This document is available to the public from the National Technical Information Service, Springfield, VA 22161 

U.S. Department

of Transportation

National Highway

Traffic Safety 

Administration

DOT HS 809 760 September 2004

Technical Report

An Assessment of the Crash-Reducing 

Effectiveness of Passenger Vehicle 

Daytime Running Lamps (DRLs)

Published By:

National Center for Statistics and Analysis

Advanced Research and Analysis
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 i

1. Report No.

DOT HS 809 760

2. Government Accession No. 3. Recipient's Catalog No.

5. Report Date

September 2004

4. Title and Subtitle

An Assessment of the Crash-Reducing Effectiveness of Passenger Vehicle 

Daytime Running Lamps (DRLs) 6. Performing Organization Code

NPO-101

7. Author(s)

Joseph M. Tessmer

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

Mathematical Analysis Division, National Center for Statistics and Analysis

National Highway Traffic Safety Administration

U.S. Department of Transportation

NPO-121, 400 Seventh Street, S.W.

Washington, D.C. 20590

11. Contract or Grant No.

13. Type of Report and Period Covered

NHTSA Technical Report

12. Sponsoring Agency Name and Address

Mathematical Analysis Division, National Center for Statistics and Analysis

National Highway Traffic Safety Administration

U.S. Department of Transportation

NPO-121, 400 Seventh Street, S.W.

Washington, D.C. 20590

14. Sponsoring Agency Code

15. Supplementary Notes

Abstract

This study estimates the effectiveness of passenger vehicle daytime running lights in reducing two-vehicle opposite direction 

crashes, pedestrian/bicycle crashes, and motorcycle crashes. The authors chose the generalized simple odds, a conventional 

statistical technique, to analyze the data.

Results based on simple odds indicate that from 1995 to 2001:

• DRLs reduced opposite direction daytime fatal crashes by 5 percent.

• DRLs reduced opposite direction/angle daytime non-fatal crashes by 5 percent. 

• DRLs reduced non-motorists, pedestrians and cyclists, daytime fatalities in single-vehicle crashes by 12 percent.

• DRLs reduced daytime opposite direction fatal crashes of a passenger vehicle with a motorcycle by 23 percent.

Reviewers of this paper required the inclusion of results using the odds ratio technique. The estimated the effect of DRLs are 

–6.3 percent, –7.9 percent, 3.8 percent, and 26 percent, respectively. None of these results were statistically significant. 

17. Key Words

daytime running lamps, daytime running lights, DRL, 

DRLs, logistic regression, simple odds, fatal crashes, 

FARS, NASS, GES, motorcycle, pedestrians, cyclists

18. Distribution Statement

Document is available to the public through the National 

Technical Information Service, Springfield, VA 22161

http//:www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

36

22. Price

Form DOT F 1700.7 (8-72) Reproduction of complete page authorized 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 ii

TABLE OF CONTENTS

Executive Summary ........................................................................................................................1

Background .....................................................................................................................................3

Methodological Changes from Preliminary Assessment ..............................................................7

Data and Methodology...................................................................................................................9

Caveats..........................................................................................................................................12

DRL Effectiveness in Fatal Two-Vehicle Crashes......................................................................12

DRL Effectiveness in Fatal Two-Vehicle Crashes - Results......................................................13

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes.............................................................13

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes - Results .............................................14

DRL Effectiveness in Fatal Single-Vehicle Pedestrian/Cyclist Crashes...................................15

DRL Effectiveness in Fatal Single-Vehicle Pedestrian/Cyclist Crashes - Results...................16

DRL Effectiveness in Fatal Crashes of a Passenger Vehicle with a Motorcycle .....................16

DRL Effectiveness in Fatal Crashes of a Passenger Vehicle with a Motorcycle - Results .....17

Conclusions....................................................................................................................................17

Appendix A Analytic Approach....................................................................................................19

Logistic Regression Estimates Using the Simple Odds .............................................................20

Appendix B Alternate Analytic Approach....................................................................................23

Logistic Regression Estimates Using the Odds Ratio ...............................................................25

Appendix C SAS® Code ................................................................................................................29

Bibliography ..................................................................................................................................31
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 iii

List of Tables

Table 1 Summary of Findings on DRL Effectiveness..................................................................7

Table 2 DRL-Equipped Vehicles in Target and Single-Vehicle Fatal Crashes, 

FARS 1995-2001...........................................................................................................................12

Table 3 Vehicles w/o DRL in Target and Single-Vehicle Fatal Crashes, 

FARS 1995-2001...........................................................................................................................13

Table 4 DRL-Equipped Vehicles in Target and Single -Vehicle Non-Fatal Crashes,

NASS/GES 1995-2001 ..................................................................................................................14

Table 5 Vehicles w/o DRL in Target and Single-Vehicle Non-Fatal Crashes, 

NASS/GES 1995-2001 ..................................................................................................................14

Table 6 Single-vehicle Pedestrian and Cyclist Fatalities FARS 1995-2001 Vehicles Equipped 

with DRLs .....................................................................................................................................15

Table 7 Single-vehicle Pedestrian and Cyclist Fatalities FARS 1995-2001Vehicles Not 

Equipped with DRLs.....................................................................................................................15

Table 8 Passenger Vehicles with DRLs Involved in Fatal 2-Vehicle Crashes of a Motorcycle 

and a Single Passenger Vehicle, FARS 1995-2001.....................................................................16

Table 9 Passenger Vehicles w/o DRLs Involved in Fatal 2-Vehicle Crashes of a Motorcycle 

and a Single Passenger Vehicle, FARS 1995-2001.....................................................................17
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 1

Executive Summary

This study estimates the effectiveness of passenger vehicle daytime running lights in reducing two-vehicle 

opposite direction crashes, pedestrian/bicycle crashes, and motorcycle crashes. The authors chose the 

generalized simple odds, a conventional statistical technique, to analyze the data. The generalized odds 

ratio attempts to adjust for a variety of exogenous factors other than the presence or absence of DRLs 

not specifically controlled for within the model.

