes recorded between 2008 and 2012 in Indiana. Crashes involving near-side median cable barriers were found to be 57% less severe than crashes involving a guardrail face. Geometric characteristics of the traffic barrier systems play a pivotal role in promoting safety. With respect to barrier heights for instance, low barrier heights may increase the risk of override and rollover crashes, whereas excessive heights may increase the likelihood of small vehicles underriding or penetrating the barrier. Thus, the barrier height in relation to a vehicle’s bumper height at impact should be in a range that is neither too high nor too low to cause rollover or underriding crashes (AASHTO RDG, 2011). It should be noted that median barriers may be installed to prevent cross median crashes by containing or redirecting vehicles. For this reason, barriers are commonly grouped into flexible, semi-rigid, and rigid systems. The implication is that the choice of a barrier type in terms of materials and design is influenced by its role. For example, the RDG indicates that flexible or semi-rigid barriers can be installed in wide medians with relatively flat cross-slopes given that the dynamic deflection of the barrier is less than half the median width. For roadways with a narrow median width and high traffic volumes where deflections are not desired, rigid systems are usually installed. Post spacing is also known to impact vehicular deflections after a crash has occurred. The post spacing determines whether some barriers are rigid or flexible. Closely spaced posts increase barrier stiffness and decreases lateral deflection to some extent, while posts that are wider apart have the opposite effect (RDG, 2011). As mentioned in the previous paragraphs, past studies provided valuable findings by evaluating the effect of several variables on traffic barrier crashes utilizing simulation tools, field surveys, and crash data analysis with an actual crash dataset. However, as demonstrated in the literature review, previous studies did not explicitly investigate the impact of the geometric variables of traffic barriers on injury severity considering statewide crash analysis with actual data. The effect of the geometry of traffic barriers (most especially barrier height) on crashes have been previously analyzed through simulation and field tests. Most of these tests mainly focused on the likelihood of rollover, penetration, lateral deflection, and redirection of vehicles after a crash with the barrier but did not directly relate the geometric factors to crash injury severity. Also, it should be mentioned that simulation and field crash tests are involved with limitations. While there are numerous possible vehicle paths in ROR crashes, simulation and crash field tests could model only a limited number of them (National Cooperative Highway Research Program, 2019). Therefore, actual crash data could consider a larger number of variables in terms of vehicle type, speed, road geometry, weather conditions, driver features, etc. From another point of view, crash tests are complex experiments because they could result in random dynamic performance in duplicating the crash tests (National Cooperative Highway Research Program, 1981). The current study aims to provide new findings and contribute to the body of existing literature using actual crash datasets. The main contribution of this study is the analysis of effect of dimensional variables of median traffic barriers on injury severity considering a statewide crash dataset. Also, based on the literature review, there are just a few traffic barrier
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studies conducted in Rocky Mountain Region (RMR). It is expected that this study will identify new insights regarding variables impacting median traffic barrier crashes because of challenging road geometry, significant truck demand, and adverse weather conditions dominating in the RMR.
3. Data preparation 3.1. Data collection As the first step to include traffic barrier dimensional variables in the dataset, a field survey was conducted from summer 2016 through summer 2018 to measure about 0.6 million linear feet (over 110 miles) of traffic barriers across interstate roadways in Wyoming. Based on the data collected, overall, 204 miles median traffic barriers exist on a total 912 miles of interstate roads in Wyoming (0.22 miles median traffic barriers per mile). The traffic barriers inventoried included 53.1, 27.6, 16.7, and 16.3 miles of box beam, W-beam, concrete, and cable systems, respectively. Fig. 1 shows a picture of each traffic barrier type inventoried for this study. Based on the CARE package, the traffic barriers collected were involved in 1879 crashes. It should be noted that 3151 median barrier crashes have been recorded between 2008 and 2017 in Wyoming. It means that this study included 60% of all median traffic
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barrier crashes. Overall, 7622 traffic barrier crashes were reported in this period; however, 66 work zone related, 295 multi-vehicle, and 437 end-treatment crashes were ignored from the dataset to include only the crashes with a direct impact of traffic barriers. The reason is that the severity experienced in these crashes might not be as a direct result of traffic barriers. Also, 315 crashes were removed due to possible errors by police officers in recording the accurate location of crashes. In fact, these crashes were reported as a traffic barrier crash, while there was no traffic barrier at the location reported. In summary, 6509 crashes were selected of which 3151 were involved with median traffic barriers. As another modification considered in providing the final dataset, the traffic barrier types recorded as unknown were checked by reviewing the photos taken during the field survey. Based on the scope defined in this research, the traffic barrier survey was done statewide to collect geometric dimensions of all traffic barriers apart from cable barriers. Cable barrier systems were not collected statewide since they were mostly new and designed based on recent design standards and policies. Moreover, they were already identified as the median traffic barriers causing the minimum risk regarding crash severity in the previous studies (Russo & Savolainen, 2018; Chimba et al., 2013; Alluri, Gan, Haleem, & Mauthner, 2015; Zou et al., 2014; Mehrara Molan, Rezapour, & Ksaibati (2019c); and Olsen et al., 2013). Therefore, the other traffic barrier types were placed as the priority for future improvements by decision-makers in Wyoming. Note that the
Fig. 1. Different traffic barrier types inventoried during the field survey.
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other types were inventoried completely, apart from a few cases where there was a safety risk for data collectors in measuring geometric dimensions. Traffic barriers were collected considering five points on the barriers as shown in Fig. 2. 3.2. Data description Table 1 provides a summary regarding geometric variables of traffic barriers inventoried during the field survey, and some road characteristics collected in this study. The traffic barrier data were combined with historical crashes recorded in the CARE package. The CARE package has been used by researchers in Wyoming since 2009. It provides the most comprehensive crash dataset in Wyoming considering a set of over 160 different variables related to crash environment (road traffic volume, road geometry, weather condition, etc.) and driver characteristics (age, gender, residency, etc.). For a few variables related to traffic volume and road geometric features, CARE has provided only an estimation. Therefore, traffic volume data and road
geometric features were obtained using WYDOT datasets. Also, in a few road segments with no available geometric data, Wyoming pathweb software (2018), Google Earth, and AutoCAD (2018) were utilized to include geometric data of missing rows in the dataset (Fig. 3). Average annual snowfall and average annual snow days were extracted from the National Oceanic and Atmospheric Administration (NOAA, 2018). Reviewing the weather data, annually, an average of 38 snowy days and 64.15 in. snowfall were recorded in the nearest weather stations to the median traffic barriers. The KABCO severity scale was utilized for categorizing the injury level of median traffic barrier crashes. There are five injury levels for crashes based on KABCO scale (NSC 1970): fatal crashes (type K), incapacitating injury crashes (type A), non-incapacitating injury crashes (type B), possible injury crashes (type C), and property damage only crashes (type O, or PDO). This study combined crashes type K and A, and type B and C together due to the lower number of fatal and injury crashes in comparison to PDO crashes. Therefore, the injury scale considered in this analysis was defined as below:
Fig. 2. Five points considered during traffic barrier inventory.
