TRANSPORTATIONCOMPARISON OF SEVERITY AFFECTING FACTORSBETWEEN YOUNG AND OLDER DRIVERS INVOLVEDIN SINGLE VEHICLE CRASHES
Sunanda DISSANAYAKE, Ph.D., P.E.
Assistant Professor
Department of Civil Engineering, Kansas State University
Kansas, USA(Received March 1, 2004)
Single vehicle crashes contribute to a significant amount of fatalities in the United States. At the same time, fatality crash involvement rates ofyoung and older drivers are well above the average and both groups are identified as critical groups when it comes to highway safety. Therefore, thestudy described in this paper developed separate models to predict crash severity of single vehicle crashes by young and older drivers. By using themodels, factors affecting towards increased crash severity were identified for each group and comparisons were made. Almost all the common identified
factors influenced both driver groups in the same manner except in the case of alcohol and drug usage, which indicated an interesting finding in the caseof crash severity of older drivers. Speeding and non-usage of a restraint device were the two most important factors affecting towards increased crashseverity for both driver groups at all severity levels. Additionally, ejection and existence of curve/grade were determinants of higher young driver crashseverity at all levels. For older drivers, having a frontal impact point was a severity determinant at all levels. County of residence and weather condition
were not effective in making any changes with respect to crash severity at any level, while some other factors had a minimal affect. Findings of this study
are beneficial in investigating the potential ways of reducing crash severity, which could also be influential in reducing the occurrence of crashes as well.
Key Words: Crash severity, Safety, Logistic regression, Driver age differences, Modeling

1. INTRODUCTION
According to the assessments of injuries and fatalities in traffic crashes by the National Highway Traffic
Safety Administration (NHTSA), approximately 42,850
persons died in an estimated 38,356 motor vehicle trafficcrashes in the United States in 20021. This represents anincrease of 1.7% fatalities from the 42,116 reported in 2001
and is the highest level of fatalities since 1990. An additional 2,914,000 persons were injured on U.S. publicroads and highways in the calendar year 2002. Single vehicle crashes account for a considerable number of these
traffic crashes in the United States2. This situation isworsened by the fact that the majority of single vehicle
crashes (56.8 %) end up as fatal crashes as indicated inTable 1 Single vehicle crashes by severity level, 2001United States
Severity of
the Crash

SingleVehicleMultiple
Vehicle16,318

Total % of Single
VehicleCrashes

Fatal

21,47737,795

56.8

Injury589,000

1,414,000 2,003,000

29.4

PropertyDamage-Only

1,297,000

2,985,000 4,282,000

30.2

All

1,907,000

4,416,000 6,323,000

30.1

48 • IATSS RESEARCH Vol.28 No.2, 2004

Table 1.
On the other hand, both young and older drivers
are often identified as having critical highway safety
needs3. As indicated by crash statistics, both groups experience higher fatality involvement rates than the average driver population2. This is graphically illustrated in
Figure 1 based on the fatality rate per 100,000 licensed
drivers. Based on the figure, young drivers aged 16–25
years had a fatality involvement rate of 63.36, which is
the highest among all the groups. It is an important task
for the highway safety community to look at the ways of
reducing this alarming death rate of young people.
When a more accurate measure of exposure suchas number of vehicle miles traveled is taken into consideration, older drivers show much higher fatality involvement rates4. According to the changes that are taking
place within the age structure of the United States population, transportation needs and related highway safetyissues of the elderly are becoming increasingly important
as well. The majority of this elderly population is going
to be dependent on automobiles for their mobility needs,
thereby increasing the proportion of older drivers in thegeneral driving population.
Even though both young and older drivers are identified as critical groups with regard to highway safety,
their driving and other characteristics are completely different, making a comparison sensible. Looking into the

COMPARISON OF SEVERITY AFFECTING FACTORS BETWEEN YOUNG AND OLDER DRIVERS INVOLVED IN SINGLE VEHICLE CRASHES

Fatality Involvement Rate
(Per 100,000 Licensed Drivers)