Significant results of this study show that from 1995 to 2001:

Simple Odds Results:

• DRLs reduced opposite direction daytime fatal crashes by 5 percent.

• DRLs reduced opposite direction/angle daytime non-fatal crashes by 5 percent. 

• DRLs reduced non-motorists, pedestrians and cyclists, daytime fatalities in single-vehicle 

crashes by 12 percent.

• DRLs reduced daytime opposite direction fatal crashes of a passenger vehicle with a 

motorcycle by 23 percent.

The reviewers of this paper required the inclusion of an analysis based on odds ratio, which can be 

found in Appendix B. Like the simple odds, the odds ratio attempts to control for a variety of factors 

other than the presence or absence of DRLs. The estimated effectiveness of DRLs based on this 

technique is extremely sensitive to small changes encountered in real world crash data. As a result, 

reductions in target crashes during the daytime using the odds ratio technique may not be detected over 

the inherent background noise of the data system. None of the results based on the odds ratio are 

statistically significant. 

Odds Ratio Results:

• DRLs reduced opposite direction daytime fatal crashes by –6.3 percent that is DRLs increase 

opposite direction daytime fatal crashes by 6.3 percent.

• DRLs reduced opposite direction/angle daytime non-fatal crashes by –7.9 percent that is DRLs 

increase opposite direction/angle daytime non-fatal crashes by 7.9 percent.

• DRLs reduced non-motorists, pedestrians and cyclists, daytime fatalities in single-vehicle 

crashes by 3.8 percent. 

• DRLs reduced daytime opposite direction fatal crashes of a passenger vehicle with a 

motorcycle by 26 percent. 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 2

METHODOLOGY:

A case-control method was chosen as the approach for this study, since only specific make-models for 

each year were equipped with DRLs. The number of crashes for a set of passenger vehicles equipped 

with DRLs is compared to passenger vehicles manufactured in the same years without DRLs. The 

groups of vehicles are analyzed by time of day and crash type.

The generalized simple odds method was used to analyze the data. This technique implicitly attempts to 

control for factors, other than the presence or absence of DRLs, that could be associated with crash 

occurrences. The effectiveness of DRLs due to differences in passenger vehicle types, namely, 

passenger cars, SUVs, vans, and light/pickup trucks is addressed explicitly. The simple odds provided 

useful statistically significant results. 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 3

Background

This is the second NHTSA study on the effectiveness of Daytime Running Lamps (DRLs). The 

preliminary study was published in June 2000 and is the basis of this research.

Many traffic crashes are the result of the failure of a driver to notice another vehicle. Visual contrast is 

an essential characteristic that enables a driver to detect vehicles. The purpose of daytime running 

lamps (DRLs) is to increase the drivers’ ability to detect DRL-equipped vehicles, particularly in the 

peripheral visual field, by increasing visual contrast. Seven countries require the use of DRLs during all 

daytime periods: Canada, Denmark, Finland, Hungary, Iceland, Norway, and Sweden. Results of 

DRL studies from these countries consistently, however not conclusively, show that DRLs reduce the 

number of two-vehicle crashes during daylight, dusk, and dawn. This study examines the effectiveness 

of first-generation DRLs, using U.S. national data for passenger vehicles.

DRLs come in a variety of configurations. DRLs may be upper beam headlamps at reduced intensity, 

low-beam headlamps at full or reduced power, turn signals or dedicated lamps. In addition the 

brightness, color and light dispersion are design features of DRLs. Four manufacturers began equipping 

selected 1995 model year vehicles, for sale within the U.S., with DRLs. General Motors Corporation 

produces DRL-equipped vehicles with higher intensity DRLs than those used in Scandinavian countries. 

 In the U.S. the availability of DRL-equipped vehicles has increased with each model year since 1995. 

Since the cost of DRLs is low, small reductions in the number of crashes would likely be considered 

cost effective. A partial chronological summary of results from several previous studies of the 

effectiveness of DRLs follows.

Finland’s legislation of 1972 required the use of low-beam headlights in rural areas during winter. The 

rural multiple-vehicle daytime crash rate decreased by 27 percent as a result.1

In 1975, Clayton and Mackay2, at Indiana University, found that drivers failing to process information 

properly caused almost half of all crashes. The most prevalent information processing errors were faulty 

visual perception, recognition errors and comprehension errors. In addition, it was shown that traffic 

crashes were due more to inattention and distraction than to poor vision. The crash reduction potential 

of DRLs lies in their ability to attract attention, especially in the peripheral visual field, thereby enhancing 

detect ability.