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A. Mehrara Molan et al. / Journal of Safety Research 71 (2019) 163–171 Table 1 Summary of variables collected conducting a field survey in this study. Road features Traffic Barrier Geometric Features
Length (mile)
Shoulder Width (ft)
Lateral Offset (ft)
System Height (in)
Post-Spacing (ft)
Front-Slopea Ratio (V:H)
Back-Slopea Ratio (V:H)
Road Characteristics
Speed Limit (mph)
AADTb (vehicle/day)
AADTTc (truck/day)
a b c
Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Maximum Minimum Mean Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum
Box Beam Barriers
W-Beam Barriers
Concrete Barriers
Cable Barriers
0.14 0.17 0.93 0.01 6.5 3.6 22.1 0.0 4.0 5.6 23.5 0.0 29.4 2.7 37.2 18.0 6.0 3.6 22.1 1.2 1:6 1:0.6 1:20 1:5 1:2 1:20 72 5.5 75 50 6303 2567 12,903 749 2037 1110 3689 131
0.68 0.53 2.20 0.01 4.8 2.9 13.0 0.0 4.8 5.0 26.0 0.0 29.2 3.1 37.2 16.8 6.4 1.3 12.6 2.5 1:6 1:4 1:20 1:8 1:6 1:20 73 6.4 75 50 6002 1676 12,903 1624 2488 803 3413 284
0.41 0.67 1.94 0.01 10.6 7.4 25.4 0.0 0.6 2.1 12.7 0.0 35.1 5.6 55.2 20.4 1:8 1:5 1:20 1:10 1:10 1:20 66 6.6 75 50 8135 1913 10,995 3388 1892 860 3689 235
0.58 0.67 1.92 0.11 3.4 0.6 5.1 2.7 8.8 1.7 20.7 7.3 29.9 2.8 42.0 25.2 16.2 0.2 16.8 16.0 1:12 1:6 1:20 72 6.4 75 50 4465 950 7208 3432 898 133 1690 590
Side-slopes flatter than 1:20 were considered as flat. Annual Average Daily Traffic. Annual Average Daily Truck Traffic.
Fig. 3. A cross-section view of some of the variables collected in the field survey.
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Killed and severe injury crashes (type K and A) – referred to as KSI crashes, Slight injury crashes (type B and C) – referred to as SI crashes, and Property damage only crashes (type O) – referred to as PDO crashes.
used in simulation and has been found to provide better results than purely random draws (Greene, 2012; Milton, Shankar, & Mannering, 2008; Washington et al., 2011). Estimation efficiency was improved by using 200 Halton draws as recommended by Greene (2012) and Halton (1960).
5. Discussion
Table 2 shows a summary of the statistics of target crash characteristics in this study.
Table 3 presents the results of the RP ordered logit regression model developed for the severity of crashes involving median traffic barriers. As the results of a log-likelihood test indicated, the RP model provided a superior fit in comparison to the traditional fixedeffect model. The random variables have been distinguished showing their standard deviation in Table 3. Also, a parameter estimate with a positive sign shows that the variable increases the odds of more severe injury upon crash, while decreasing the odds of more severe injury crashes when the sign is negative. Based on Table 3, cable barriers with a minimum height of 30, and a maximum height of 42 in. were found to be the least severe type in median traffic barrier crashes. The superior performance of cable barriers in terms of crash severity is consistent with past studies (Russo & Savolainen, 2018; Zou et al., 2014). However, finding lower severity in crashes involving taller cable barriers could show that they may be more consistent with vehicle dimensions, or they might perform better in absorbing crash forces than shorter cable barriers. W-beam barriers with a height between 24 and 30 in. tended to experience lower crash severity. According to the tests done by the Federal Highway Administration (FHWA, 2008), W-beam barriers with a height shorter than 24 in. were categorized as ‘‘no longer reasonably functional.” A similar finding was identified in Table 3. Also, the maximum acceptable height should be considered equal to 30 in. for W-beam barriers to minimize the crash severity. Concrete barriers increased the odds of more severe injury upon crash if their height was shorter than 32 in. RDG (AASHTO, 2011) recommends two heights of 32 and 42 in. for concrete barriers. Therefore, the result found in Table 3 regarding short concrete barriers is expected, especially in Wyoming, where a taller concrete barrier is mostly recommended due to the significant truck volume. Note should be taken that truck traffic composition is up to 60% in some segments of I-80 in Wyoming (Mehrara Molan, Rezapour, & Ksaibati, 2019b). The severity of crashes involving a box beam barrier had an insignificant relationship with height. To present a clear view regarding the role of traffic barrier’s height in crashes, Fig. 4 compared the percentage of KSI and SI crashes found in each traffic barrier type with respect to their
4. Statistical analysis The statistical analysis was done using NLOGIT 6 statistical package (Econometric Software Inc., 2016). The standard ordered response logit model is derived by defining an unobservable variable z, which is used as a basis for modeling the ordinal ranking of data (Washington, Karlaftis, & Mannering, 2011). The discrete injury severity categories are assumed to be associated with this latent variable. This latent variable is mostly specified as a linear function for each observation such that (Washington et al., 2011):
z ¼ bX i þ ei
ð1Þ
where, X i is a vector of variables determining the discrete ordering of for each crash observation, b is a vector of estimable parameters, and ei is a random error term. Using the above equation, observed injuries (y) which are ordinal, for each observation can be expressed as:
y ¼ 1 if z l0 y ¼ 2 if l0 z l1 y ¼ 3 if
ð2Þ
l1 z l2
where, l are estimable threshold parameters that define y, and correspond to integer ordering. To estimate the parameterl, with the model parameters b, an assumption is made on the distribution of the random error term, e. If the distribution of the error term is assumed to be logistically distributed across observations, an ordered logit model results. On the other hand, a normal distribution assumption of the error term will result in the ordered probit model. The lower threshold l0 , is usually set to zero and results in the outcome probabilities Pðy ¼ iÞ ¼ r li bX rðli1 bXÞ where li and li1 represent the upper and lower thresholds for injury severity i (Washington et al., 2011). The numerical integration of the ordered logit model with random parameters is achieved by using a simulation-based maximum likelihood analysis. The process is performed by imposing Halton draws. The Halton draws approach has been commonly
Table 2 Summary of target crash data collected. Variable
Crash Severity
Surface Condition
Vehicle Type
Rollover Involved Total
Box Beam Barriers
Killed and Severe Slight Injury Property Damage Only Dry Wet Snowy/Icy Passenger Car SUV and Van Pickup Truck (>10,000 lbs) Motorcycle Yes No
W-Beam Barriers
Concrete Barriers
Cable Barriers
Overall
No.
%
No.
%
No.
%
No.
%
No.
%
31 152 863 296 120 630 405 196 350 89 6 65 981 1046
3 15 82 28 12 60 38 19 33 9 1 6 94 100
14 46 315 118 45 212 135 59 124 54 3 33 342 375
4 12 84 31 13 56 36 16 33 14 1 9 91 100
10 95 265 91 59 220 153 105 87 23 2 20 350 370
3 26 71 25 16 59 41 28 24 6 1 5 95 100
2 9 77 20 14 54 34 18 28 8 0 4 84 88
2 10 88 23 16 61 39 20 32 9 0 5 95 100
57 302 1520 525 238 1116 727 378 589 174 11 122 1757 1879
3 16 81 28 13 59 39 20 31 9 1 6 94 100
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A. Mehrara Molan et al. / Journal of Safety Research 71 (2019) 163–171 Table 3 Random parameters ordered logit model for crash severity of median traffic barriers on interstate roads. Variable Constant Traffic Barrier
Traffic, Road, and Crash Environment
Box Beam Barriers with a Front-slope Height of 0.1–1 ft Standard Deviation of Parameter Distribution Box Beam Barriers Located on a Flat Slope Box Beam Barriers with a Lateral Offset Shorter than 2 ft Cable Barriers with a Height of 30–42 in Concrete Barriers with a Height Shorter than 32 in W-Beam Barriers with a Height of 24–30 in W-Beam Barriers with a Post-Spacing of 6.1–6.3 ft Standard Deviation of Parameter Distribution Dry Surface Conditions Standard Deviation of Parameter Distribution Horizontal Curves with a Radius Shorter than 2000 ft Log Annual Average Daily Truck Traffic (AADTT) Standard Deviation of Parameter Distribution Motorcycles Female Drivers Driver Improper Restraint Rollover Occurred Drivers with a Record of Alcohol Citation
Estimate
Standard Error
P-Value
0.183 0.762 0.871 0.473 0.358 1.187 0.225 0.27 0.798 1.583 0.208 0.588 0.298 0.500 0.152 3.884 0.446 0.892 1.629 0.758
0.476 0.126 0.085 0.157 0.124 0.380 0.192 0.155 0.202 0.160 0.094 0.074 0.151 0.143 0.013 0.565 0.083 0.110 0.135 0.178
0.700 <0.001 <0.001 <0.001 <0.001 <0.001 0.048 0.040 <0.001 <0.001 0.027 <0.001 0.039 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Restricted Log Likelihood = 1,067.20. Log Likelihood at Convergence (RP) = 897.63. Log Likelihood at Convergence (Fixed) = 905.89.