70
60
50
40
30
20
10
>6
9

65
–6
9

55
–6
4

45
–5
4

35
–4
4

25
–3
4

21
–2
4

16
–2
0

0

Driver Age Group (yrs)Fig. 1 Fatality involvement rate by driver age group
2001-United States

ways of improving the highway safety situation of these
two groups, older and young drivers, could be addressedby using two approaches. One approach is to try to reduce
the number or the frequency of crashes by each group. The
second approach would be to try to reduce the severityof crashes. The analysis conducted in this study attemptedto address the issue through the second approach. On theother hand, it could be envisioned that the conditions affecting increased traffic crash severities are also contributing towards the occurrence of crashes as well.
Understanding the causes and situations underwhich drivers are more likely to be fatally or more severely injured will help towards improving the overall
highway safety situation. Accordingly, this study developed separate models to identify the factors that are contributing towards increased crash severity of older andyoung drivers involved in single vehicle crashes andmakes a comparison between the affecting factors.

2. PAST STUDIES
A considerable amount of literature is available on
the general subject of crash vulnerability of older drivers5,6,7. Similarly, a significant amount of literature isavailable on the safety issues related to young drivers aswell 8,9,10,11. The general models developed in the past
to identify the most important parameters which are crucial in reducing or increasing the level of injury severityof passengers, drivers or crashes include the following.
A study conducted by applying the techniques of categori-

S. DISSANAYAKE

cal data analysis to built a structural model relating driver
characteristics and behaviors to type of crash and injury
severity found that the driver behaviors of alcohol anddrug use and lack of seat belt use greatly increased theodds of more severe crashes and injuries7. Driver errors
were found to have a small effect, while personal characteristics of age and gender were generally insignificant,as found in this study. Another study by Mercier et5 alused logistic regression to assess whether age or genderor both influenced severity of injuries suffered in headon automobile collisions on rural highways. Individual
variables included age of the driver or passenger, position of the vehicle, and the form of the protection used,along with a set of interactive variables. Stewart discussed
the applications of classification and regression tree methods (CART) in roadway safety studies and used it to estimate measures of driver injury severity when the crashconsisted of a vehicle striking a fixed object on the roadside 12. Effects of vehicle air bags on the severity of offroad, fixed object crashes were studied by using crashdata from three states, Illinois, North Carolina, and Utah
obtained from the Highway Safety Information System(HSIS)13. A disaggregate model of road accident severity based on sequential logit models, was developed in
Canada, using Ontario road accident database14. O’Donnel
and Conner15, presented statistical evidence showing how
variations in the attributes of road users could lead tovariations in the probabilities of sustaining different levels of injury in motor vehicle accidents by using data from
New South Wales, Australia, using ordered logit andprobit models. Another study used logistic regression to
analyze Pennsylvania vehicle crash data to identify thedriver, highway, and environmental factors that differentiate run-off-road (ROR) crashes from non-ROR crashes16.
Nested logit formulation was used in Washington as ameans to predict the accident severity given that an accident had occurred17. Ordered probit models have also beenused to identify specific variables significantly influencing levels of injury in two-vehicle truck-car rear-end collisions on divided roadways and single vehicle large truck
collisions18,19. However, there were no published studies
available to the author regarding the comparison of severity affecting factors based on age group.

3. METHODOLOGY
3.1

Model format
When identifying the factors affecting crash severIATSS RESEARCH Vol.28 No.2, 2004 •49