A study conducted by Transport Canada3 in 1975-1976 examined the crash experience with part of the 

Canadian defense vehicle fleet equipped with automatic headlights, a version of DRLs. The results 

 

 

1Andersson, K., Kilsson, G., and Salusjärvi, S. The Effect On Traffic Accidents on the Recommended use of Vehicle 

Running Lights in the Daytime in Finland. Report No 102. Swedish road and Traffic Research Institute (VTI), 1976.

 

2Claton, A.B. and Mackay, G.M. Aetiology of Traffic Accidents. Health Bulletin, 31(4), 277-280, 1972.

 

3Attwood, D.A. The Potential of Daytime Running Lights as a Vehicle Collision Countermeasure. SAE Technical 

Paper 810190. Society of Automotive Engineers, 1981.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 4

published by Attwood in 1981 showed a 20 percent crash decrease in the specially equipped vehicles 

compared to the comparison group of unmodified vehicles.

Swedish legislation required the use of DRLs throughout the year starting in October 1977. An 11 

percent reduction in daytime crashes was observed. Two-vehicle, head-on crashes were reduced by 

10 percent, angle crashes were reduced 9 percent, crashes involving a bicycle or moped were reduced 

by 21 percent, and crashes involving a pedestrian or a cyclist decreased 17 percent.4 These results 

were questioned by Theeuwes and Riemersma in 19955, as the proportion of multi-party crashes was 

not reduced as a proportion of all crashes.

Hills, in 19806, and more recently Sekuler and Blake,7 found that increasing the visual contrast of a 

vehicle increases the ability of other drivers to detect and monitor the vehicle. Low contrast between a 

vehicle and its background can be quite common during daylight hours. Contrast is reduced by color, 

rain, clouds and low levels of light that occur at dawn and dusk.

Stein reported in 19858 the results of a study by the Insurance Institute for Highway Safety (IIHS), 

which equipped over 2,000 passenger cars, light trucks and vans with DRLs. Relevant multi-vehicle 

crashes were 7 percent lower for the DRL-equipped vehicles than the comparison (unmodified) 

vehicles.

Norway required the installation of DRLs by vehicle manufacturers in January of 1985 and the use of 

low beam head lights was required on all vehicles in Norway not equipped with DRLs in April of 1988. 

 Elvik reported9 that a 15 percent reduction in all summertime multi-vehicle daylight crashes was 

achieved.

Canada required that all new passenger cars, trucks, multi-purpose vehicles, and buses manufactured 

for sale in Canada be equipped with DRLs after December 1, 1989. In September 1993 Arora, et al.10

 

 

4Andersson, K. Nilsson, G. The Effects on Accidents of Compulsory Use of Running Lights During Daylight in 

Sweden. Report No. 208A, Swedish Road and Traffic Research Institute (VRI),1981.

 

5

Theeuwes, J. and Riemersma, J. Daytime Running Lights as a Vehicle Collision Countermeasure: The Swedish 

Evidence Reconsidered. Accident. Anal. Prevention. 27:633-642, 1995.

 

6Hills, B.L. Vision, Visibility and Perception in Driving. Perception, 9, 183-216, 1980. 

 

7

Sekuler, R. and Blake, R. Perception, (Second Edition) Toronto: McGraw-Hill, 1990. 

 

8

Stein, H. Fleet Experience with Daytime Running Lights in the United States. SAE Technical Paper 851239. 

Warrendale, PA, Society of Automotive Engineers, 1985.

 

9

Elvik, R. The Effects of Accidents of Compulsory Use of Daytime Running Lights for Cars in Norway. Accident 

Analysis and Prevention, 25(4) 383-398, 1993.

 

10Arora, H. Collard, D. Robbins, G. Welbourne, E.R. White, J.G. Effectiveness of Daytime Running Lights in Canada, 

Report No. TP1298 (E), Transport Canada 1994.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 5

conducted an extensive analysis on the effectiveness of DRLs for Transport Canada. They estimate that 

relevant crashes were reduced by 11.3 percent, which was statistically significant at p<0.05.

In October of 1990, Denmark required universal use of DRLs. No overall effect was reported. 

However, Hansen identified a statistically significant 37 percent decrease in crashes involving a left turn 

in 199311.

Hungary has required the use of DRLs on rural roads since March 1993. Hollo studied the crash 

experience of DRL-equipped vehicles and presented the findings at a conference in the Czech Republic 

in 199512. Several changes in traffic regulations and enforcement, which includes the reduction of the 

speed limit, stricter seat belt laws, increases in police patrols, significantly higher fines and a campaign to 

increase public awareness of traffic-related issues were considered confounding factors, thereby making 

it difficult to estimate the effect of DRLs. Nonetheless, Hollo estimates that DRLs reduced the number 

of rural daytime "frontal and cross traffic" crashes by 7 to 8 percent. Hallo further claims that during 

"good visibility" crashes are reduced 11 to 14 percent.

