Exploring factors contributing to injury severity at work

IATSSR-00191; No of Pages 8 IATSS Research xxx (xxxx) xxx

Contents lists available at ScienceDirect

IATSS Research

Research article

Exploring factors contributing to injury severity at work zones considering adverse weather conditions Ali Ghasemzadeh ⁎, Mohamed M. Ahmed Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States

a r t i c l e

i n f o

Article history: Received 29 March 2018 Received in revised form 15 October 2018 Accepted 16 November 2018 Available online xxxx Keywords: Work zone Injury Severity Crash Characteristics Ordered Probit Model Adverse Weather Conditions Lighting Conditions

a b s t r a c t Despite recent efforts to improve work zone safety, the frequency and severity of crashes at work zones are still considerably high. The effect of work zones on traffic safety can be exacerbated by adverse weather conditions. As an example, a sudden reduction in visibility may intensify the severity of work zone crashes. There is a lack of studies that strive to gain a good understanding of the effect of weather on the severity of work zone crashes. In this study, an Ordered Probit Model was developed to identify factors affecting the severity of work zone crashes in different spatial, temporal, and environmental conditions in Washington state using five-year of work zone-related crashes (2009–2013). The interesting findings of this study showed that weather and lighting conditions are among the most important factors influencing the severity of crashes at work zones. Lack of daylight was found to be a determining factor in increasing the severity of work zone crashes, specifically, during dusk and dawn. It was also found that although drivers have less severe work zone-related crashes in adverse weather conditions, the interactions between adverse weather conditions and other contributing factors might increase the severity of work zone crashes. The results of this study will help traffic engineers to design effective safety countermeasures considering different contributing factors including the weather and lighting conditions in the work zone planning and installation stages to prevent safety deficiencies. © 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction The safe and efficient mobility of traffic in work zones is a major concern to transportation officials. In the past few years, work zones have evolved from simple layouts that may not have been particularly effective in promoting safety into more modern designs that attempt to improve safety [1]. According to The National Work Zone Safety Information Clearinghouse, N12,000 fatal work zone crashes were reported between 2000 and 2013. Adverse weather conditions might intensify the severity of crashes. Inclement weather conditions such as fog, snow, ground blizzards, slush, rain, and strong winds negatively affect pavement conditions, vehicle performance, visibility, and driver behavior. Driver behavior exhibits high variability and is difficult to quantify, particularly in inclement weather conditions, and it is imperative to understand when describing the influence of adverse weather conditions on roadways. Adverse weather inhibits a drivers' ability to perceive their

environment, and visibility reductions – caused by adverse weather events – are known to increase the likelihood of crashes. Many studies have investigated the impact of the work zone on injury severity [2–4]. These studies revealed that factors including but not limited to age, gender, lighting conditions, and posted speed limits are among the most important factors affecting the severity of work zone crashes. However, there is a lack of studies that explicitly considered the impact of adverse weather conditions on work zone crash severity. Identifying factors affecting the severity of work zone crashes in different weather conditions is the basis for developing effective strategies to enhance work zone safety in all weather and traffic conditions. Therefore, the objective of this study is to investigate the impact of weather conditions as well as other spatial, temporal, and environmental conditions on the severity of work zone crashes. 2. Background 2.1. Work zone related studies

⁎ Corresponding author. E-mail addresses: [email protected] (A. Ghasemzadeh), [email protected] (M.M. Ahmed). Peer review under responsibility of International Association of Traffic and Safety Sciences.

A number of studies have been conducted on work zone crashes. Results from these studies showed that crash frequency and rate increase in work zones [1,4,5]. A study on fatal crashes in Texas showed that 8% of traffic fatalities were directly related to work zones, and 39% were indirectly influenced by work zones [1].

https://doi.org/10.1016/j.iatssr.2018.11.002 0386-1112/© 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: A. Ghasemzadeh and M.M. Ahmed, Exploring factors contributing to injury severity at work zones considering adverse weather conditio..., IATSS Research, https://doi.org/10.1016/j.iatssr.2018.11.002

2

A. Ghasemzadeh, M.M. Ahmed / IATSS Research xxx (xxxx) xxx

Characteristics of work zone crashes were considered in previous studies. Most of work zone injury severity studies investigated singlevehicle crashes or the most severe crashes in multiple-vehicle crashes [6–13]. Previous studies indicated that 50% to 75% of work zone crashes involved multiple vehicles and stopping or slowing was the primary pre-crash driving task [6,12,13]. In addition, these studies showed that the severity of work zone crashes increases at locations with higher speed limits [7,12], when the lighting is poor [7–9,12,13], and when the driver is under the influence of alcohol, etc. [12,13]. In, addition, vehicles equipped with airbags, driving in clear weather conditions, and the use of seatbelts are associated with less severe crashes in work zones [5,9,12,13]. Moreover, duration of construction, traffic volume and types of work zones were found to have direct effects on injury and non-injury crashes in work zones [4]. Configurations of work zones may have an impact on the severity of crashes [14–16]. While the reduction of the length of transition area may help drivers to complete merging manoeuvers more quickly, the short transition distance leading to work zones showed an increase in the risk of rear-end crashes [15,16]. Analyzing where crashes and fatal crashes are more prone to occur at work zones, activity areas are the most hazardous locations with the highest number of fatal crashes. The termination areas, where traffic resumes normal operations, were found to be the safest zones in terms of number of crashes. Moreover, as traffic move from the transition area to the work area, the proportions of rear-end and same-direction sideswipe crashes decrease, and the proportions of fixed-object, off-road, and angle crashes increase, with rear-end crashes still being predominant [14].