TRANSPORTATION

ity of young and older drivers, this study utilized logistic regression, which was identified as appropriate due tothe discrete nature of the dependent variable, which was
the crash severity. The purpose of the model development
was to identify roadway, environmental, vehicle, anddriver related characteristics that affect the severity ofsingle vehicle crashes by young and older drivers and to
make a comparison among them. Ordinal response logistic regression was tested first and it was found to be unsatisfactory due to the violation of the common slope
parameter assumption. Additionally, even a single severity level difference was considered as important, leading
to the development of a set of sequential binary logistic
regression models for each driver group considered in thisstudy. Each ordered level of the sequential structure wasselected as indicated in Figure 2. This formulation was
based on five crash severity levels, fatal (within 30 days),incapacitating, non-incapacitating, injury and property
damage only (no injury).
Each ordered level of the model was a binary logistic regression model, where the relationship between

a dichotomous or binary response variable and one ormore explanatory variables are modeled. The logistic regression model uses the explanatory variables to predictthe probability that the response variable takes on a given
value. The response variable takes one of the two binary
values in the case of binary logistic regression models.
For a binary response variable y, the linear logistic regression model has the form,
Logit(pi) = log [pi /(1–pi)] = α + β’Xi
where,
pi = Prob. (yi = y1 | Xi) is the response probability to be modeled, and y1 is the first ordered level of y,α = intercept parameter,β’= vector of slope parameters, andXi = vector of explanatory variables.
3.2

Data
Traffic crash data for the development of models
in this study came from the Florida Traffic Crash Data-

Traffic Crash Database

Older DriverSingle Vehicle Crashes

Young DriverSingle Vehicle Crashes

Fatal vs. at most incapacitating crash

Fatal vs. at most incapacitating crash

Incapacitating vs. at most non-incapacitating crash

Incapacitating vs. at most non-incapacitating crash

Non-incapacitating vs. at most possible injury crash

Non-incapacitating vs. at most possible injury crash

Injury vs. property damage only crash

Injury vs. property damage only crashComparison of the Factors Affecting Crash Severity of Youngand Older Drivers Involved in Single Vehicle Crashes
Fig. 2 Formulation of the model structures

50 • IATSS RESEARCH Vol.28 No.2, 2004

COMPARISON OF SEVERITY AFFECTING FACTORS BETWEEN YOUNG AND OLDER DRIVERS INVOLVED IN SINGLE VEHICLE CRASHES

S. DISSANAYAKE

base, which provided sufficiently large sample sizes forboth young and older drivers involved in single vehicle
crashes. It was obtained from the State Data Program thatis maintained by the National Center for Statistics andAnalysis (NCSA) established under National Highway
Traffic Safety Administration (NHTSA) in the UnitedStates20. The State Data Program provides a comprehensive and illustrative census of motor vehicle crash patterns and trends for the 17 states in the State Data Systemat this time: California, Florida, Georgia, Illinois, Indiana, Kansas, Maryland, Michigan, Missouri, New
Mexico, North Carolina, Ohio, Pennsylvania, Texas,
Utah, Virginia, and Washington. South Carolina joined
the State Data System in January 2003 and NHTSA is
actively seeking additional members21.

Table 2 Explanatory variables considered in the modeling process

FREEWY

Crash occurred on a freeway.

3.3

VFOLT

Vehicle is at fault for the crash.

IMP_SIDE

Impact point is side of the vehicle.

IMP_FRNT

Impact point is front of the vehicle.

Age group definitions
Previous studies have used somewhat inconsistentcategorizations when it comes to the labeling of young
and older drivers. For the purpose of this study young
drivers were considered as 16–25 year olds. Those drivers older than 65 years were considered as old drivers.

Variable

Description When Response =1

BDWTHER

Weather is not clear.

DAYLIGHT

Crash occurred during day light condition.

RURAL

Crash occurred in a rural area.

PERDEF

Physical condition of the driver is a factor in thecrash.

PEJECT

Driver has ejected in the crash.

MALE

Driver is a male.

CNTY_RES

Driver is a resident of the same county.

FAULTC

Driver is at fault for the crash.

RESTR

A restraint device is used.

GR_CURA curve or a grade exists at the crash location.

AL_DRG

Driver is under influence of alcohol or drugs.