Fig. 4. The percentage of different severity level crashes in each median traffic barrier included.
height. Note that all the categories mentioned in Fig. 4 had more than 100 observations, except the categories of cable barrier (<30 in.), cable barrier (=>30 in.), short concrete barrier (<32 in.), short W-beam barrier (<24 in.), and short box beam barrier (<25 in.) with 43, 45, 64, 42, and 68 observations, respectively. According to Fig. 4, short W-beam barriers increases the odds of more severe injury crash. In terms of SI crashes, concrete barriers had the highest number of SI crashes by a percentage of 25–27% of all crashes hitting the median concrete barrier. This rate of SI crashes was seen considerably higher than all the other median traffic barriers considered in this study. On the other hand, both of the cable barriers categories had the lowest percentage of injury crashes. As another important point, the height between 27 and 29 in. in box beam barriers should be the most appropriate level to reduce KSI crashes. Based on statistics, there were only three
KSI crashes out of 228 crashes identified in this category. Also, concrete barriers with a height of 32–38 were found to be less severe than taller concrete barriers (>38 in.). This result might be related to the fact that the height of car drivers’ eye is about 42–51 in. (3.5–4.25 ft.) based on the Green Book (AASHTO, 2018). In other words, there might be a higher probability that a car driver’s head may hit the concrete barrier when it has a height of more than 38 in. in crashes. Regarding other geometric dimensions found to be significant in Table 3, box beam barriers located on a flat or a slight front side-slope (with a maximum height of 1 ft. from the road surface) were more likely to decrease the risk of severe injury upon crash. Also, the odds of more injury upon crash increased when there was a lateral offset shorter than 2 ft. for box beam barriers. Similar to the recommendations provided regarding post-spacing by RDG
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(AASHTO, 2011), W-beam barriers with a post-spacing between 6.1 and 6.3 ft. resulted in lower crash severity. Interstate roads with a higher Annual Average Daily Truck Traffic (AADTT) were found to be less severe. This may be attributable to the fact that truck drivers are more experienced, and other drivers might also tend to driver more cautiously when there are more trucks. As previous studies also identified (Russo & Savolainen, 2018; Zou et al., 2014), female drivers, dry surface conditions, and motorcycles increased the risks of more severe injury upon crash involving median traffic barriers. As a possible reason for higher crash severity on dry surfaces, higher operational speed is expected on dry surface conditions in comparison to wet, snowy, and icy surfaces. Both unbelted drivers and drivers with a record of alcohol citation significantly increased odds of more severe injury upon crash. As expected, crashes involved with a rollover showed one of the highest parameter estimates (b = 1.629) among the variables related to the crash environment. Sharp horizontal curves (as opposed to tangent segments or horizontal curves with a radius larger than 2000 ft.) also increased the odds of more severe injuries upon crash. A competing multinomial logit model with random parameters (mixed logit model) was tested to analyze if it was better suited to the crash data. The results of the analysis indicated that the ordered logit model with random parameters provided a better fit to the data compared to the mixed logit model. The loglikelihood at convergence for the mixed logit model was found to be 960.38 compared to a value of 905.89 for the ordered logit model with random parameters. Also, the ordered logit model with random parameters resulted in 20 statistically significant parameters compared to 16 for the mixed logit model. Nine out of the 20 parameters for the ordered logit model with random parameters was found statistically significant were related to geometric features and other characteristics of traffic barriers in comparison to two for the mixed logit model. Based on practical and statistical considerations, the ordered logit model was therefore retained for this study.
6. Conclusions This study evaluated the performance of median traffic barriers with different geometric dimensions on the severity of crashes on interstate roads in Wyoming. For this purpose, a field survey was conducted to record geometric variables (e.g., height, postspacing, and lateral offset) of over 110 miles of median traffic barriers. The statistical analysis was done using an ordered logit model with random parameters. The following paragraphs summarize the significant findings of this study. Traffic barrier height was one of the main variables impacting the severity of crashes that involved hitting median traffic barriers. Concrete barriers with a height shorter than 32 in. were more likely to result in higher crash severity. On the other hand, crashes involving median cable barriers with a minimum height of 30 in. tended to be the least severe in comparison to other types. This finding might show that taller cable barriers (30 <= height <=42) could perform better in absorbing crash forces. To reduce odds of more severe injuries upon crash, a minimum height of 24 ft, and a maximum height equal to 30 in. should be recommended for W-beam barriers. Box beam barriers located on flat or slight side-slopes (with a vertical distance shorter than 1 ft. from the surface) resulted in lower severity of median traffic barrier crashes. Crashes involving W-beam barriers with a post-spacing of 6.1– 6.3 ft. were also least likely to cause a high-severity injury. The results show that there is an increased odds for severe injuries upon crashes for motorcycles in comparison to other vehicle types. As expected, unbelted drivers and drivers with an alcohol
citation record were found to significantly increase the risks of more severe injuries upon crash for median traffic barrier crashes. Interstate roads with a higher AADTT were more likely to decrease the odds of more severe injury upon crash with a fewer number of median barrier crashes. This finding could be attributable to the fact that truck drivers are usually more experienced than other drivers. Also, light vehicle drivers might be more cautious in interstate roads with higher truck traffic. Important variables including vehicle class and speed limit, which are known to impact crash severity, were not statistically significant in the severity model at the 0.05 confidence level. The insignificant impact of speed limit could be attributed with the small variability of speed limit on interstate roads in Wyoming. Note that the majority portion of interstate roads has the speed limit of 70–75 mph in Wyoming. It should be noted that speed limit was not also found to be statistically significant in the model presented by Zou et al. (2014). As a general point of view, the effect of speed limit might seem to be a controversial issue. Another study (Russo & Savolainen, 2018) showed that interstate roads with a lower speed limit could result in a higher probability of severe crashes involving median traffic barriers. A few important determinants of crash injury severity variables involving median traffic barriers such as collision speed and angle were not available for analysis. Future studies aimed at gaining more insights into barrier crashes should consider incorporating these variables to improve the crash severity model.
Acknowledgment The authors would like to acknowledge that this work is part of project #RS03218 funded by the Wyoming Department of Transportation (WYDOT). The subject matter, all figures, tables, and equations not previously copyrighted by outside sources are copyrighted by WYDOT, State of Wyoming and the University of Wyoming. All rights reserved copy righted in 2016.