IIHS’ Highway Loss Data Institute (HLDI) in 199713 released findings from a study of the personal 

injury claims for vehicles that added DRLs as a standard feature in 1995 and 1996, compared to the 

claim frequencies for the same makes and models prior to adding DRL. The number of relative claims 

was found to have increased slightly after DRLs were introduced. However, HLDI’s study was not able 

to identify a consistent pattern of increases among vehicles. HLDI’s study hypothesized that this finding 

was not surprising, as “...claims for striking vehicles, single-vehicle crashes, and nighttime crashes could 

not be identified...” and therefore, could not be excluded from the study. Striking vehicle, single-vehicle, 

and nighttime crashes would not likely be impacted by the presence of DRLs.

Tofflemire and Whitehead14 re-analyzed the Canadian DRL law in 1997 using a “quasi-experimental 

comparative posttest design” and found that opposite direction and angle crashes were reduced by 5.3 

percent, which was statistically significant at p<0.05. The study concluded that the DRL law had a 

greater effect on opposite direction crashes (15 percent reduction) than angle crashes (2.5 percent 

reduction). 

Each province in Canada was individually analyzed. Only Nova Scotia and New Brunswick 

 

 

11Hansen, L.K. Daytime Running Lights in Denmark - Evaluation of the Safety Effect. Translated exact.

 

12Hollo, P. Changes of the DRL-Regulations and their Effects on Traffic Safety in Hungary. Paper presented at the 

conference: Strategic Highway Safety Program and Traffic Safety, the Czech Republic, September 20-22, 1995. Preprint for 

sessions on September 21, 1995.

 

13Highway Loss Data Institute Bulletin Volume 15, Number 1, December 1997.

 

14Tofflemire, T. C., Whitehead, P.C. An Evaluation of the Impact of Daytime Running Lights on Traffic Safety in 

Canada, Journal of Safety Research, Volume 28, Number 4, 1997.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 6

experienced a statistically significant (p<0.05) reduction in crashes.

While the 1993 and 1997 Canadian studies described above are among the few studies reporting 

statistically significant results, in most other studies the data sets are small, which can result in 

nonsignificant statistical results, even when an effect might exist.

Hollo15 reported that DRLs reduced daylight frontal and crossing vehicle crashes by 4.7 percent to 15.2 

percent in Hungary, depending on the statistical technique used and assumptions made.

Tessmer16 estimated that the effectiveness of DRLs in US fatal two-vehicle opposite-direction crashes 

ranged from –8 percent to 2 percent. For non-fatal crashes the effectiveness ranged from 5 percent to 

7 percent. For pedestrians fatalities in single-vehicle crashes, the estimated effectiveness ranged from 

28 percent to 29 percent.

Lau17 estimates that DRLs reduce multiple vehicle crashes by 5 to 13 percent. Lau even estimates that 

DRLs reduce multiple vehicle nighttime crashes by 5 percent, which suggests that there may a 

confounding lurking variable within the data.

Farmer and Williams18 demonstrated that DRLs are associated with a 3.2 percent decline in multiplevehicle daylight crashes. 

Thompson19 in 2003 presented a paper at the April SAE meeting in Washington, DC. He estimated 

that DRLs reduced multiple vehicle collisions by 2.3 percent to 12.4 percent, depending on DRL type.

Table 1 summarizes findings from studies of the effectiveness of DRLs in several countries, including the 

U.S. The individual studies are identified by year, investigator(s), the type of study, i.e., did the study 

analyze the effects of DRLs on a specific fleet of vehicles, a case controlled study, or the result of a 

change in the law, applicable country, and the estimated effects of DRLs.

Table 1

Summary of Findings on DRL Effectiveness*

 

 

15Hollo, P., Changes in the Legislation on the Use of Daytime Running Lights by Motor Vehicles and Their Effect 

on Road Safety in Hungary, Accid. Anal. And Prev., Volume 30, No.2, pp 183-199, 1998.

 

16Tessmer, J.M., A Preliminary Assessment of the Crash-Reducing Effectiveness of Passenger Car Daytime 

Running Lamps (DRLs) ; DOT HS 808 645, June 2000.

 

17 Lau, E. Daytime Running Light Effectiveness A Preliminary Evaluation, Presented at a Government/Industry 

Meeting, June 19-21, 2000 Washington, DC.

 

18 Farmer, C.M. and Williams, A.F. Effects of daytime running lights on multiple-vehicle daylight crashes in the 

United States; Accid. Anal. And Prev., Volume 34, pp 197-203, 2002.

 

19Thompson, P.A., Daytime Running Lamps (DRLs) for Pedestrian Protection SAE Paper 2003-0102072, April 2003.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 7

Year Investigator(s) Study

Type Country Estimated Effects

1972 Anderson et al1 Law Finland 27% reduction rural multi-vehicle

1975 Attwood3Fleet Canada 20% some defense vehicles

1977 Anderson et al4 Law Sweden 9% to 21% crash type dependent

1985 Stein8Fleet U.S. 7% reduction selected vehicles

1988 Elvik9 Law Norway 15% reduction summer multi-vehicle

1993 Arora et al10 Law Canada 11.3% reduction 2-vehicle opposite-direction

1993 Hansen11 Law Denmark up to 37% reduction - crash type dependent

1995 Hollo12 Law Hungary 7% to 14% reduction frontal cross traffic

1997 Tofflemire et al14 Law Canada 5.3% reduction opposite direction/angle crashes

1998 Hollo15 Law Hungary 4.7% to 15.2% reduction frontal cross traffic

2000 Tessmer16 CC U.S. -8% to 29% crash type dependent

2000 Lau17 CC U. S. 5% to 13% reduction multiple vehicle crashes

2002 Farmer et al18 CC U.S. 3.2% decline in mult. vehicle daylight crashes

2003 Thompson19 CC U.S. 2.3% to 12.4% DRL type dependent

* See Bibliography for detailed information on published studies.