SPEED

Actual speed of the vehicle at the time of the crash.

which was a continuous variable.
3.4

Model development
Crash records related to drivers of the two groups
involved in single vehicle crashes were extracted from theFlorida Traffic Crash Database obtained from the State
Data Program. Since the weight and the type of vehicle
might also affect the crash severity outcome, only theautomobiles were considered in order to control for that.
Logistic regression model development was then carriedout by using PROC LOGISTIC procedure available inSAS22. In this procedure, severity levels had to be orderedfrom more severe to less severe23. Thus, in the case of binary logistic regression models, the event of interest(crashes with high severity) always received the rank order number 1. This situation arises due to the fact thatby default, PROC LOGISTIC models the probability of
the response that corresponds to the lower ordered value.
3.5

Explanatory variables
Explanatory variables that could possibly be considered as affecting crash severity were selected from all
the available variables in the crash database. They fell into
four categories: driver-related factors, vehicle-related factors, roadway-related factors, and environmental-related
factors. A detailed list of all the selected explanatory variables considered in the model development is given in
Table 2. All of the explanatory variables were treated as
dichotomous variables (0 and 1) except the travel speed,

3.6

Model fitness
In the analyses using SAS, results provide two criteria known as AIC and SC that are useful for comparing models and two other criteria (–2 LOG L) and Score
to test the null hypothesis that all regression coefficientsare zero. Except for the score statistic, all of the criteria
are based on the likelihood for fitting a model with intercepts only or fitting a model with intercepts and explanatory variables. AIC (Akaike Information Criterion)
and SC (Schwarz Criterion) are adjustments to the –2
LOG L score based on the number of explanatory variables in the model and the number of observations used.
For a given set of data, the AIC and SC are goodnessof-fit measures that can be used to compare one modelwith another, with lower values indicating a more desirable model. (–2 LOG L) is the –2 Log Likelihood statistic, which has a Chi-square distribution under the null
hypothesis that all regression coefficients of the modelare zero. The LOGISTIC procedure provides this Chisquare value along with the degrees of freedom, and ap-value for this statistic. A significant p-value (Ex.
p<0.05) provides the evidence that at least one of the regression coefficients for an explanatory variable is nonzero. Score is a score statistic, which has an asymptotic
Chi-square distribution under the null hypothesis. The
procedure outputs the chi-square value, degrees of freeIATSS RESEARCH Vol.28 No.2, 2004 •51

TRANSPORTATION

dom, and a p-value for this statistic. All four parameters
were utilized in this study to evaluate the model fitness.

4. ANALYSIS RESULTS

and driver ejection had an affect towards an increased
crash severity of young drivers, but they were not significant in making any difference in older driver injury
severity. Having a frontal impact point increased the possibility of a fatal crash for older drivers, but it was unimportant for young drivers.

Results of the models at each level that include model
coefficients and Odds Ratios are provided in Tables 3through 5. Odds Ratio is a measure of the strength of therelationship between the explanatory variable and theevent of interest. When the Odds Ratio is farther awayfrom 1 (depending on the sign of the coefficient) thestrength of the relationship increases. The following are
the descriptions of the comparison of the identified severity affecting factors for young and older drivers involved in single vehicle crashes.

4.2

4.1

INTERCEPT-3.1223--2.530-DAYLIGHT

-

-

0.219

1.25

0.5500

1.733

0.484

1.62
4.61

Fatal vs. at most incapacitating crashes
There were five factors (whether the driver was at
fault, restraint device usage, existence of curve or grade,having a side impact point, and speed of the vehicle) thatwere identified as important for both young and older
drivers. The relationship between each of the factors andthe outcome, fatal crash as against at most incapacitating crash was consistent. However, some factors weremore strongly influential towards increased crash severity of one group than the other. For example, a young
driver who is at fault in a crash is much more likely to
die in a single vehicle crash than an older driver at fault
in a similar crash. In addition to that, lighting conditionat the time of the crash, influence of alcohol and drugs,

Incapacitating vs. at most non-incapacitating
crash
Again, there were five variables (rural crash location, driver ejection, being a male, restraint device usage,

Table 4 Modeling results for incapacitating vs. at most
non-incapacitating crash
Explanatory
Variable