References A Policy on Geometric Design of Highways and Streets. (2018). American Association of State Highway and Transportation (AASHTO). Washington, DC. Albuquerque, F., Stolle, C., Sicking, D., Faller, R., Lechtenberg, K., & Emerson. E. (2015). Optimization of guardrail length-of-need for interstate highways. 94th Annual Meeting of TRB 2015, Washington, DC, USA. Alluri, P., Gan, A., Haleem, K., & Mauthner, J. (2015). Safety performance of G4 (1S) W-beam guardrails versus cable median barriers on Florida’s freeways. Journal of Transportation Safety and Security, 7(3), 208–227. Atahan, A. O. (2016). Crashworthiness analysis of a bridge rail-to-guardrail transition. International Journal of Crashworthiness, 21(5), 423–434. https://doi. org/10.1080/13588265.2016.1164446. AutoCAD. (2018). Autodesk. Cafiso, S., Agostino, C., & Persaud, B. (2017). Investigating the influence on safety of retrofitting Italian motorways with barriers meeting a new EU standard. Traffic Injury Prevention, 18(3). Chimba, D., Emaasit, D., Allen, S., Hurst, B., & Nelson, M. (2013). Factors affecting median cable barrier crash frequency: New insights. Journal of Transportation Safety & Security, 6(1), 62–77. Econometric Software Inc. (2016). NLOGIT 6. Econometric Software Inc. Federal Highway Administration. (2008). W-beam guardrail repair. A guide for highway and 2008 street maintenance personnel. Google Earth. https://www.google.com/earth/. Greene, W. H. (2012). Econometric analysis. Boston, MA: Prentice Hall. Halton, J. (1960). On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik, 2(1), 84–90. Hampton, C., & Gabler, H. (2013). Development of a missing post repair guideline for longitudinal barrier crash safety. Journal of Transportation Engineering, 139, 6. Julin, R., Pajouh, M., Stolle, C., & Reid, J. (2017). Maximum mounting height for Midwest guardrail system (MGS). 96th Annual Meeting of TRB 2017, Washington, DC, USA. Ksaibati, K. & Mehrara Molan, A. (2018). Developing a new barrier condition index (BCI) to optimize barrier improvements in Wyoming (research proposal). Wyoming Department of Transportation. Li, N., Park, B., & Lambert, J. (2017). Effect of guardrail on reducing fatal and severe injuries on freeways: Real-world crash data analysis and performance
A. Mehrara Molan et al. / Journal of Safety Research 71 (2019) 163–171 assessment. Journal of Transportation Safety & Security. https://doi.org/10.1080/ 19439962.2017.1297970. Mehrara Molan, A., & Ksaibati, K. (2018). Developing the new barrier condition index (BCI) to unify the barrier assessments – A case study in wind river Indian reservation, Wyoming. The Open Transportation Journal, 12, 182–191. Mehrara Molan, A., Rezapour, M., & Ksaibati, K. (2019a). Modeling the impact of various variables on severity of crashes involved with traffic barriers. Journal of Transportation Safety and Security. Mehrara Molan, A., Rezapour, M., & Ksaibati, K. (2019b). Modeling traffic barriers crash severity by considering the effect of traffic barrier dimensions. The Journal of Modern Transportation, 27(2), 141–151. https://doi.org/10.1007/s40534-0190186-1. Mehrara Molan, A., Rezapour, M., & Ksaibati, K. (2019c). Investigating the Relationship between the type of Traffic Barrier and Crash Severity in Trucks and Light Vehicles. Journal of Traffic and Transportation Engineering. https://doi. org/10.1016/j.jtte.2019.03.004. Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: An exploratory empirical analysis. Accident Analysis and Prevention, 40(1), 260–266. National Cooperative Highway Research Program. (1981). Recommended procedures for the safety performance evaluation of highway appurtenances (NCHRP Report 230). Washington, DC: Transportation Research Board. National Cooperative Highway Research Program. (2019). Performance of longitudinal barriers on curved, super elevated roadway sections (NCHRP Report 894). Washington, DC: Transportation Research Board. National Safety Council. (1970). Manual on classification of motor vehicle traffic accidents. Chicago, USA. Roadside Design Guide. (2011). American Association of State Highway and Transportation (AASHTO). Washington, DC. Russo, B., & Savolainen, P. (2018). A comparison of freeway median crash frequency, severity, and barrier strike outcomes by median barrier type. Journal of Accident Analysis and Prevention, 117(2018), 216–224. Schmidt, J., Weiland, N., Reid, J., and Faller, R. (2015). Minimum Effective Length for the Midwest Guardrail System. 94th Annual Meeting of TRB, Washington DC. Tan, K., Tan, W., & Wong, S. (2008). Design of motorcyclist-friendly guardrail using finite element analysis. International Journal of Crashworthiness, 13(5), 567–577.
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Teng, T., Liang, C., Hsu, C., Shih, C., and Tran, T. (2016). Effect of Soil Properties on Safety Performance of W-beam Guardrail. International Conference on Advanced Material Science and Environmental Engineering (AMSEE 2016). Villwock, M., Blond, N., & Tarko, A. (2009). Cable barriers and traffic safety on rural interstates. Journal of Transportation Engineering, 137(4). Washington, S. P., Karlaftis, M. G., & Mannering, F. L. (2011). Statistical and econometric methods for transportation data analysis. Washington, DC: CRC Press. Wiebelhaus, M., Lechtenberg, K., Sicking, D., Faller, D., and Rosenbaugh, S. (2013). CostEffective Treatment of Existing Guardrail Systems. MwRSF Research Report No. TRP03-254-13. Wyoming Department of Transportation. (2019). Crash cost estimation worksheet. Zou, Y., Tarko, A. P., Chen, E., & Romero, M. A. (2014). Effectiveness of cable barriers, guardrails, and concrete barrier walls in reducing the risk of injury. Journal of Accident Analysis and Prevention, 72, 55–65. Amirarsalan Mehrara Molan, PhD., received his doctoral degree in Civil Engineering-Transportation from Wayne State University in 2017 and he is a Lecturer at California Polytechnic State University now. To date, he has been first author or co-author of over 25 peer-reviewed articles in civil engineering journals and conferences. His main research interests are highway design, highway safety, and traffic operation analysis. Milhan Moomen, PhD., is a Postdoctoral Research Associate at the University of Wyoming. He received his PhD degree at the University of Wyoming in summer 2019. His area of research is in transportation engineering and truck safety. Mr. Moomen holds a master’degree from Purdue University. Khaled Ksaibati, Ph.D., P.E. obtained his BS degree from Wayne State University and his MS and Ph.D. degrees from Purdue University. Khaled Ksaibati worked for the Indiana Department of Transportation for a couple of years prior to coming to the University of Wyoming in 1990. He was promoted to an associate professor in 1997 and full professor in 2002. Khaled Ksaibati has authored and co-authored multiple peer-reviewed scientific papers and presented works at many national and International conferences. Khaled Ksaibati research interests include Pavement design, maintenance and rehabilitation, traffic safety, traffic operation and control.
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Journal of Safety Research journal homepage: www.elsevier.com/locate/jsr
Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets Amirarsalan Mehrara Molan a,⇑, Milhan Moomen b, Khaled Ksaibati b a
Department of Civil and Environmental Engineering, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA 93407 Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E. University Avenue, Dept. 3295, Laramie, WY 82071, United States b
a r t i c l e
i n f o
Article history: Received 18 April 2019 Received in revised form 23 August 2019 Accepted 1 October 2019 Available online 15 November 2019 Keywords: Median traffic barrier Traffic barrier dimensions Crash severity Random parameters ordered logit Wyoming
a b s t r a c t Introduction: Despite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads. Method: Geometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity. Results: Crashes involving cable barriers with a height between 3000 and 4200 were less severe than other traffic barrier types, while concrete barriers with a height shorter than 3200 were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers. In terms of variables impacting crash frequency, Wbeam and box beam barriers located on frontslopes had higher crash frequency. Practical applications: The results show that using flare barriers should reduce the number of crashes compared to parallel barriers. Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.