Several factors could influence the effectiveness of DRLs, e.g., geography and the climate, the mix of 

rural and urban crashes, traffic conditions, and manner of collision. The approach of this study attempts 

to limit the influence of such exogenous variables by using comparison groups where the effects should 

be similar. This study examines the effectiveness of DRLs in the U.S. for vehicles of model years 1995 

and later. Two sources of data maintained by the National Highway Safety Traffic Administration 

(NHTSA) are used to study DRL effectiveness: the Fatality Analysis Reporting System (FARS) and the 

National Automotive Sampling System/ General Estimates System (NASS/GES). 

Methodological Changes from Preliminary Assessment

This study is the second study conducted by NHTSA to determine the effectiveness of Daytime Running 

Lamps (DRLs). The same basic statistical techniques to evaluate DRLs have been used. However, with 

the collection of additional data and the knowledge gained from NHTSA’s first study, A Preliminary 

Assessment of the Crash-Reducing Effectiveness of Passenger Car Daytime Running Lamps (DRLs), 

which appeared in 2000, several improvements have been made. A great deal was learned about using 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 8

national traffic crash data to analyze DRLs, which guided our efforts in the current study.

In the original study two comparison groups of fatal crashes were used, single vehicle fatal crashes and 

2-vehicle same direction fatal crashes. There are many more single vehicle fatal crashes than 2-vehicle 

same direction fatal crashes. The results of the analysis based on using the 2-vehicle same direction fatal 

crashes do not produce sufficient power to reject the null hypothesis. Critics of the earlier study pointed 

out that in same direction crashes, a potential striking vehicle with DRLs could have the DRLs detected 

in the rear view mirror of the potentially stuck vehicle, which could then take corrective action. They 

argue that same direction crashes are not independent of DRLs and using them, as a comparison group 

would skew the results. For these two reasons, analysis using 2-vehicle same direction fatal comparison 

crashes has been eliminated from this study.

In the original study, both the simple odds, O = TD/(CD+TN+CN)20, and the odds ratio,

? = (TD/CD)/(TN/CN)1, were used in the analysis. The standard error of the odds ratio is much larger 

than the standard error of the simple odds. To be statistically precise, when using the simple odds, the 

null hypothesis can be marginally rejected, however, the power of the odds ratio is not sufficient to 

reject the null hypothesis. Therefore the analysis in the main body of this report was based solely on the 

simple odds. Several reviewers of the report required publication of the non-statistically significant 

results, based on the odds ratio. The results based on the odds ratio can be found in Appendix B of 

this report. Generalized forms of the simple odds and the odds ratio were also used in this study; see 

the appendices. A generalized form of the ratios allows one to adjust for a variety of identifiable factors 

such as vehicle type.

Target vehicles with DRLs and the comparison vehicles without DRLs have been partitioned in a 

different way. In the original study two groups of comparison passenger cars were used. The groups of 

target and comparison vehicles were identified by make and model. The original study’s first 

comparison group consisted of vehicles of the same make and model prior to the adoption of DRLs. 

Vehicles in this comparison group were from 1 to 6 years older than the target vehicles equipped with 

DRLs. To eliminate the potential bias due to age in the original study a second group of comparison 

vehicles was selected, namely vehicles manufactured by the Ford Motor Company at the same time that 

the target vehicles were manufactured.

In the current study, the vehicles under analysis have been expanded from passenger cars to passenger 

vehicles. Both the target and comparison vehicles have been identified by analysis of the vehicle 

identification number (VIN). Target and comparison vehicles were all manufactured during the same 

time period. All passenger vehicles that could be classified as having DRLs as standard equipment were 

classified as target vehicles. All passenger vehicles that did not have DRLs as standard equipment nor 

as a standard option were included as comparison vehicles.

The effectiveness of DRLs in preventing fatal two-vehicle daytime opposite direction crashes of 

passenger vehicles with motorcycles was examined.

 

20 See Appendix A, Page 19 for additional details on the simple odds and odds ratios.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 9

Four states, Florida, Maryland, Missouri and Pennsylvania were used to examine the effectiveness of 

DRLs for non-fatal crashes in the original study. However, one cannot extrapolate the effectiveness of 

DRLs to the nation. To obtain a national estimate, data from the General Estimates System (GES) was 

used. Since GES is a survey and not a census of crashes, software for the statistical analysis of 

correlated data, SUDAAN, was used to obtain credible estimates of statistical significance.

Finally a meta-analysis was used in the original study to attempt to provide an overall estimate of DRL 

effectiveness. This has been eliminated from the current analysis since the survey data was used to 

estimate the effects of DRLs for non-fatal crashes. The mixture of survey data and census data in a 

meta-analysis does not provide reliable results.