The literature shows a variation of crash risk estimates; however, a general trend can be found that adverse weather and road conditions can easily elevate the risk of crashes. There is a lack of studies focusing on the impact of weather conditions on work zone injury severity; therefore, this paper utilized data from the second Strategic Highway Research Program (SHRP2) Roadway Information Datasets (RID) to shed some light on different factors affecting work zone crash severity considering different weather conditions. 3. Data The comprehensive SHRP2 Roadway Information Database (RID) consists of roadway data collected from the mobile data collection project, government, public, and private parties, in addition to supplemental data that was utilized in this study [29,30]. Mobile data collection project focuses mostly on the roadway characteristics of the most traveled routes during the three years SHRP2 Naturalistic Driving Study (NDS) project. In addition, supplemental data refers to the collected data from external sources such as police crash reports and traffic regulations in the SHRP2 six states (Florida, Indiana, New York, North Carolina, Pennsylvania, and Washington), as well as aerial imagery, etc. This study specifically utilized crash data in Washington State for five years (2009–2013) available in the RID supplemental dataset. Weather information has been extracted from the crash reports. A total of 20,294 work zone related crashes in different spatial, temporal, and environmental conditions were considered in this study. It is worth mentioning that the RID is a flexible database that is accessible via Geographic Information System (GIS) interface.

2.2. Weather conditions impact on crashes

4. Descriptive analysis

Inclement weather events such as fog, snow, ground blizzards, slush, rain, and strong wind affect roadways by impacting pavement conditions, vehicle performances, visibility, and driver behavior [17–22]. Sudden reduction in visibility is one of the negative effects of adverse weather conditions. This reduction in visibility can significantly impact roadways and increase the risk of crashes. A previous study by Federal Highway Administration (FHWA) utilizing National Highway Traffic Safety Administration (NHTSA) data found that 24% of crashes were weather related. The results from some studies revealed that the likelihood of being involved in crashes can be increased by 100% or more due to visibility reduction during rainfall [23,24], while others found more moderate, but still statistically significant, increases [25,26]. It was also found that the reduction in visibility caused by adverse weather conditions can also increase the severity of crashes. A study by NHTSA's Fatality Analysis Reporting System (FARS) showed that adverse weather resulted in 31,514 fatal crashes between 2000 and 2007. Another study reported that locations with a high number of rainy days per month, maximum rainfall, and maximum snowfall have higher crash rates [27]. Ahmed et al. (2012) reported that an additional one-inch increase in precipitation elevated the risk of a crash by 169% [28].

Eight years (2006–2013) of crashes were extracted to examine the frequency and trends of clear and weather-related crashes at work zones in Washington State. However, only five years of data (2009–2013) were considered for crash severity modeling in this study. Fig. 1 illustrates the yearly percentages of weather-related crashes compared to the total crashes that occurred at work zones. The great recession had a significant effect on transportation in general, and specifically on traffic safety. The vehicle miles traveled (VMT) was reduced from 3031 billion miles in 2007 to 2957 billion miles in 2009. This reduction in VMT significantly reduced the total number of fatal from 37,435 in 2007 to 30,862 in 2009 [31]. Maheshri and Winston demonstrated that not only was there a downward trend in VMT during the recession, but also the riskier drivers' VMT decreased while the share of safer drivers' VMT increased [32]. Work zone weather-related crashes were not an exception, and they fluctuated in different years. The drop in 2009 could be due to the reduction in road construction/maintenance projects due to the great recession between December 2007 and June 2009 [33–35]. The distribution of work zone weather-related crashes was also considered in this study. Crashes were mostly found to be located in and around King, Snohomish and Thurston counties. Fig. 2 uses a heat-

Fig. 1. The trend of Work Zone Weather-Related Crashes between 2006 and 2013.



3

Fig. 2. Washington State Work Zone Weather-Related Crashes Heat-map (2009 to 2013).

map to show the work zone weather-related crash densities in Washington State. Note: Red shows higher densities with lighter for lower crash densities. Different categories of explanatory variables were considered in the empirical analysis to account for different contributing factors including roadway, traffic, temporal, driver, and crash characteristics. Table 1 indicates variable description and the frequency distribution of explanatory variables. Roadway characteristics were considered in this study using several categorical variables, including functional class, geometric design factors, and lighting conditions. As can be seen in Table 1, each roadway was classified based on its functional class into one of the following functional class types: “principal urban arterial”, “urban freeway/expressway”, “minor urban arterial”, “urban interstate”, “rural principal arterial”, “rural interstate” and “other”. In addition, other geometric design characteristics including whether the roadway was curved or not were also considered in this study. Roadway lighting conditions were also assessed and considered as one of the roadway characteristics. Traffic characteristics included speed limit at work zone areas. The speed effect was considered using a categorical variable with five levels, as shown in Table 1. The effect of temporal characteristics was captured using two categorical variables: “time of day” and “peak hours”. Time of day impact was considered using three time categories, including daytime (6 a.m. to 6 p.m.), evening (6 p.m. to 12 am) and late night (12 a.m. to 6 a.m.) [36]. In addition to the time of day, another indicator was defined to capture the effect of peak hours. This indicator represented whether the work zone crash occurred during peak hours or not. Peak hours were defined as 7 to 10 am and 4 to 7 pm, based on a previous study [36]. Crash characteristics included number of vehicles involved in a crash, collision types and whether the crash related to the intersection or not. Finally, driver characteristics included age, gender, and whether the driver was under the influence or not. Other factors such as work zone type and surface conditions were also considered and are shown in Table 1.

severity levels, and the explanatory variables are the factors that have a significant influence on the crash severity levels. The crash severity is ordinal in nature which defined as three levels including Property Damage Only (PDO), injury, and fatal in this study.

Where y∗iis the latent measure of crash injury severity measuring the crash severity of ith accident; xi is a vector of observed explanatory variables. β is a vector of parameters to be estimated; εi is a random error term which assumed to follow a normal distribution (N) with mean = 0 and variance = 1. Finally, i can take values from 1 to n. The observed and coded discrete crash injury severity variable y is determined from the model as follows.