1. Introduction Traffic barrier crashes include a considerable portion of crashes in the United States. Based on the American Association of State Highway and Transportation Officials (AASHTO) Roadside Design Guide (AASHTO RDG, 2011), traffic barriers have been involved in 8% of fatal fixed-object crashes in the United States. In Wyoming, between 2008 and 2017, over 7600 traffic barrier crashes occurred with a total estimated crash cost of about $900 million based on the Wyoming Department of Transportation (WYDOT) crash cost estimation worksheet (Wyoming Department of Transportation. (2019) (2019), 2019). Based on statistics, traffic barrier crashes had the highest crash frequency among all fixed-object crashes ⇑ Corresponding author. E-mail addresses: [email protected] (A. Mehrara Molan), mmoomen@uwyo. edu (M. Moomen), [email protected] (K. Ksaibati). https://doi.org/10.1016/j.jsr.2019.10.001 0022-4375/Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.
with a percentage of 37% of whole fixed-object crashes reported in Wyoming (Mehrara Molan, Rezapour & Ksaibati, 2019a). Also, a constant ratio of 6.5% of all crashes was involved with traffic barriers annually between 2008 and 2017 in Wyoming. Meanwhile, the ratio of high-severity (fatal and incapacitating injury) barrier crashes had an increase from about 4.5% to near 6% in the same period (Ksaibati & Mehrara Molan, 2018). This fact shows that traffic barriers pose a higher risk in crashes compared to past years in Wyoming. As the first step to decrease the risk of traffic barrier crashes, it is essential to identify traffic barriers that are not operating efficiently in crashes as they may even be a more serious threat than the hazards and objects they are supposed to protect road users from. Reviewing past studies indicated that there are still big gaps and reservations regarding evaluating the performance of traffic barriers in crashes. For example, despite the numerous studies conducted using simulation tools or statistical methods, the effect of
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variables like traffic barrier height has not been investigated considering a comprehensive statewide actual crash dataset yet. This study aimed at filling this gap by evaluating the effect of the variables related to traffic barriers’ geometric dimensions (height, post-spacing, sideslope, lateral offset, etc.) on traffic barrier crashes. Finally, a statistical model was presented for the severity of crashes involving median traffic barriers on interstate roads in Wyoming. This study targeted the interstate roads because most traffic barrier crashes have been involved with this road functional class. Based on the Critical Analysis Reporting Environment (CARE) package, 45% of traffic barriers are located on interstate roads in Wyoming, while approximately accounting for 70% of traffic barrier crashes.
2. Literature review Simulation models have been in widespread use in various aspects of past traffic barrier assessments (Albuquerque et al., 2015; Atahan, 2016; Hampton & Gabler, 2013; Julin, Pajouh, Stolle, & Reid, 2017; Schmidt et al., 2015; Teng et al., 2016; Tan, Tan, & Wong, 2008). Albuquerque et al. (2015) investigated whether designers can design a shorter length for traffic barriers than the recommended length by the RDG (AASHTO, 2011) when the sideslope is flat. Research by Julin et al. (2017) examined the maximum traffic barrier height for the Midwest guardrail systems (MGS). They concluded that a 36-inch height could be considered as the maximum height that does not create a threat to vehicles in terms of underride crashes. Another focus of past simulation efforts (Atahan, 2016; Hampton & Gabler, 2013; Tan et al., 2008; Teng et al., 2016) was on evaluating the reaction of traffic barriers when vehicles hit them during crashes. Using simulation modeling, a new V-profile barrier was proposed and tested by Tan et al. (2008). Based on the results, the proposed design performed better than the traditional W-beam barriers in absorbing the energy in traffic barrier crashes. The research by Hampton and Gabler (2013) found that removing a post could increase the maximum deflection up to 25% in W-beam barriers. A few studies included a field survey for their safety analysis. Wiebelhaus et al. (2013) conducted a field survey on 68 W-beam barrier systems in Kansas, U.S., to recommend cost-effective improvements using Roadside Safety Analysis Program (RSAP). Mehrara Molan and Ksaibati (2018) created a new model called ‘‘barrier condition index (BCI)” to evaluate the existing condition of six traffic barrier systems compared with recommendations provided in RDG (AASHTO, 2011). Conducting actual crash analysis on historical crashes was another method used in past traffic barrier studies. From a general point of view, there are two study categories for previous actual crash data evaluations: (1) studies that conducted a before-after study in a specific location after installing new traffic barrier systems, and (2) studies that evaluated traffic barriers’ performance using statistical analysis on historical crashes. Villwock, Blond, and Tarko (2009) evaluated the effect of installing new median cable barriers in a before-after study. The study found that new cable barriers caused more crashes on wide medians; however, the crashes were less severe than before. In another before-after study (Chimba, Emaasit, Allen, Hurst, & Nelson, 2013), installing new cable barriers resulted in approximately 82%, and 76% fewer fatal and incapacitating injury crashes, respectively. According to Li et al. (2017), the severity of traffic barrier crashes could be reduced by 50% in highway departures with traffic barrier systems in comparison to the similar locations without any traffic barrier. Cafiso, Agostino, and Persaud (2017) estimated a crash modification factor (CMF) of 0.78 for upgrading old traffic barriers based on new standards in Europe. Russo and Savolainen (2018)
conducted a safety analysis on crashes involving median traffic barriers from 2009 through 2013 in Michigan. Among the median barriers studied by Russo and Savolainen (2018), cable barriers were least likely to cause a high-severity crash. A possible reason for this finding was that cable median barriers resulted in fewer vehicle re-direction back to the highway. This means that the possibility of having a secondary crash could be less in cable median barriers in comparison to the other barrier types. Research by Zou, Tarko, Chen, and Romero (2014) investigated the effective parameters on the severity of over 2000 traffic barrier crashes recorded between 2008 and 2012 in Indiana. Crashes involving near-side median cable barriers were found to be 57% less severe than crashes involving a guardrail face. Geometric characteristics of the traffic barrier systems play a pivotal role in promoting safety. With respect to barrier heights for instance, low barrier heights may increase the risk of override and rollover crashes, whereas excessive heights may increase the likelihood of small vehicles underriding or penetrating the barrier. Thus, the barrier height in relation to a vehicle’s bumper height at impact should be in a range that is neither too high nor too low to cause rollover or underriding crashes (AASHTO RDG, 2011). It should be noted that median barriers may be installed to prevent cross median crashes by containing or redirecting vehicles. For this reason, barriers are commonly grouped into flexible, semi-rigid, and rigid systems. The implication is that the choice of a barrier type in terms of materials and design is influenced by its role. For example, the RDG indicates that flexible or semi-rigid barriers can be installed in wide medians with relatively flat cross-slopes given that the dynamic deflection of the barrier is less than half the median width. For roadways with a narrow median width and high traffic volumes where deflections are not desired, rigid systems are usually installed. Post spacing is also known to impact vehicular deflections after a crash has occurred. The post spacing determines whether some barriers are rigid or flexible. Closely spaced posts increase barrier stiffness and decreases lateral deflection to some extent, while posts that are wider apart have the opposite effect (RDG, 2011). As mentioned in the previous paragraphs, past studies provided valuable findings by evaluating the effect of several variables on traffic barrier crashes utilizing simulation tools, field surveys, and crash data analysis with an actual crash dataset. However, as demonstrated in the literature review, previous studies did not explicitly investigate the impact of the geometric variables of traffic barriers on injury severity considering statewide crash analysis with actual data. The effect of the geometry of traffic barriers (most especially barrier height) on crashes have been previously analyzed through simulation and field tests. Most of these tests mainly focused on the likelihood of rollover, penetration, lateral deflection, and redirection of vehicles after a crash with the barrier but did not directly relate the geometric factors to crash injury severity. Also, it should be mentioned that simulation and field crash tests are involved with limitations. While there are numerous possible vehicle paths in ROR crashes, simulation and crash field tests could model only a limited number of them (National Cooperative Highway Research Program, 2019). Therefore, actual crash data could consider a larger number of variables in terms of vehicle type, speed, road geometry, weather conditions, driver features, etc. From another point of view, crash tests are complex experiments because they could result in random dynamic performance in duplicating the crash tests (National Cooperative Highway Research Program, 1981). The current study aims to provide new findings and contribute to the body of existing literature using actual crash datasets. The main contribution of this study is the analysis of effect of dimensional variables of median traffic barriers on injury severity considering a statewide crash dataset. Also, based on the literature review, there are just a few traffic barrier
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studies conducted in Rocky Mountain Region (RMR). It is expected that this study will identify new insights regarding variables impacting median traffic barrier crashes because of challenging road geometry, significant truck demand, and adverse weather conditions dominating in the RMR.