Data and Methodology

Previous studies of DRL effectiveness often have used a before vs. after approach. This approach is 

appropriate, for example, when a law goes into effect at a given point in time and one wishes to 

determine the effect of that law on traffic crashes. A case-control method was chosen as the approach 

for this study, since only specific make-models for each year were equipped with DRLs. A casecontrol method attempts to control for factors, other than the presence or absence of DRLs that could 

be associated with crash occurrence. In this study, the number of crashes for a fleet of vehicles 

equipped with DRLs is compared to a fleet of vehicles without DRLs produced in the same years. Both 

groups of vehicles are analyzed by time of day and crash type. Analysis of the Vehicle Identification 

Number (VIN) was used to partition passenger cars, vans, pickups/light trucks, and sport utility vehicles 

(SUVs) into a fleet of vehicles that did and did not have DRLs. Passenger vehicles that permitted 

DRLs as a standard option were removed from the analysis, since one could not analyze the VIN to 

determine if the specific vehicle was or was not equipped with DRLs.

Data from FARS21 for calendar years 1995 - 2001 were used to examine DRL effectiveness for fatal 

two-vehicle opposite-direction crashes and for single-vehicle pedestrian/cyclist crashes. NASS/GES22

data for calendar years 1995 – 2001 were used to examine DRL effectiveness for non-fatal two-vehicle 

opposite-direction crashes.

The analysis focused on the possible effect of DRLs in reducing crashes during daylight or twilight hours, 

as opposed to nighttime hours, when traditional lighting would be in use by all drivers. Therefore, the 

 

 

21 Fatal crash data are from NHTSA’s Fatality Analysis Reporting System (FARS). FARS contains data on a census 

of fatal traffic crashes within the United States and Puerto Rico. A crash must involve a motor vehicle traveling on a 

public roadway and must result in the death of an occupant of a vehicle or a non-motorist within 30 days of the crash to 

be included in FARS.

 22 Non-fatal crash data are from NHTSA’s National Automotive Sampling System/General Estimates System 

(NASS/GES). NASS/GES contains data from a survey of approximately 55,000 weighted traffic crashes across the 

United States. Both injury crashes and property damage only crashes are included.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 10

target time period is daytime, including dawn and dusk, and the comparison time period is night23.

Target crashes and comparison crashes are defined by the crash configuration. Ideally, the only 

difference between daytime target crashes and daytime comparison crashes is that the set of daytime 

target crashes consists of crashes that could be affected by DRLs, while the set of daytime comparison 

crashes consists of crashes that would not be affected by DRLs. A target crash is a crash where the 

DRLs can be seen by the driver of the other crash involved vehicle. A comparison crash is a crash 

involving a single vehicle, where the visibility of DRLs is not relevant.

Neither the FARS nor the NASS/GES databases have a variable that partitions the data exactly into 

target and comparison crashes. Both data sets have variables, which permit one to approximate the 

desired partition. Therefore, it is possible that the partition of target crashes and comparison crashes 

may not be perfect. For example, the geometry of an angle crash might prevent a driver from seeing the 

DRLs of the other vehicle. If angle crashes that cannot be affected by DRLs are included in the set of 

target crashes, the estimated effect of DRLs, using FARS may be underestimated. Since the 

effectiveness is expected to be small, fatal target crashes have been limited to head-on crashes and 

sideswipe opposite direction crashes. Although the glare from DRLs may contribute to a single vehicle 

crash, this is unlikely. However, the data do not have the fidelity to identify such crashes. At night, one 

assumes neither the target crashes nor the comparison crashes should be affected by DRLs. This 

assumption, like all assumptions can be challenged. For example, if a driver of a DRL-equipped vehicle 

does not turn on his head/tail lights at night a crash may result. Again this unlikely set of events is within 

the realm of possibility; however, the available data do not permit one to identify or analyze such 

crashes. Two-vehicle target crashes were further distinguished, for the purposes of this study, by 

focusing on those involving crashes in which the two vehicles were traveling in opposite-directions. 

The FARS and NASS/GES target crashes include head-on and sideswipe opposite direction crashes24. 

 The set of single-vehicle crashes is used as a set of comparison crashes. The comparison groups of 

crashes, ideally, would represent those crashes, which would not be affected by the presence or 

absence of DRLs. In the case of nighttime crashes, it has been pointed out that the use of DRLs may 

cause headlamps to burn out more frequently, contributing to an increase in nighttime crashes. 

However, only early Volkswagen and Volvo vehicles use full intensity lower beam headlamps for DRLs. 

 In addition, all vehicles equipped with DRLs are relatively new, model year 1995 and later, so the 

potential problem of burned out headlamps should be minimal. Hauer (1995) pointed out that singlevehicle crashes might also be affected by DRLs. Namely, two-vehicles on a collision course may detect 

each other earlier due to DRLs. In such a situation, a multi-vehicle crash may be avoided and a single-

 

23 An alternative partition of the light condition would be to exclude all dawn and dusk crashes from the analysis. A

preliminary analysis to calculate the point estimate of DRL effectiveness during dawn and dusk was made. The result 

showed a larger value of DRL effectiveness during dawn and dusk than during the day. However, due to the limited 

number of dawn and dusk crashes, the result was not statistically significant. 