RURAL

Older Driver Model

Young Driver Model

Coefficient Odds Ratio Coefficient Odds Ratio

PEJECT

1.0224

2.78

1.528

MALE

-0.19130.826-0.292

0.75

RESTR

-0.7934

0.452-0.875

0.42

0.229

1.26

-

-

GR_CUR--

FREEWAY-0.4188

0.658

IMP_FRNT

0.4784

1.613

AL_DRG-0.6821

0.506

-

-

SPEED

0.0398

1.041

0.02

1.02

“-”: Variable is not significant or Odds Ratio is not applicable.

Table 5 Non-incapacitating vs. at most possible injury crash
Table 3 Modeling results for fatal vs. at most incapacitating crash
Explanatory
Variable

Older Driver Model

Young Driver Model

Explanatory
Variable

Older Driver Model

Young Driver Model

Coefficient Odds Ratio Coefficient Odds Ratio

INTERCEPT-2.2822--1.286-

Coefficient Odds Ratio Coefficient Odds Ratio

RURAL

0.6039

1.829

0.554

1.74

INTERCEPT-5.0069--4.795-PERDEF0.2177

1.243

DAYLIGHT---0.572

0.56

PEJECT

0.6394

1.895

0.802

2.23

PEJECT

-

-

1.736

5.67

MALE

-0.35230.703-0.3380.71

FAULTC-1.3684

0.255-3.328

0.04

RESTR

-0.49420.61-0.645

0.53

RESTR

-1.2752

0.279

-1.36

0.26

GR_CUR

0.3029

1.354

0.324

1.38

GR_CUR

0.4820

1.619

0.629

1.88

VFOLT

-

-

0.125

1.33

IMP_SIDE

0.8202

2.271

0.851

2.34

IMP_SIDE

-

-

0.203

1.23

IMP_FRNT

1.3056

3.690

-

-

IMP_FRNT

0.6643

1.943

0.284

1.33

AL_DRG

-

-

1.346

3.84

AL_DRG-0.5783

0.561

-

-

SPEED

0.0456

1.047

0.026

1.03

SPEED

0.0359

1.037

0.012

1.01

“-”: Variable is not significant or Odds Ratio is not applicable.

52 • IATSS RESEARCH Vol.28 No.2, 2004“-”: Variable is not significant or Odds Ratio is not applicable.

COMPARISON OF SEVERITY AFFECTING FACTORS BETWEEN YOUNG AND OLDER DRIVERS INVOLVED IN SINGLE VEHICLE CRASHES

Table 6 Injury vs. property damage only crash
Explanatory
Variable

Older Driver Model

Coefficient Odds Ratio Coefficient Odds Ratio

INTERCEPT-2.2566-

Young Driver Model-1.068-DAYLIGHT

0.2834

1.328

0.202

1.22

RURAL

0.4566

1.579

0.512

1.67

PERDEF

0.2925

1.34--

PEJECT

-

-

1.48

4.37

MALE

-0.37270.689-0.736

0.48

RESTR

-0.18420.832-0.3190.73

GR_CUR

0.2797

1.323

0.187

1.21

IMP_SIDE-0.28850.749-0.242

0.79

IMP_FRNT

0.5639

1.758

-

-

AL_DRG-0.6629

0.515

0.436

1.13

SPEED

0.0370

1.038

0.020

1.02

“-”: Variable is not significant or Odds Ratio is not applicable.

and speed) that were significant at this level for bothyoung and older drivers. The nature of the relationshipin each case was consistent across both driver groups.
However some factors for example, ejection of the driver,has a very strong effect (higher Odds Ratio) on increased
severity of young drivers than it is for older drivers. Additionally, three more factors were identified as significant for older drivers, but not for young drivers. Those
were freeway crash location, having a frontal impact
point, and alcohol/drug usage. Driving under daylight
condition was an influential factor for young drivers only.
4.3