3. Data preparation 3.1. Data collection As the first step to include traffic barrier dimensional variables in the dataset, a field survey was conducted from summer 2016 through summer 2018 to measure about 0.6 million linear feet (over 110 miles) of traffic barriers across interstate roadways in Wyoming. Based on the data collected, overall, 204 miles median traffic barriers exist on a total 912 miles of interstate roads in Wyoming (0.22 miles median traffic barriers per mile). The traffic barriers inventoried included 53.1, 27.6, 16.7, and 16.3 miles of box beam, W-beam, concrete, and cable systems, respectively. Fig. 1 shows a picture of each traffic barrier type inventoried for this study. Based on the CARE package, the traffic barriers collected were involved in 1879 crashes. It should be noted that 3151 median barrier crashes have been recorded between 2008 and 2017 in Wyoming. It means that this study included 60% of all median traffic
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barrier crashes. Overall, 7622 traffic barrier crashes were reported in this period; however, 66 work zone related, 295 multi-vehicle, and 437 end-treatment crashes were ignored from the dataset to include only the crashes with a direct impact of traffic barriers. The reason is that the severity experienced in these crashes might not be as a direct result of traffic barriers. Also, 315 crashes were removed due to possible errors by police officers in recording the accurate location of crashes. In fact, these crashes were reported as a traffic barrier crash, while there was no traffic barrier at the location reported. In summary, 6509 crashes were selected of which 3151 were involved with median traffic barriers. As another modification considered in providing the final dataset, the traffic barrier types recorded as unknown were checked by reviewing the photos taken during the field survey. Based on the scope defined in this research, the traffic barrier survey was done statewide to collect geometric dimensions of all traffic barriers apart from cable barriers. Cable barrier systems were not collected statewide since they were mostly new and designed based on recent design standards and policies. Moreover, they were already identified as the median traffic barriers causing the minimum risk regarding crash severity in the previous studies (Russo & Savolainen, 2018; Chimba et al., 2013; Alluri, Gan, Haleem, & Mauthner, 2015; Zou et al., 2014; Mehrara Molan, Rezapour, & Ksaibati (2019c); and Olsen et al., 2013). Therefore, the other traffic barrier types were placed as the priority for future improvements by decision-makers in Wyoming. Note that the
Fig. 1. Different traffic barrier types inventoried during the field survey.
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other types were inventoried completely, apart from a few cases where there was a safety risk for data collectors in measuring geometric dimensions. Traffic barriers were collected considering five points on the barriers as shown in Fig. 2. 3.2. Data description Table 1 provides a summary regarding geometric variables of traffic barriers inventoried during the field survey, and some road characteristics collected in this study. The traffic barrier data were combined with historical crashes recorded in the CARE package. The CARE package has been used by researchers in Wyoming since 2009. It provides the most comprehensive crash dataset in Wyoming considering a set of over 160 different variables related to crash environment (road traffic volume, road geometry, weather condition, etc.) and driver characteristics (age, gender, residency, etc.). For a few variables related to traffic volume and road geometric features, CARE has provided only an estimation. Therefore, traffic volume data and road
geometric features were obtained using WYDOT datasets. Also, in a few road segments with no available geometric data, Wyoming pathweb software (2018), Google Earth, and AutoCAD (2018) were utilized to include geometric data of missing rows in the dataset (Fig. 3). Average annual snowfall and average annual snow days were extracted from the National Oceanic and Atmospheric Administration (NOAA, 2018). Reviewing the weather data, annually, an average of 38 snowy days and 64.15 in. snowfall were recorded in the nearest weather stations to the median traffic barriers. The KABCO severity scale was utilized for categorizing the injury level of median traffic barrier crashes. There are five injury levels for crashes based on KABCO scale (NSC 1970): fatal crashes (type K), incapacitating injury crashes (type A), non-incapacitating injury crashes (type B), possible injury crashes (type C), and property damage only crashes (type O, or PDO). This study combined crashes type K and A, and type B and C together due to the lower number of fatal and injury crashes in comparison to PDO crashes. Therefore, the injury scale considered in this analysis was defined as below:
Fig. 2. Five points considered during traffic barrier inventory.
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A. Mehrara Molan et al. / Journal of Safety Research 71 (2019) 163–171 Table 1 Summary of variables collected conducting a field survey in this study. Road features Traffic Barrier Geometric Features
Length (mile)
Shoulder Width (ft)
Lateral Offset (ft)
System Height (in)
Post-Spacing (ft)
Front-Slopea Ratio (V:H)
Back-Slopea Ratio (V:H)
Road Characteristics
Speed Limit (mph)
AADTb (vehicle/day)
AADTTc (truck/day)
a b c
Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Maximum Minimum Mean Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum Mean Standard Deviation Maximum Minimum
Box Beam Barriers
W-Beam Barriers
Concrete Barriers
Cable Barriers
0.14 0.17 0.93 0.01 6.5 3.6 22.1 0.0 4.0 5.6 23.5 0.0 29.4 2.7 37.2 18.0 6.0 3.6 22.1 1.2 1:6 1:0.6 1:20 1:5 1:2 1:20 72 5.5 75 50 6303 2567 12,903 749 2037 1110 3689 131
0.68 0.53 2.20 0.01 4.8 2.9 13.0 0.0 4.8 5.0 26.0 0.0 29.2 3.1 37.2 16.8 6.4 1.3 12.6 2.5 1:6 1:4 1:20 1:8 1:6 1:20 73 6.4 75 50 6002 1676 12,903 1624 2488 803 3413 284
0.41 0.67 1.94 0.01 10.6 7.4 25.4 0.0 0.6 2.1 12.7 0.0 35.1 5.6 55.2 20.4 1:8 1:5 1:20 1:10 1:10 1:20 66 6.6 75 50 8135 1913 10,995 3388 1892 860 3689 235
0.58 0.67 1.92 0.11 3.4 0.6 5.1 2.7 8.8 1.7 20.7 7.3 29.9 2.8 42.0 25.2 16.2 0.2 16.8 16.0 1:12 1:6 1:20 72 6.4 75 50 4465 950 7208 3432 898 133 1690 590
Side-slopes flatter than 1:20 were considered as flat. Annual Average Daily Traffic. Annual Average Daily Truck Traffic.
Fig. 3. A cross-section view of some of the variables collected in the field survey.
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Killed and severe injury crashes (type K and A) – referred to as KSI crashes, Slight injury crashes (type B and C) – referred to as SI crashes, and Property damage only crashes (type O) – referred to as PDO crashes.
used in simulation and has been found to provide better results than purely random draws (Greene, 2012; Milton, Shankar, & Mannering, 2008; Washington et al., 2011). Estimation efficiency was improved by using 200 Halton draws as recommended by Greene (2012) and Halton (1960).