24 Sideswipe opposite direction crashes are two-vehicle crashes with the vehicles moving in opposite directions. The 

initial engagement does not overlap the corner of either vehicle by more than four inches, so that there is no 

significant involvement of the front or rear surface areas. In addition, there is no pocketing of the impact in the 

suspension areas. The impact swipes along the surface of the vehicles parallel to the direction of travel. There is low 

retardation of the force along the surface of the vehicles.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 11

vehicle crash may result. Thus, the two comparison groups, nighttime crashes and single-vehicle crashes 

may not be statistically independent of DRLs, a required theoretical assumption for the analysis 

performed here. However, from a practical point of view, these two groups are as statistically 

independent from the target as is reasonably possible. That is, in general, a two-vehicle oppositedirection crash does not cause, nor does it prevent, a single vehicle crash. Likewise, a single-vehicle 

crash does not cause, nor does it prevent, a two-vehicle crash.

Two-vehicle crashes involving the rear end of one or more vehicles and sideswipe same-direction 

crashes have been eliminated from the study. Two-vehicle rear-end and sideswipe same-direction 

crashes might be meaningful choices for comparison crashes because they share similar vision-related 

causal factors as the target crashes, even though DRLs could play a role as a countermeasure in rear 

end crashes. One problem is that the number of such crashes is much smaller than single vehicle crashes 

and the results would not have enough power to reject the null hypothesis. However, there is another 

argument that although rear-end and same-direction sideswipe crashes are not the intended target of 

DRLs, they are relevant since they draw attention to following vehicles – particularly tailgating vehicles –

where drivers may respond with actions that potentially can increase or decrease the risk of a crash. If 

this is the case, design issues of location, brightness and color may be relevant.

Crashes of three or more vehicles were eliminated from the analysis. The crash geometry can become 

quite complex and vague for crashes of three or more vehicles and the number of such crashes is small. 

It is easy to misclassify such a crash. Therefore, to reduce the possibility of contamination of the 

analysis, all crashes involving three or more vehicles have been eliminated.

Another possible source of contamination, albeit a small one, is crashes involving parked vehicles in a 

fatal crash. To insure a vehicle involved in the crash was not parked, the requirement that a driver was 

present or that the driver had left the scene, was imposed.

The vehicles in the analysis were restricted to passenger vehicles of model year 1995 and later. 

Passenger vehicles include passenger cars, SUVs, light tucks, and vans. The target group of vehicles 

with daytime running lamps and the comparison group of vehicles without daytime running lamps were 

identified by analysis of the Vehicle Identification Number, VIN. Analysis of the VIN partitioned 

vehicles into 4 distinct groups: 1) vehicles that had DRLs as standard equipment, 2) vehicles that did not 

have DRLs as standard equipment nor as a standard option, 

3) vehicles that have DRLs as a standard option, and 4) other vehicles including vehicles where the VIN 

was not reported or could not be decoded.

The target group of vehicles was the group of vehicles with DRLs as standard equipment. The 

comparison group of vehicles was the group of vehicles without DRLs, which did not have DRLs as a 

standard option. Vehicles with DRLs as a standard option and the vehicles in the “other” category were 

eliminated from the analysis.

Caveats
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 12

To analyze the effect of a new vehicle safety device one needs to compare it to vehicles that do not have 

the device and in situations that should and should not be affected by the device. One attempts to 

assure that the respective partition of vehicles and crashes eliminates any lurking variables, but this can 

never be fully guaranteed. The selection and partition of vehicles and crashes were based on the 

analytic judgment. 

DRL Effectiveness in Fatal Two-Vehicle Crashes

The target crashes are two-vehicle crashes where the vehicles are traveling in opposite-directions. The 

target crashes include head-on, and sideswipe opposite direction crashes. Single-vehicle crashes are 

the comparison crashes.

Table 2 shows the cross tabulation of the target and single-vehicle crashes under daytime and nighttime 

conditions for vehicles equipped with DRLs.

Table 2

DRL-Equipped Vehicles in Target and 

Single-Vehicle Fatal Crashes, FARS 1995-2001

Time of Day Target 

Crashes

Single-Vehicle 

Crashes Total

Daytime 2,117 3,360 5,477

Nighttime 1,047 4,573 5,620

Total 3,164 7,933 11,097

Source: NHTSA, NCSA, FARS

Table 3 shows the cross tabulation of the target and single-vehicle crashes under daytime and nighttime 

conditions for the comparison group of vehicles without DRLs.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 13

Table 3

Vehicles w/o DRL in Target and 

Single-Vehicle Fatal Crashes, FARS 1995-2001

Time of Day Target 

Crashes

Single-Vehicle 

Crashes Total

Daytime 6,699 10,058 16,757

Nighttime 3,450 13,413 16,863

Total 10,149 23,471 33,620

Source: NHTSA, NCSA, FARS

DRL Effectiveness in Fatal Two-Vehicle Crashes - Results

The effectiveness, based on the simple odds, of DRLs in preventing two-vehicle opposite direction fatal 

crashes during daylight is estimated to be 5.3 percent with (p = 0.052).

Passenger vehicle type may influence the effectiveness of DRLs. To examine this issue, vehicle types 

were included in the logistic fit of the data. The results are similar. The effectiveness, based on the 

simple odds, of DRLs in preventing two-vehicle opposite direction crashes during daylight is estimated 

to be 5.1 percent with (p = 0.061) when adjusting for vehicle type.

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes

The target crashes are two-vehicle crashes where the vehicles are traveling in opposite-directions. 

Single-vehicle crashes are the comparison crashes.