Non-incapacitating vs. at most possible injury
crash
Seven common variables, (rural crash location,
driver ejection, being a male, restraint device usage, existence of curve or grade, having a frontal impact point,and speed) were identified as affecting severity at thislevel, which were related to the outcome in the same way
in the case of both young and older drivers. Similar to
the above-discussed two levels, the effect of ejection was
more critical for young drivers. Two additional variablesin the older driver model were, physical condition of thedriver, and usage of alcohol/drugs. As for the young
driver group, vehicular faults, and having a side impact
point, were identified as influential in addition to theseven common variables.
4.4

Injury vs. property damage only crash
There were more common variables included inboth young and older driver models at this level, which

S. DISSANAYAKE

were daylight conditions, rural crash location, being amale, restraint device usage, existence of curve/grade,having a side impact point, usage of alcohol/drugs, andspeed. Two additional variables included in the older
driver model were, physical condition of the driver, andhaving a frontal impact point, whereas the young drivermodel included the ejection of the driver.
4.5

Discussion
Travel speed of the vehicle at the time of the crash(SPEED) and restraint device usage are the two mostpowerful factors affecting severity at all levels, equallyimportant for both young and older drivers in single vehicle crashes. As indicated by the positive sign of thevariable SPEED, the higher travel speed always leads toincreased crash severity, which is explainable based on
the laws of physics. Similarly, it is an obvious fact thatthe usage of a restraint device reduces the possibility of
having a higher severity irrespective of the driver age andseverity level (Negative coefficient of RESTR).
As for the older driver models, having a frontal impact point also affected towards increased crash severity
at all levels in addition to speed and restraint device usage. Additionally, three other variables, usage of alcoholand drugs, being a male, and rural crash locations are capable of affecting severity at the three lower levels ofolder driver models. Female older drivers and crashes inrural areas tend to be of higher severity, where as an interesting observation could be made related to the usageof alcohol or drugs. When an older driver is under the
influence of alcohol or drugs the severity of the crashtends to be lower, which is contradictory to young drivers in similar crashes. A possible explanation could bethat elderly, when they are aware of the fact that they are
under the influence of alcohol or drugs may be somewhat
cautious (even though they are incapable of avoiding
crashes) when driving.
In young driver models, two more variables, ejection of the driver and the existence of grade or curve/
grade become significant at all levels. As indicated by ahigh Odds Ratio, crash severity is highly affected by theejection of the young driver. Additionally, young drivers who lack driving experience are not capable of handling or negotiating curves and grades well and tend tohave higher severity crashes at such locations.
County of residence (CNTY_RES) and weather
condition (BDWTHER) are not effective in making any
changes with respect to crash severity at any level. As
indicated by the CNTY_RES, driver familiarity with thearea in which he/she is driving is not a determinant in
IATSS RESEARCH Vol.28 No.2, 2004 •53

TRANSPORTATION

crash severity, however outcome from BDWTHER
should be interpreted in the pretext that the crash data forthe modeling came from Florida, where no seriously hazardous bad weather related driving conditions such as icy
roads are existent. Driver or vehicle faults in single vehicle crashes were also of very minor importance.

5. CONCLUSIONSThis paper developed two sets of sequential binary
logistic regression models for young and older drivers involved in single vehicle crashes with the intention of identifying and comparing severity affecting factors. The
methodology provided a reasonably satisfactory way ofachieving these objectives, even though drawbacks suchas the accuracy and reliability of police reported crashdata and inter-relationships between considered independent variables could be existent.
All of the factors identified influenced both drivergroups in a uniform way, except the influence due to alcohol and drugs, which was surprisingly capable of reducing crash severity of older drivers. Speeding and not usinga restraint device were the two most important factors
affecting towards increased crash severity for both drivergroups at all severity levels. This emphasized the need forfurther efforts towards increasing the seat belt usage rates
among drivers. Additionally, ejection and existence of
curve/grade were determinants of higher young drivercrash severity at all levels. For older drivers, having a frontal impact point was a severity determinant at all levels.

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