5. Methodology

6.1. Variable description

The crash severity model in this study was developed to investigate the factors affecting the severity of work zones crashes considering adverse weather conditions. The dependent variable in the model is crash

Table 1 shows the selected variables for developing the work zone crash severity model. The dependent variable is crash severity which has three levels; property damage only crashes (PDO), injury crashes,

5.1. Ordered probit regression Ordered probit model (OPM) could be used in case of dealing with more than two outcomes of an ordinal dependent variable. This model is considered as a generalization of the probit model. An ordered probit model is as follows [37–39]: yi ¼ α þ xi β þ εi ;

εi ∼Nð0; 1Þ; ∀i ¼ 1; …; n

yi ¼ m If m−1≤y bμm from m ¼ 1 to M

ð1Þ

ð2Þ

Where the threshold values μ are unknown parameters to be estimated. The extreme categories, 1 and M, are defined by open-ended intervals with μ0 = − ∞ and μm = + ∞. The discrete measure of severity is ordinal in nature. Both ordered probit and logit models have been utilized in crash-severity studies. The ordered probit model is favored over the logit model because of the assumption that the error term is normally distributed and it is more likely to be valid while for the logit model, the errors are assumed to follow the standard logistic distribution [37,39]. 6. Model


4


Table 1 Variables descriptions. Variable

Description

Type

Definition

Data (%)

Assigned code

Crash severity level

Crash severity level

Ordinal

Speed

Maximum posted speed

Categorical

Gender

Driver's gender

Categorical

Age

Driver's age

Categorical

Vehicle Involved

Number of motor vehicles involved

Categorical

Collision Type

Crash type

Categorical

Driving Under the Influence Roadway Characteristics

If the driver was under the influence of alcohol or drugs

Binary

If there is a curve at the crash location

Binary

Lighting Condition

Lighting conditions at the crash location

Binary

Time of Day

Crash Time

Categorical

Peak Hours

Whether the crash occurred during peak hour or not

Binary

Surface Conditions

Surface conditions at the crash location

Binary

Functional Classification

Roadway functional classification

Categorical

PDO Injury Fatal b35 35–40 45–50 55–60 65–70 Male Female Youngb25 Middle(25–64) OldN64 Single Multiple Rear-end Angle Sideswipe Head on Other No Yes No Yes Day-Light Dusk & Dawn Dark Street Lights On Dark No Street Light & Street Light Off Day (6 a.m. to 6 p.m.) Evening (6 p.m. to 12 am) Late Night (12 a.m. to 6 a.m.) Peak Off-peak Dry Wet Urban Principal Arterial Urban Freeway/Expressway Urban Minor Arterial Urban Interstate Rural Principal Arterial Rural Interstate Other yes No Construction Maintenance Utility Other (Incident Management Operations, etc.) Flashing Amber/Flashing Red Officer/Flagger Signals/RR Signal Stop Sign/Yield Other Traffic Control No Traffic Control Clear Rain Snow Fog Hail Severe Crosswind Blowing Snow

62.55 37.14 0.32 25.1 21.92 10.51 39.01 3.47 58.95 41.05 32.27 60.01 7.73 90.90 9.10 56.43 12.51 13.56 0.42 17.08 92.55 7.45 86.84 13.16 70.21 3.24 18.38 8.17 74.73 18.35 6.92 58.85 41.15 76.04 23.96 11.31 13.29 1.93 31.66 2.31 6.49 33.01 25.48 74.52 71.54 14.22 4.28 9.96

1 2 3 b35 35–40 45–50 55–60 65–70 1 2 1 2 3 1 2 1 2 3 4 5 1 2 1 2 1 2 3 4 1 2 3 1 2 1 2 1 2 3 4 5 6 7 1 2 1 2 3 4

0.4 7.45 12.97 4.25 18.77 56.17 83.52 15.11 0.57 0.64 0.05 0.04 0.07

1 2 3 4 5 6 1 2 3 4 5 6 7

Intersection Related

Whether the crash related to the intersection or not

Binary

Work Zone Type

Work zone type at the crash location

Categorical

Traffic Control

Presence of traffic control device at work zone crash location

Categorical

Weather Conditions

Weather conditions at the time of crash occurrence

Categorical

and fatal crashes. Explanatory variables can be considered as driver behavior, roadway conditions, environmental conditions, and crash characteristics. 6.2. Model evaluation Results of model estimation are summarized in Table 2. It is worth mentioning that Multi-collinearity was assessed by calculating the

Reference level

Ref ≤35

* *

*

* * * *

* * *

* * *

*

* *

Variance Inflation Factor (VIF) for each predictor. This factor evaluates how much the variance of an estimated regression coefficient increases if the predictors are correlated. Variance Inflation Factor (VIF) between 5 and 10 shows a high correlation between predictors and VIF N10 indicates that the regression coefficients are poorly estimated due to multicollinearity [40]. The variables included in the final model showed a VIF ranged between 1.01and 1.86, excluding any concerning multicollinearity.



5

Table 2 Estimation of the ordered probit model for work zone crashes. Variable

Intercept Time of Day Peak Hours Functional Classification

Collision Type

Work Zone Type Maximum posted speed

Traffic Control

Lighting Condition

Age Gender Driving Under the Influence Vehicle Involved Intersection Related Weather Cond.

Interactions Intersection Related & Lighting Condition Lighting Conditions & Roadway Characteristics Work Zone Type & Lighting Conditions

Work Zone Type & Traffic Control

Peak Hours & Weather cond. Vehicle Involved & Weather cond. Functional Classification & Weather cond. Driving Under the Influence & Weather cond. Collision Type & Weather cond. Weather cond. & Intersection Related Speed & Weather cond. Lighting Condition & Weather cond.