5. Discussion
Table 2 shows a summary of the statistics of target crash characteristics in this study.
Table 3 presents the results of the RP ordered logit regression model developed for the severity of crashes involving median traffic barriers. As the results of a log-likelihood test indicated, the RP model provided a superior fit in comparison to the traditional fixedeffect model. The random variables have been distinguished showing their standard deviation in Table 3. Also, a parameter estimate with a positive sign shows that the variable increases the odds of more severe injury upon crash, while decreasing the odds of more severe injury crashes when the sign is negative. Based on Table 3, cable barriers with a minimum height of 30, and a maximum height of 42 in. were found to be the least severe type in median traffic barrier crashes. The superior performance of cable barriers in terms of crash severity is consistent with past studies (Russo & Savolainen, 2018; Zou et al., 2014). However, finding lower severity in crashes involving taller cable barriers could show that they may be more consistent with vehicle dimensions, or they might perform better in absorbing crash forces than shorter cable barriers. W-beam barriers with a height between 24 and 30 in. tended to experience lower crash severity. According to the tests done by the Federal Highway Administration (FHWA, 2008), W-beam barriers with a height shorter than 24 in. were categorized as ‘‘no longer reasonably functional.” A similar finding was identified in Table 3. Also, the maximum acceptable height should be considered equal to 30 in. for W-beam barriers to minimize the crash severity. Concrete barriers increased the odds of more severe injury upon crash if their height was shorter than 32 in. RDG (AASHTO, 2011) recommends two heights of 32 and 42 in. for concrete barriers. Therefore, the result found in Table 3 regarding short concrete barriers is expected, especially in Wyoming, where a taller concrete barrier is mostly recommended due to the significant truck volume. Note should be taken that truck traffic composition is up to 60% in some segments of I-80 in Wyoming (Mehrara Molan, Rezapour, & Ksaibati, 2019b). The severity of crashes involving a box beam barrier had an insignificant relationship with height. To present a clear view regarding the role of traffic barrier’s height in crashes, Fig. 4 compared the percentage of KSI and SI crashes found in each traffic barrier type with respect to their
4. Statistical analysis The statistical analysis was done using NLOGIT 6 statistical package (Econometric Software Inc., 2016). The standard ordered response logit model is derived by defining an unobservable variable z, which is used as a basis for modeling the ordinal ranking of data (Washington, Karlaftis, & Mannering, 2011). The discrete injury severity categories are assumed to be associated with this latent variable. This latent variable is mostly specified as a linear function for each observation such that (Washington et al., 2011):
z ¼ bX i þ ei
ð1Þ
where, X i is a vector of variables determining the discrete ordering of for each crash observation, b is a vector of estimable parameters, and ei is a random error term. Using the above equation, observed injuries (y) which are ordinal, for each observation can be expressed as:
y ¼ 1 if z l0 y ¼ 2 if l0 z l1 y ¼ 3 if
ð2Þ
l1 z l2
where, l are estimable threshold parameters that define y, and correspond to integer ordering. To estimate the parameterl, with the model parameters b, an assumption is made on the distribution of the random error term, e. If the distribution of the error term is assumed to be logistically distributed across observations, an ordered logit model results. On the other hand, a normal distribution assumption of the error term will result in the ordered probit model. The lower threshold l0 , is usually set to zero and results in the outcome probabilities Pðy ¼ iÞ ¼ r li bX rðli1 bXÞ where li and li1 represent the upper and lower thresholds for injury severity i (Washington et al., 2011). The numerical integration of the ordered logit model with random parameters is achieved by using a simulation-based maximum likelihood analysis. The process is performed by imposing Halton draws. The Halton draws approach has been commonly
Table 2 Summary of target crash data collected. Variable
Crash Severity
Surface Condition
Vehicle Type
Rollover Involved Total
Box Beam Barriers
Killed and Severe Slight Injury Property Damage Only Dry Wet Snowy/Icy Passenger Car SUV and Van Pickup Truck (>10,000 lbs) Motorcycle Yes No
W-Beam Barriers
Concrete Barriers
Cable Barriers
Overall
No.
%
No.
%
No.
%
No.
%
No.
%
31 152 863 296 120 630 405 196 350 89 6 65 981 1046
3 15 82 28 12 60 38 19 33 9 1 6 94 100
14 46 315 118 45 212 135 59 124 54 3 33 342 375
4 12 84 31 13 56 36 16 33 14 1 9 91 100
10 95 265 91 59 220 153 105 87 23 2 20 350 370
3 26 71 25 16 59 41 28 24 6 1 5 95 100
2 9 77 20 14 54 34 18 28 8 0 4 84 88
2 10 88 23 16 61 39 20 32 9 0 5 95 100
57 302 1520 525 238 1116 727 378 589 174 11 122 1757 1879
3 16 81 28 13 59 39 20 31 9 1 6 94 100
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A. Mehrara Molan et al. / Journal of Safety Research 71 (2019) 163–171 Table 3 Random parameters ordered logit model for crash severity of median traffic barriers on interstate roads. Variable Constant Traffic Barrier
Traffic, Road, and Crash Environment
Box Beam Barriers with a Front-slope Height of 0.1–1 ft Standard Deviation of Parameter Distribution Box Beam Barriers Located on a Flat Slope Box Beam Barriers with a Lateral Offset Shorter than 2 ft Cable Barriers with a Height of 30–42 in Concrete Barriers with a Height Shorter than 32 in W-Beam Barriers with a Height of 24–30 in W-Beam Barriers with a Post-Spacing of 6.1–6.3 ft Standard Deviation of Parameter Distribution Dry Surface Conditions Standard Deviation of Parameter Distribution Horizontal Curves with a Radius Shorter than 2000 ft Log Annual Average Daily Truck Traffic (AADTT) Standard Deviation of Parameter Distribution Motorcycles Female Drivers Driver Improper Restraint Rollover Occurred Drivers with a Record of Alcohol Citation
Estimate
Standard Error
P-Value
0.183 0.762 0.871 0.473 0.358 1.187 0.225 0.27 0.798 1.583 0.208 0.588 0.298 0.500 0.152 3.884 0.446 0.892 1.629 0.758
0.476 0.126 0.085 0.157 0.124 0.380 0.192 0.155 0.202 0.160 0.094 0.074 0.151 0.143 0.013 0.565 0.083 0.110 0.135 0.178
0.700 <0.001 <0.001 <0.001 <0.001 <0.001 0.048 0.040 <0.001 <0.001 0.027 <0.001 0.039 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Restricted Log Likelihood = 1,067.20. Log Likelihood at Convergence (RP) = 897.63. Log Likelihood at Convergence (Fixed) = 905.89.
