A Nationwide Investigation of Microscopic and Macroscopic Factors and Screening Counties with Respect to Fatal Crashes Due to Drowsiness

Dr. Jaeyoung Lee & Ahmad Abdel-Aty* - University of Central FloridaHyunsuk Lee - Korea Expressway Corporation

OverviewDrowsy driving has been considered a serious problem in traffic safety because it makes driver less attentive, slows reaction time and affects a driver’s capability to make decisions. The objectives of this study are: 1) discovering contributing factors for fatal crashes due to drowsiness both at microscopic and macroscopic levels; and 2) screening counties with respect to drowsiness related fatal crashes. Binary logistic and negative binomial models were developed at the microscopic and macroscopic levels, respectively, to uncover factors for fatal crashes due to drowsiness using FARS data (2007-2009). Subsequently, a nationwide county-level screening was conducted to identify counties with drowsy driving problems. The microscopic model identified that heavy trucks on the high-speed roadway segments with the less number of lanes in dark conditions are more likely to be involved in drowsiness related fatal crashes. On the other hand, the macroscopic modeling revealed county-level factors such as demographic (age, race, etc.) and socio-economic factors (commuting characteristics, industry, etc.). The top 5% of counties with respect to drowsy driving problems were identified by the screening technique. Among these counties, the largest number of counties is located in Texas (35%), and followed by California (8%), and Utah (6%). This study identified the contributing factors for drowsiness related fatal crashes at macro- and micro-level. The key findings from this study are expected to be useful for effective nation-wide strategic plans to alleviate drowsy driving relevant factors. Furthermore, the screening results where to focus with priority at the macroscopic level.

Summary and Conclusion

·The binary logistic regression identified microscopic factors that have a significantly different association with fatal crashes involving drowsiness when compared with fatal crashes not influenced by drowsiness, including time of day, vehicle type, and road type·The negative binomial count model model revealed several demographic factors that associated with increased drowsiness related fatal crash frequencies, including age, race, and type of economic activity. ·PSI identified the potential for drowsiness related fatal crash reductions by county. Among the counties identified to be in the top 5% of counties in terms of PSI, Texas has the largest number (35%), followed by California (8%), Utah (6%), Arizona (5%), Colorado (4%), Pennsylvania (4%), and South Carolina (4%). ·Both microscopic and macroscopic contributing factors found from this study are expected to be utilized for effective nation-wide strategic plans to alleviate drowsy driving issues. Screening results can be used to determine where to focus with priority at the macroscopic level. These counties can be focused on for area-wide engineering treatments (i.e. installing rumble strips, providing more rest areas, etc.) and more targeted awareness campaigns and education.

Background·A drowsy driving crash is defined as a crash in which the driver is reported with sleepiness, drowsiness, or fatigue (NHTSA, 2011). ·According to the NHTSA statistics in 2011, approximately 2.2% to 2.6% of total fatal crashes in 2005-2009 involved drowsiness. ·Drowsy driving has been considered a serious problem in traffic safety because it makes driver less attentive, slows reaction time, affects a driver’s capability to make decisions and thus it increases the probability of severe crash occurrence.·Previous studies have found that the majority of drowsiness involved drive-off-the-road crashes, occurred at higher speed, happened primarily during the nighttime and mid-afternoon time, and related to surface condition, speed, education, driving experience, work schedule, and gender. ·Most of the previous studies have mainly focused on crash characteristics, environmental conditions, and driver characteristics, affecting drowsiness while driving. Few have investigated drowsiness related crashes in more macroscopic aspects for effective and practical strategic policies to reduce drowsiness related severe crashes along with GIS visualization.

Methods• Fatal crash data (2007-2009), including data on roadway type, surface conditions, and

light conditions, were obtained from FARS (Fatality Analysis Reporting Systems). • County-level data, including data on demographic, socio-economic, commute, and

industry variables, were acquired from the U.S. Census Bureau.• A binary logistic regression model was employed to identify the factors contributing

to fatal crashes due to drowsiness at the microscopic level. The drowsiness involved crashes, the case group, (N=2151) was matched to the comparison group which was a set of randomly selected non-drowsiness involved fatal crashes (N=6288) in an approximately 1:3 ratio.• A negative binomial model, or Poisson Gamma model, was developed to reveal the

macroscopic contributing factors for drowsiness involved fatal crashes. • The Kernel Density Estimation (KDE) was used to serve the purpose of clustering the

crashes and identifying the hotspots. • PSI (Potential for Safety Improvements) was used as a performance measure to

identify hot zones with respect to drowsiness related safety problems that can benefit from safety treatments. If a county has PSI greater than zero, the cluster was considered hazardous, while with a PSI of zero or smaller were considered safe.

Variable Estimate S.E. p-value

Intercept -3.3669 0.2082 <.0001

Heavy truck 0.3343 0.0872 0.0001

Multi vehicle involved -0.2442 0.0681 0.0003

Number of lanes -0.1258 0.0352 0.0004

Intersection or driveway access -0.7597 0.1239 <.0001

Ramp -1.2675 0.4528 0.0051

Curve -0.1432 0.0617 0.0203

Urban area -0.6414 0.0760 <.0001

Functional classification: Freeway or expressway 0.7195 0.1286 <.0001

Functional classification: Arterial road 0.7140 0.1011 <.0001

Functional classification: Collector 0.2671 0.1060 0.0118

Surface condition: Wet -0.4269 0.0936 <.0001

Surface condition: Snow or Ice -1.6084 0.2241 <.0001

Speed limit 0.0465 0.00344 <.0001

Lighting condition: Dark -0.1357 0.0563 0.0159

Lighting condition: Dawn 1.0878 0.1643 <.0001

Lighting condition: Dusk -1.0104 0.2861 0.0004

Area under ROC curve 0.758

Drowsy fatalities N=2,151

Non-drowsy fatalities N=6,288

Category Variable Estimate S.E. p-value- Intercept -1.6007 0.6237 0.0103

Demographic Log of population 0.5151 0.0310 <.0001

Percentage of elderly people (65 or older)

-0.0293 0.0092 0.0015

Percentage of African Americans

0.0068 0.0025 0.0067

Percentage of Hispanics 0.0067 0.0020 0.0006

Socio-economic Median household income (in USD 1,000)

-0.0067 0.0039 0.0842

Employment Percentage of unemployed people

-0.0619 0.0123 <.0001

Percentage of people working at home

-0.0303 0.0125 0.0156

Commute Mean commute time to work (in minutes)

-0.0174 0.0072 0.0153

Percentage of commuters using public transportation

-0.0553 0.0120 <.0001

Industry Construction 0.0353 0.0141 0.0122

Manufacturing -0.0514 0.0068 <.0001

Wholesale trade -0.1403 0.0311 <.0001

Retail trade -0.0458 0.0147 0.0018

Transportation, warehousing, and utilities

0.0406 0.0160 0.0113

Information -0.0721 0.0366 0.0491

Educational services, healthcare and social assistance

-0.0494 0.0084 <.0001

Arts, entertainment, recreation, and accommodation and food services

-0.0208 0.0106 0.0487

Public administration -0.0337 0.0116 0.0036

Dispersion 0.8751 0.0724  

Log-likelihood (full model) -3408.7951Log-likelihood (intercept-only model) -3746.0873Number of Counties N=3,232

Table 1: Binary Logistic Regression Model for Drowsiness Involved Fatal Crashes

Table 2: Negative Binomial model for Drowsiness related Fatal Crashes based on Nationwide Counties