Gender & Weather cond. Thresholds: μ 0 ¼ 0:00 μ 1 ¼ 1:42 μ 2 ¼ 3:92

Description

Coefficient

Std. error

t-value

P-value

Marginal effect Injury

Fatal

Evening (6 p.m. to 12 am) Peak Urban Principal Arterial Urban Freeway/Expressway Urban Minor Arterial Urban Interstate Rural Interstate Other Rear-end Angle Sideswipe Head-on Maintenance Utility 45 50 60 65 70 Flashing Amber/Flashing Red Officer/Flagger Stop Sign/Yield Other Traffic Control Dusk & Dawn Dark Street Lights On Dark No Street Light & Street Light Off Middle(25–64) Female Yes Multiple Related Rain Snow Fog

−0.211 0.109 −0.036 −0.327 −0.282 −0.361 −0.372 −0.309 −0.284 0.521 0.234 −0.221 1.277 −0.104 −0.347 0.160 0.153 0.231 0.436 0.280 0.880 0.107 −0.097 0.157 0.150 −0.082 −0.116 0.060 0.101 0.772 −0.399 0.067 −0.573 −0.337 −0.874

0.085 0.030 0.022 0.066 0.065 0.094 0.065 0.074 0.064 0.039 0.046 0.048 0.140 0.045 0.064 0.045 0.047 0.033 0.135 0.065 0.205 0.038 0.055 0.078 0.070 0.030 0.039 0.020 0.019 0.038 0.050 0.033 0.101 0.135 0.262

– −0.039 0.013 0.115 0.100 0.127 0.131 0.109 0.100 −0.184 −0.083 0.078 −0.451 0.037 0.122 −0.056 −0.054 −0.081 −0.154 −0.099 −0.310 −0.038 0.034 −0.055 −0.053 0.029 0.041 −0.021 −0.035 −0.272 0.141 −0.024 0.202 0.119 0.308

– 0.038 −0.012 −0.113 −0.097 −0.124 −0.128 −0.106 −0.098 0.179 0.081 −0.076 0.439 −0.036 −0.119 0.055 0.053 0.079 0.150 0.096 0.303 0.037 −0.033 0.054 0.051 −0.028 −0.040 0.021 0.035 0.266 −0.137 0.023 −0.197 −0.116 −0.300

– 0.001 0.000 −0.003 −0.003 −0.003 −0.003 −0.003 −0.003 0.005 0.002 −0.002 0.011 −0.001 −0.003 0.001 0.001 0.002 0.004 0.002 0.008 0.001 −0.001 0.001 0.001 −0.001 −0.001 0.001 0.001 0.007 −0.004 0.001 −0.005 −0.003 −0.008

Intersection Related & Dusk & Dawn Intersection Related & Dark Street Lights On Dusk & Dawn & Curve Dark No Street Light & Street Light Off & Curve Maintenance & Dusk & Dawn Utility & Dark Street Lights On Utility & Dark No Street Light & Street Light Off Construction & Flashing Amber/Flashing Red Construction & Signals/RR Signal Construction & Other Traffic Control Utility & Stop Sign/Yield Peak & Rain Multiple & Rain Urban Minor Arterial & Rain DUI & Rain Rear-end & Rain Sideswipe & Rain Rain & Intersection Related Fog & Intersection Related 45 & Rain Dusk & Dawn & Rain Dark Street Lights On & Fog Dusk & Dawn & Fog Female & Fog

−0.369 0.137 −0.269 0.368 0.443 0.410 0.579 −0.941 0.173 −0.145 0.371 0.131 0.449 0.671 0.194 −0.214 −0.222 0.238 0.601 0.185 −0.427 0.944 −0.975 0.530

0.128 −2.88 0.004 0.130 0.054 2.54 0.0112 −0.048 0.158 −1.7 0.0883 0.095 0.087 4.23 b0.0001 −0.130 0.172 2.58 0.01 −0.156 0.137 3 0.0027 −0.144 0.185 3.13 0.0018 −0.204 0.287 −3.28 0.001 0.332 0.064 2.7 0.0069 −0.061 0.083 −1.74 0.0811 0.051 0.154 2.42 0.0157 −0.131 0.053 2.46 0.0141 -0.046 0.099 4.53 b0.0001 −0.159 0.174 3.86 0.0001 −0.237 0.094 2.06 0.0396 −0.069 0.069 −3.1 0.0019 0.076 0.105 −2.1 0.0356 0.078 0.065 3.67 0.0002 −0.084 0.266 2.26 0.024 −0.212 0.109 1.7 0.0887 −0.065 0.145 −2.94 0.0033 0.151 0.255 3.7 0.0002 −0.333 0.511 −1.91 0.0564 0.344 0.249 2.13 0.0335 −0.187 Log-likelihood at convergence: −12,897 Number of observations: 20,294

−0.127 0.047 −0.093 0.126 0.152 0.141 0.199 −0.324 0.060 −0.050 0.128 0.045 0.155 0.231 0.067 −0.074 −0.076 0.082 0.207 0.064 −0.147 0.325 −0.335 0.182

−0.003 0.001 −0.002 0.003 0.004 0.004 0.005 −0.008 0.002 −0.001 0.003 0.001 0.004 0.006 0.002 −0.002 −0.002 0.002 0.005 0.002 −0.004 0.008 −0.009 0.005

−2.47 3.6 −1.69 −4.95 −4.36 −3.84 −5.73 −4.19 −4.47 13.34 5.04 −4.64 9.14 −2.34 −5.43 3.57 3.26 6.97 3.22 4.28 4.29 2.83 −1.76 2.01 2.12 −2.7 −2.98 2.99 5.38 20.32 −8.04 2.07 −5.67 −2.5 −3.34

0.0136 0.0003 0.0912 b0.0001 b0.0001 0.0001 b0.0001 b0.0001 b0.0001 b0.0001 b0.0001 b0.0001 b0.0001 0.0195 b0.0001 0.0004 0.0011 b0.0001 0.0013 b0.0001 b0.0001 0.0047 0.0789 0.0444 0.0336 0.0069 0.0029 0.0028 b0.0001 b0.0001 b0.0001 0.0385 b0.0001 0.0125 0.0008

PDO

7. Discussion of key factors

7.1. Roadway characteristics

Table 2 presents the estimation results of the OPM model. Interpretation of the key factors is discussed below:

Analyzing the functional classification showed that principal urban arterial decreased the likelihood of severe crashes according to the work zone crash severity OPM results. To be specific, the OPM model


6


indicates that drivers who were involved in crashes on urban principal arterials were 1.15% less likely to be involved in severe work zone crashes in comparison with those on rural principal arterials (reference level). A previous study by Osman et al. showed that severe crashes are more likely to occur on rural principal arterials in comparison with rural minor arterials, collectors, and local systems [36]. This study revealed that rural principal arterials are associated with higher risks of severe crashes even in comparison with urban principal arterials. Other functional class categories were also statistically significant factors in the developed ordered probit work zone crash severity model. In particular, the OPM indicated that, on average, rural principal arterials have higher crash severity risk relative to urban principal arterials, urban freeways/ expressways, minor urban arterials, urban interstates, rural interstates and other roadways. Lighting conditions was a significant factor affecting work zone crash severity. Drivers who were involved in work zone crashes during dusk and dawn were more likely to be involved in severe crashes. Driver's sleepiness, inattention and lack of alertness, which result from sleep deprivation at night might increase the severity of crashes [41,42]. As visibility deteriorates and the complexity of work zones may lead to shorter time available to drivers to make evasive maneuvers avoiding collision with other vehicles, as well as nearby objects, thus increasing the chance of accidents. Moreover, during daylight and good visibility, the potential of secondary crashes might be less [43].

Crashes within intersection influence areas had a significant effect on the work zone crash severity model. More clearly, intersectionrelated crashes were more likely to have a higher severity as compared to non-intersection related crashes. This finding is supported by the literature as a previous study showed that N30% of work zone multivehicle fatal and injury crashes occurred within intersection influence areas [47].

7.2. Traffic characteristics

The interactions between several key factors were assessed to better understand the in-depth effect of contributing factors on work zone crash severity using the OPM model. The interactions between lighting conditions, gender, intersection-related crashes, roadway characteristics, weather conditions, work zone type, and traffic control devices at work zones were considered for further analysis. The interaction between lighting conditions and roadway characteristics revealed that drivers were more involved in severe crashes at curve segments without street light or street light off. Previous studies showed that it is more challenging to negotiate a sharper curve [53,54]. This study clearly showed this could be even more problematic in the lack of daylight. In addition, the interaction between lighting conditions and work zone type revealed that drivers had more severe crashes at work zones in lack of daylight. Also, analyzing the interaction between work zone type and traffic control devices showed that drivers were less likely to be involved in severe crashes at construction work zones equipped with amber/ flashing red lights. Although analyzing the impact of adverse weather conditions on severity of work zone crashes showed that drivers have less severe crashes in adverse weather conditions at work zones, which is consistent with previous studies [5,9,55]; the interaction between weather conditions and number of vehicles involved in crashes revealed that drivers were involved in crashes with higher severity in multiple vehicles involved crashes and in rainy weather conditions. In addition, an interesting finding showed that peak hours' crashes were more severe in rainy weather conditions, which could be because of jerky driving and slippery surface conditions [56–58]. The results also revealed the negative effect of driving under the influence and the reduced visibility caused by rainy weather conditions in more severe crashes. Another interesting finding is about the interaction between intersection-related crashes and adverse weather conditions, which showed that drivers were more involved in severe crashes within intersection influence area and rainy or foggy weather conditions.

The modeling results showed that, on average, higher speed limits have higher risk proclivity. In fact, higher speed dramatically increases the severity of work zone crashes. Driving over the speed limit could be risky because of the lack of sufficient time for a suitable response to control and handle unexpected situations. Moreover, the positive coefficients of speed limits of 45, 50, 60, 65 and 70 mph were found to be associated with a higher likelihood of more severe crashes based on the OPM crash severity model. This result is not surprising, and it is consistent with previous studies on work zone crash severity [12,36,44]. 7.3. Temporal characteristics The results of the developed crash severity model also showed that drivers who were traveling during the evening were more likely to be involved in work zone severe crashes in comparison with late night crashes. A Previous study showed that traveling during evening times might be associated with higher speeds due to lower traffic congestion [36]. On the other hand, traveling during peak hours was a significant factor in the work zone crash severity model. To be specific, drivers who were involved in work zone crashes during peak hours were less likely to be involved in crashes with higher severity in comparison with non-peak-hour crashes. This finding is consistent with previous studies as traffic congestion during peak hours leads to slower operating speeds, hence reducing the severity of crashes [36,45,46]. 7.4. Crash characteristics Number of vehicles involved in a crash turned out to be a significant factor in the work zone crash severity model. Interestingly, results showed that drivers who were involved in multi-vehicle crashes were less likely to be involved in severe crashes. Types of crashes were found to be a significant factor in the developed crash severity model. Analyzing the drivers who were involved in sideswipe crashes showed that those drivers were less likely to be involved in severe crashes. In contrast, the results showed that the likelihood of getting injured for drivers who were involved in rear-end, angle, and head-on crashes was higher. This finding is not surprising as head-on crashes can cause serious accidents and devastating injuries.

7.5. Driver characteristics As supported by previous studies, driving under the influence (DUI) was another significant factor in work zone crash severity model. Drivers involved in DUI were more likely to be involved in severe crashes. DUI adversely affects driving ability, cognition, and judgment, increases perception and reaction time, impairs hearing and vision, and reduces eye/hand/foot coordination. This could be worsened by the complexity of work zones [48–51]. Interestingly, female drivers were more likely to be involved in work zone crashes. This finding is in line with recent studies indicating that female drivers have a greater risk of being involved in injury work zone crashes compared to male drivers [9,52]. 7.6. In-depth analysis of work zone crashes in different weather conditions considering interaction between variables