Fig. 4. The percentage of different severity level crashes in each median traffic barrier included.
height. Note that all the categories mentioned in Fig. 4 had more than 100 observations, except the categories of cable barrier (<30 in.), cable barrier (=>30 in.), short concrete barrier (<32 in.), short W-beam barrier (<24 in.), and short box beam barrier (<25 in.) with 43, 45, 64, 42, and 68 observations, respectively. According to Fig. 4, short W-beam barriers increases the odds of more severe injury crash. In terms of SI crashes, concrete barriers had the highest number of SI crashes by a percentage of 25–27% of all crashes hitting the median concrete barrier. This rate of SI crashes was seen considerably higher than all the other median traffic barriers considered in this study. On the other hand, both of the cable barriers categories had the lowest percentage of injury crashes. As another important point, the height between 27 and 29 in. in box beam barriers should be the most appropriate level to reduce KSI crashes. Based on statistics, there were only three
KSI crashes out of 228 crashes identified in this category. Also, concrete barriers with a height of 32–38 were found to be less severe than taller concrete barriers (>38 in.). This result might be related to the fact that the height of car drivers’ eye is about 42–51 in. (3.5–4.25 ft.) based on the Green Book (AASHTO, 2018). In other words, there might be a higher probability that a car driver’s head may hit the concrete barrier when it has a height of more than 38 in. in crashes. Regarding other geometric dimensions found to be significant in Table 3, box beam barriers located on a flat or a slight front side-slope (with a maximum height of 1 ft. from the road surface) were more likely to decrease the risk of severe injury upon crash. Also, the odds of more injury upon crash increased when there was a lateral offset shorter than 2 ft. for box beam barriers. Similar to the recommendations provided regarding post-spacing by RDG
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(AASHTO, 2011), W-beam barriers with a post-spacing between 6.1 and 6.3 ft. resulted in lower crash severity. Interstate roads with a higher Annual Average Daily Truck Traffic (AADTT) were found to be less severe. This may be attributable to the fact that truck drivers are more experienced, and other drivers might also tend to driver more cautiously when there are more trucks. As previous studies also identified (Russo & Savolainen, 2018; Zou et al., 2014), female drivers, dry surface conditions, and motorcycles increased the risks of more severe injury upon crash involving median traffic barriers. As a possible reason for higher crash severity on dry surfaces, higher operational speed is expected on dry surface conditions in comparison to wet, snowy, and icy surfaces. Both unbelted drivers and drivers with a record of alcohol citation significantly increased odds of more severe injury upon crash. As expected, crashes involved with a rollover showed one of the highest parameter estimates (b = 1.629) among the variables related to the crash environment. Sharp horizontal curves (as opposed to tangent segments or horizontal curves with a radius larger than 2000 ft.) also increased the odds of more severe injuries upon crash. A competing multinomial logit model with random parameters (mixed logit model) was tested to analyze if it was better suited to the crash data. The results of the analysis indicated that the ordered logit model with random parameters provided a better fit to the data compared to the mixed logit model. The loglikelihood at convergence for the mixed logit model was found to be 960.38 compared to a value of 905.89 for the ordered logit model with random parameters. Also, the ordered logit model with random parameters resulted in 20 statistically significant parameters compared to 16 for the mixed logit model. Nine out of the 20 parameters for the ordered logit model with random parameters was found statistically significant were related to geometric features and other characteristics of traffic barriers in comparison to two for the mixed logit model. Based on practical and statistical considerations, the ordered logit model was therefore retained for this study.
6. Conclusions This study evaluated the performance of median traffic barriers with different geometric dimensions on the severity of crashes on interstate roads in Wyoming. For this purpose, a field survey was conducted to record geometric variables (e.g., height, postspacing, and lateral offset) of over 110 miles of median traffic barriers. The statistical analysis was done using an ordered logit model with random parameters. The following paragraphs summarize the significant findings of this study. Traffic barrier height was one of the main variables impacting the severity of crashes that involved hitting median traffic barriers. Concrete barriers with a height shorter than 32 in. were more likely to result in higher crash severity. On the other hand, crashes involving median cable barriers with a minimum height of 30 in. tended to be the least severe in comparison to other types. This finding might show that taller cable barriers (30 <= height <=42) could perform better in absorbing crash forces. To reduce odds of more severe injuries upon crash, a minimum height of 24 ft, and a maximum height equal to 30 in. should be recommended for W-beam barriers. Box beam barriers located on flat or slight side-slopes (with a vertical distance shorter than 1 ft. from the surface) resulted in lower severity of median traffic barrier crashes. Crashes involving W-beam barriers with a post-spacing of 6.1– 6.3 ft. were also least likely to cause a high-severity injury. The results show that there is an increased odds for severe injuries upon crashes for motorcycles in comparison to other vehicle types. As expected, unbelted drivers and drivers with an alcohol
citation record were found to significantly increase the risks of more severe injuries upon crash for median traffic barrier crashes. Interstate roads with a higher AADTT were more likely to decrease the odds of more severe injury upon crash with a fewer number of median barrier crashes. This finding could be attributable to the fact that truck drivers are usually more experienced than other drivers. Also, light vehicle drivers might be more cautious in interstate roads with higher truck traffic. Important variables including vehicle class and speed limit, which are known to impact crash severity, were not statistically significant in the severity model at the 0.05 confidence level. The insignificant impact of speed limit could be attributed with the small variability of speed limit on interstate roads in Wyoming. Note that the majority portion of interstate roads has the speed limit of 70–75 mph in Wyoming. It should be noted that speed limit was not also found to be statistically significant in the model presented by Zou et al. (2014). As a general point of view, the effect of speed limit might seem to be a controversial issue. Another study (Russo & Savolainen, 2018) showed that interstate roads with a lower speed limit could result in a higher probability of severe crashes involving median traffic barriers. A few important determinants of crash injury severity variables involving median traffic barriers such as collision speed and angle were not available for analysis. Future studies aimed at gaining more insights into barrier crashes should consider incorporating these variables to improve the crash severity model.
Acknowledgment The authors would like to acknowledge that this work is part of project #RS03218 funded by the Wyoming Department of Transportation (WYDOT). The subject matter, all figures, tables, and equations not previously copyrighted by outside sources are copyrighted by WYDOT, State of Wyoming and the University of Wyoming. All rights reserved copy righted in 2016.
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Teng, T., Liang, C., Hsu, C., Shih, C., and Tran, T. (2016). Effect of Soil Properties on Safety Performance of W-beam Guardrail. International Conference on Advanced Material Science and Environmental Engineering (AMSEE 2016). Villwock, M., Blond, N., & Tarko, A. (2009). Cable barriers and traffic safety on rural interstates. Journal of Transportation Engineering, 137(4). Washington, S. P., Karlaftis, M. G., & Mannering, F. L. (2011). Statistical and econometric methods for transportation data analysis. Washington, DC: CRC Press. Wiebelhaus, M., Lechtenberg, K., Sicking, D., Faller, D., and Rosenbaugh, S. (2013). CostEffective Treatment of Existing Guardrail Systems. MwRSF Research Report No. TRP03-254-13. Wyoming Department of Transportation. (2019). Crash cost estimation worksheet. Zou, Y., Tarko, A. P., Chen, E., & Romero, M. A. (2014). Effectiveness of cable barriers, guardrails, and concrete barrier walls in reducing the risk of injury. Journal of Accident Analysis and Prevention, 72, 55–65. Amirarsalan Mehrara Molan, PhD., received his doctoral degree in Civil Engineering-Transportation from Wayne State University in 2017 and he is a Lecturer at California Polytechnic State University now. To date, he has been first author or co-author of over 25 peer-reviewed articles in civil engineering journals and conferences. His main research interests are highway design, highway safety, and traffic operation analysis. Milhan Moomen, PhD., is a Postdoctoral Research Associate at the University of Wyoming. He received his PhD degree at the University of Wyoming in summer 2019. His area of research is in transportation engineering and truck safety. Mr. Moomen holds a master’degree from Purdue University. Khaled Ksaibati, Ph.D., P.E. obtained his BS degree from Wayne State University and his MS and Ph.D. degrees from Purdue University. Khaled Ksaibati worked for the Indiana Department of Transportation for a couple of years prior to coming to the University of Wyoming in 1990. He was promoted to an associate professor in 1997 and full professor in 2002. Khaled Ksaibati has authored and co-authored multiple peer-reviewed scientific papers and presented works at many national and International conferences. Khaled Ksaibati research interests include Pavement design, maintenance and rehabilitation, traffic safety, traffic operation and control.