8. Conclusions There are few studies that strive to gain a good understanding of the effect of weather on the severity of work zone crashes. This study aims to fill the gap in the literature by identifying the main contributing factors affecting the severity of work zone crashes in different weather conditions. Descriptive statistics and ordered probit model were utilized



to better understand the characteristics of work zone crashes in different weather conditions. A wide variety of explanatory variables representing roadway characteristics, temporal effect, traffic features, driver and crash characteristics were considered in the model estimation process. Crash severity is ordinal in nature and ordered probit model that can explicitly consider the ordinal nature of crash severity was utilized to better understand factors affecting the severity of work zone crashes. Factors including lighting and weather conditions are among the most important variables came out to be significant in the developed work zone crash severity model. In fact, drivers who were involved in work zone crashes during dusk and dawn were more likely to be involved in severe crashes, which could be due to drivers' sleepiness, inattention and lack of alertness. The developed ordered probit model also revealed some interesting findings among those the negative effect of adverse weather conditions on the severity of crashes that occurred during peak hours and crashes that occurred in intersection influence area. This study shed some light on how to improve work zone safety by understanding factors affecting the severity of work zone crashes in different roadway, weather, and other conditions. Based on the findings of this study, corresponding countermeasures and work zone traffic control policies can be developed, as discussed in the recommendations section.

7

10. Limitations & future studies One of the limitations of this study was the lack of information related to number of work zones in each year. Even though normalizing the number of crashes in each year was not the focus of this study, exposure measure can be considered to better understand the trend of work zone crashes in different weather conditions. Even though the information provided in different sources of crash data including the police crash report in different years share some similar features, the estimated parameters and model specifications might not be temporally stable. Temporal variability of different contributing factors can be considered in future studies. For future studies, it is recommended to use mixed effects models. Considering the fact that multiple crashes may be generated from the same location and crashes occurring at the same site are likely to share similar patterns (i.e., crashes within each site may be correlated, resulting in within-site correlation), mixed effects models would be more appropriate. Factors including but not limited to in-vehicle technologies and distraction provided by these technologies, driver's responses to vehicle safety technologies and macroeconomic conditions may play a crucial role in crash-severity models and can be considered in future studies. Naturalistic driving data provided by the SHRP2 project can provide enrich information regarding driver's responses to vehicle safety technologies and can be used in future studies.

9. Recommendations Acknowledgements This study demonstrated that safety countermeasures could be considered in work zone planning and installation stages to reduce the severity of crashes. Transportation agencies and contractors should pay more attention to the effect of weather conditions in different stages of work zone projects. Among the factors affecting work zone crashes severity considered in this study, lighting conditions was one of the significant factors affecting the severity of work zone crashes. It would be recommended to transportation agencies and contractors to invest in more adequate lighting equipment for nighttime work (since nighttime work is the only option in most urban areas). While the 2009 Manual on Uniform Traffic Control Devices (MUTCD) and other DOT's manuals provide limited guidance to address lighting needs tailored to different tasks performed in a work zone, they do not provide specifications of the appropriate lighting type, quantity, or configurations of lighting systems suitable for specific work zone, roadway and traffic conditions. In 2013, the American Traffic Safety Services Association developed the first and the most comprehensive nighttime lighting guidelines for work zones [59]. The manual provides a simple procedure for designing a nighttime lighting system for work zones that can be easily adopted by engineers, designers, and contractors without prior experience in illumination. However, more studies needed to clarify and characterize driver behavior in different lighting, visibility and weather conditions. Automated speed enforcement and posting appropriate speed limits for the condition may be other effective strategies to reduce speeding in work zones. According to the NHTSA, 28% to 33% of motor vehicle fatalities in work zones are speed related [60]. This study found that higher speed limits are significant contributing factors to crash injury severity. Improving the Temporary Traffic Control (TTC) including but not limited to the portable Variable Speed Limit (VSL), to adjust speed limits during adverse weather conditions can be beneficial in this regard. Portable Changeable Message Signs (PCMS) are also highly recommended to warn drivers about work zones to reduce the severity of work zone crashes. The positive effects of PCMS have been discussed in many studies, including but not limited to reduce the severity and frequency of rear-end and angle crashes [61,62]. These two types of crashes were positively associated with the severity of work zone crashes in this study.

This work was conducted under the second Strategic Highway Research Program (SHRP2), which is administrated by the Transportation Research Board (TRB) of the National Academies of Sciences, Engineering, and Medicine, and it was sponsored by the Federal Highway Administration (FHWA) in cooperation with American Association of State Highway and Transportation Officials (AASHTO) References [1] S.D. Schrock, G.L. Ullman, A.S. Cothron, E. Kraus, A.P. Voigt, An Analysis of Fatal Work Zone Crashes in Texas, 7, 2004 1–86http://dmkd.cs.wayne.edu/Compendium/Compendium_Files/12/12-2901.pdf. [2] E. Chen, A.P. Tarko, Modeling safety of highway work zones with random parameters and random effects models, Anal. Methods Accid. Res. 1 (2014) 86–95. [3] Y. Li, Y. Bai, Highway work zone risk factors and their impact on crash severity, J. Transp. Eng. 135 (2009) 694–701. [4] A.J. Khattak, A.J. Khattak, F.M. Council, Effects of work zone presence on injury and non-injury crashes, Accid. Anal. Prev. 34 (2002) 19–29, https://doi.org/10.1016/ S0001-4575(00)00099-3. [5] S.R. Akepati, S. Dissanayake, Characteristics and contributory factors of work zone crashes, Transp. Res. Board 90th Annu. Meet, 2011. [6] G.S. Sorock, T.A. Ranney, M.R. Lehto, Motor vehicle crashes in roadway construction workzones: an analysis using narrative text from insurance claims, Accid. Anal. Prev. 28 (1996) 131–138, https://doi.org/10.1016/0001-4575(95)00055-0. [7] A. Khattak, F. Targa, Injury severity and total harm in truck-involved work zone crashes, Transp. Res. Rec. J. Transp. Res. Board. (2004) 106–116. [8] Y. Li, Y. Bai, Fatal and Injury Crash Characteristics in Highway Work zones, Transp. Res. Board 87th Annu. Meet. (2008) 471http://www.workzonesafety.org/files/documents/database_documents/Publication9950.pdf. [9] J. Weng, Q. Meng, Analysis of driver casualty risk for different work zone types, Accid. Anal. Prev. 43 (2011) 1811–1817. [10] H. Yang, K. Ozbay, O. Ozturk, M. Yildirimoglu, Modeling work zone crash frequency by quantifying measurement errors in work zone length, Accid. Anal. Prev. 55 (2013) 192–201, https://doi.org/10.1016/j.aap.2013.02.031. [11] J. Liu, A. Khattak, M. Zhang, What role do precrash driver actions play in work zone crashes? Application of hierarchical models to crash data, Transp. Res. Rec. J. Transp. Res. Board. (2016) 1–11. [12] A. Ghasemzadeh, M.M. Ahmed, A Probit-Decision Tree Approach to Analyze Effects of adverse Weather Conditions on Work Zone Crash Severity using Second Strategic Highway Research Program Roadway Information Dataset, 96th Transportation Research Board annual meeting. Washington, D.C 2017, pp. 1–17. [13] A. Ghasemzadeh, M.M. Ahmed, A Tree-based Ordered Probit Approach to Identify Factors Affecting Work Zone Weather-Related Crashes Severity in North Carolina using the Highway Safety Information System Dataset, 96th Transp. Res. Board Annu. Meet, Washington, D.C, 2017.


8


[14] N. Garber, M. Zhao, Distribution and Characteristics of Crashes at different Work Zone Locations in Virginia, Transp. Res. Rec. 1794 (2002) 19–25, https://doi.org/ 10.3141/1794-03. [15] J. Weng, Q. Meng, X. Yan, Analysis of work zone rear-end crash risk for different vehicle-following patterns, Accid. Anal. Prev. 72 (2014) 449–457, https://doi.org/ 10.1016/j.aap.2014.08.003. [16] J. Weng, S. Xue, Y. Yang, X. Yan, X. Qu, In-depth analysis of drivers' merging behavior and rear-end crash risks in work zone merging areas, Accid. Anal. Prev. 77 (2015) 51–61, https://doi.org/10.1016/j.aap.2015.02.002. [17] M.M. Ahmed, R.K. Young, A. Ghasemzadeh, B. Hammit, A. Elhashemi, N. Khan, A. Das, H. Eldeeb, Implementation of SHRP2 Results within the Wyoming Connected Vehicle Variable Speed Limit System: Phase 2 Early Findings Report and Phase 3 Proposal, 2017. [18] A. Ghasemzadeh, B. Hammit, M. Ahmed, R. Young, Parametric Ordinal Logistic Regression and Non-Parametric Decision tree Approaches for Assessing the Impact of Weather Conditions on driver speed selection using Naturalistic Driving Data, Transp. Res. Rec. J. Transp. Res. Board. (2018) https://doi.org/10.1177/ 0361198118758035 0361198118758035. [19] B.E. Hammit, A. Ghasemzadeh, R.M. James, M.M. Ahmed, R.K. Young, Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database, Transp. Res. Part F Traffic Psychol. Behav. 59 (2018) 244–259. [20] A. Ghasemzadeh, B. Hammit, M.M. Ahmed, H. Eldeeb, Complementary Methodologies to Identify Weather Conditions in Naturalistic Driving Study Trips: Lessons Learned from the SHRP2 Naturalistic Driving Study & Roadway Information Database, 97th Transportation Research Board Annual Meeting, Washington, D.C, 2018. [21] N.M. Khan, A. Ghasemzadeh, M.M. Ahmed, Investigating the Impact of fog on Freeway speed selection using the SHRP2 Naturalistic Driving Study Data, Transp. Res. Rec. J. Transp. Res. Board. (2018) https://doi.org/10.1177/0361198118774748 0361198118774748. [22] M.M. Ahmed, A. Ghasemzadeh, H. Eldeeb, S. Gaweesh, J. Clapp, K. Ksaibati, R. Young, Driver Performance and Behavior in Adverse Weather Conditions: An Investigation Using the SHRP2 Naturalistic Driving Study Data—Phase 1 [23] National Transportation Safety Board, Fatal Highway Accidents on Wet Pavement— the Magnitude, Location, and Characteristics, Natl. Tech. Inf. Serv. Springf. Vol. HTSBHSS (1980) 1–46. [24] H. Brodsky, A.S. Hakkert, Risk of a road accident in rainy weather, Accid. Anal. Prev. 20 (1988) 161–176. [25] J. Andrey, R. Olley, The relationship between weather and road safety: past and future research directions, Climatol. Bull. 24 (1990) 123–127. [26] M.P. Andreescu, D.B. Frost, Weather and traffic accidents in Montreal, Canada, Clim. Res. 9 (1998) 225–230. [27] V. Shankar, F. Mannering, W. Barfield, Effect of roadway geometrics and environmental factors on rural freeway accident frequencies, Accid. Anal. Prev. 27 (1995) 371–389. [28] M. Ahmed, M. Abdel-Aty, R. Yu, Assessment of interaction of crash occurrence, mountainous freeway geometry, real-time weather, and traffic data, Transp. Res. Rec. J. Transp. Res. Board. (2012) 51–59. [29] S.K. Omar Smadi, Neal Hawkins, Zachary Hans, Basak Aldemir Bektas, S.H. Inya Nlenanya, Reginald Souleyrette, SHRP 2 Report: Naturalistic Driving Study, Development of the Roadway Information Database, Washington, D.C, 2015http:// onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-S04A-RW-1.pdf. [30] O. Smadi, N. Hawkins, Z. Hans, B.A. Bektas, S. Knickerbocker, I. Nlenanya, R. Souleyrette, S. Hallmark, Naturalistic Driving Study: Development of the Roadway Information Database, SHRP 2 Report S2-S04A-RW-1, 2014. [31] FARS, Fatality Analysis Reporting System, https://www-fars.nhtsa.dot.gov/Main/ index.aspx 2014. [32] V. Maheshri, C. Winston, Did the Great recession keep bad drivers off the road? J. Risk Uncertain. 52 (2016) 255–280. [33] A. Behnood, F.L. Mannering, The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: some empirical evidence, Anal. Methods Accid. Res. 8 (2015) 7–32. [34] A. Behnood, F.L. Mannering, An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models, Anal. Methods Accid. Res. 12 (2016) 1–17. [35] D.C. Brown, Models for Ordered and Unordered Categorical Variables, Popul. Res. Center- Univ. Texas Austin. (2016) https://www.utexas.edu/cola/prc/_files/cs/Multinomial_Ordinal_Models.pdf (11/19/2018).

[36] M. Osman, R. Paleti, S. Mishra, M.M. Golias, Analysis of injury severity of large truck crashes in work zones, Accid. Anal. Prev. 97 (2016) 261–273. [37] V.K. Borooah, Logit and Probit: Ordered and Multinomial Models, Sage, 2002. [38] Q. Wang, Study on Crash Characteristics and Injury Severity at Roadway Work Zones, 2009. [39] A. Agresti, An Introduction to Categorical Data Analysis, Second Edition John Wiley & Sons, Inc., Publication, 2007 https://doi.org/10.1198/jasa.2008.s251. [40] D.W. Marquaridt, Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation, Technometrics 12 (1970) 591–612. [41] J. Holguín-Veras, K. Ozbay, R. Baker, D. Sackey, A. Medina, S. Hussain, Toward a comprehensive policy of nighttime construction work, Transp. Res. Rec. J. Transp. Res. Board. (2003) 117–124. [42] G.L. Ullman, T.J. Lomax, T. Scriba, A Primer on Work Zone Safety and Mobility Performance Measurement, 2011. [43] C. Zhan, A. Gan, M. Hadi, Identifying secondary crashes and their contributing factors, Transp. Res. Rec. 2102 (2009) 68–75. [44] Z. Wang, J. Lu, Q. Wang, L. Lu, Z. Zhang, Modeling Injury Severity in work zones using Ordered PROBIT Regression, ICCTP 2010 Integr. Transp. Syst. Green, Intelligent, Reliab 2010, pp. 1058–1067. [45] L.-Y. Chang, J.-T. Chien, Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model, Saf. Sci. 51 (2013) 17–22. [46] M. Islam, S. Hernandez, Large truck–involved crashes: exploratory injury severity analysis, J. Transp. Eng. 139 (2013) 596–604. [47] Y. Li, Y. Bai, Comparison of characteristics between fatal and injury accidents in the highway construction zones, Saf. Sci. 46 (2008) 646–660. [48] W. Dejong, R. Hingson, Strategies to reduce driving under the influence of alcohol, Annu. Rev. Public Health 19 (1998) 359–378. [49] E. Chamberlain, R. Solomon, The case for a 0.05% criminal law blood alcohol concentration limit for driving, Inj. Prev. 8 (2002). [50] M.D. Keall, W.J. Frith, T.L. Patterson, The influence of alcohol, age and number of passengers on the night-time risk of driver fatal injury in New Zealand, Accid. Anal. Prev. 36 (2004) 49–61. [51] X. Zhao, X. Zhang, J. Rong, Study of the Effects of Alcohol on Drivers and Driving Performance on Straight Road, Math. Probl. Eng. 2014 (2014). [52] J. Daniel, K. Ozbay, I.-J.S. Chien, Work Zone Safety Analysis, New Jersey Department of Transportation (2013) FHWA-NJ-2013-006, https://rosap.ntl.bts.gov/view/dot/ 29328/dot_29328_DS1.pdf?. [53] S. Heydari, L. Fu, L. Joseph, L.F. Miranda-Moreno, Bayesian nonparametric modeling in transportation safety studies: applications in univariate and multivariate settings, Anal. Methods Accid. Res. 12 (2016) 18–34. [54] P.C. Anastasopoulos, F.L. Mannering, A note on modeling vehicle accident frequencies with random-parameters count models, Accid. Anal. Prev. 41 (2009) 153–159. [55] R. Harb, E. Radwan, X. Yan, A. Pande, M. Abdel-Aty, Freeway work-zone crash analysis and risk identification using multiple and conditional logistic regression, J. Transp. Eng. 134 (2008) 203–214. [56] M.M. Ahmed, A. Ghasemzadeh, The impacts of heavy rain on speed and headway Behaviors: an investigation using the SHRP2 naturalistic driving study data, Transp. Res. Part C Emerg. Technol. 91 (2018) 371–384, https://doi.org/10.1016/j.trc.2018. 04.012. [57] A. Ghasemzadeh, M.M. Ahmed, Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches, Transp. Res. Part C Emerg. Technol. 90 (2018) 379–392, https://doi.org/10.1016/j.trc.2018.03.018. [58] A. Ghasemzadeh, M.M. Ahmed, Driver's Lane Keeping Ability in Heavy rain: preliminary Investigation using the SHRP2 Naturalistic Driving Study Data, Transp. Res. Rec. J. Transp. Res. Board. (2017)https://doi.org/10.3141/2663-13. [59] Nighttime Lighting Guidelines for Work Zones a Guide for Developing a Lighting Plan for Nighttime Work Zones2013. [60] NHTSA, National Highway Traffic Safety Administration, https://www. workzonesafety.org/files/documents/training/fhwa_wz_grant/atssa_ase_podcast_ script.pdf 2010. [61] K.-P. Kang, G.-L. Chang, J. Paracha, Dynamic late merge control at highway work zones: evaluations, observations, and suggestions, Transp. Res. Rec. J. Transp. Res. Board. (2006) 86–95. [62] I.M. Dias, D. Sundra, Work Zone Crash Analysis and Modeling to Identify Factors Associated with Crash Severity and Frequency, Mid-America Transportation Center, University of Nebraska, Lincoln, 2015.