Table 4 shows the cross tabulation of the target and single-vehicle non-fatal crashes under daytime and 

nighttime conditions for vehicles equipped with DRLs. Since NASS/GES is a complex sample survey a 

program such as SUDAAN must be used to estimate the level of significance of the parameters.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 14

Table 4

DRL-Equipped Vehicles in Target and 

Single -Vehicle Non-Fatal Crashes, NASS/GES 1995-2001

Time of Day Target 

Crashes

Single-Vehicle 

Crashes Total

Daytime 972,000 248,000 1,220,000

Nighttime 215,000 216,000 432,000

Total25 1,188,000 464,000 1,652,000

Source: NHTSA, NCSA, NASS/GES

Table 5 shows the cross tabulation of the target and single-vehicle non-fatal crashes under daytime and 

nighttime conditions for the comparison group of vehicles without DRLs.

Table 5

Vehicles w/o DRL in Target and 

Single-Vehicle Non-Fatal Crashes, NASS/GES 1995-2001

Time of Day Target 

Crashes

Single-Vehicle 

Crashes Total

Daytime 3,074,000 737,000 3,812,000

Nighttime 695,000 608,000 1,303,000

Total24 3,770,000 1,345,000 5,115,000

Source: NHTSA, NCSA, NASS/GES

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes - Results

The effectiveness, based on the simple odds, of DRLs in preventing two-vehicle opposite direction nonfatal crashes during daylight is estimated to be 5.2 percent with (p = 0.075).

Passenger vehicle type may influence the effectiveness of DRLs. To examine this issue, vehicle types 

were included in the logistic fit of the data. The results are similar. The effectiveness of DRLs in 

preventing two-vehicle opposite direction non-fatal crashes during daylight is estimated to be 4.4 

percent with (p = 0.133) when adjusting for vehicle type. Since the value of p is greater than 0.1, the 

null hypothesis cannot be rejected. However, since this estimate of effectiveness is similar to the 

significantly significant value calculated without adjusting for vehicle type, with (p = 0.075) one could 

interpret this estimate as a weak confirmation of the previous result.

 

25 Totals may not add due to rounding.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 15

DRL Effectiveness in Fatal Single-Vehicle Pedestrian/Cyclist Crashes

As daytime running lamps reduce two-vehicle opposite-direction crashes, daytime running lamps may 

also reduce single-vehicle crashes with pedestrians or cyclists. To answer that question, one can modify 

the approach used above. FARS, 1995 to 2001, can again be used for this analysis. However, the 

analysis is performed at the person level, rather than the vehicle level26. The target group of persons is 

fatally injured pedestrians and cyclists in single vehicle crashes; the comparison group of persons is 

fatally injured occupants in single vehicle crashes. The target time period is daytime, including dawn and 

dusk and the comparison time period is night. The results follow:

Table 6

Single-vehicle Pedestrian and Cyclist Fatalities FARS 1995-2001

Vehicles Equipped with DRLs

Time of Day

Pedestrian 

and Cyclist

Deaths

Occupant 

Deaths Total

Daytime 710 6,288 6,998

Nighttime 1,153 8,136 9,289

Total 1,863 14,424 16,287

Source: NHTSA, NCSA, FARS

Table 7

Single-vehicle Pedestrian and Cyclist Fatalities FARS 1995-2001

Vehicles Not Equipped with DRLs

Time of Day

Pedestrian 

and Cyclist 

Deaths

Occupant 

Deaths Total

Daytime 2,515 19,540 22,055

Nighttime 3,876 24,946 28,822

Total 6,391 44,486 50,877

Source: NHTSA, NCSA, FARS

 

26 It is possible for a pedestrian fatality and an occupant fatality to occur in the same crash. In this case, both the 

pedestrian and cyclist death cell and occupant death cell are incremented. To avoid potential single vehicle crashes 

involving a pedestrian/cyclist death and an occupant death from confounding the data, this analysis is performed at 

the person level, not the crash level.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 16

DRL Effectiveness in Fatal Single-Vehicle Pedestrian/Cyclist Crashes - Results

The effectiveness, based on the simple odds, of DRLs in preventing single-vehicle pedestrian/cyclist 

fatalities during daylight is estimated to be 12.4 percent with (p = 0.002).

Passenger vehicle type may influence the effectiveness of DRLs. To examine this issue, vehicle types 

were included in the logistic fit of the data. The results are similar. The effectiveness, based on the 

simple odds, of DRLs in preventing single-vehicle pedestrian/cyclist fatal crashes during daylight is 

estimated to be 12.9 percent with (p = 0.002) when adjusting for vehicle type.

DRL Effectiveness in Fatal Crashes of a Passenger Vehicle with a Motorcycle

Target crashes are two-vehicle opposite direction crashes between a passenger vehicle and a 

motorcycle. Comparison crashes are single vehicle crashes. In the analysis that follows, the DRL status 

of the passenger vehicle involved in a two-vehicle crash with a motorcycle determined if the crash was a 

DRL equipped crash or a non-DRL equipped crash.

Table 8 shows the cross tabulation of the target and single passenger vehicle crashes under daytime and 

nighttime conditions for passenger vehicles equipped with DRLs.

Table 8

Passenger Vehicles with DRLs Involved in Fatal 2-Vehicle Crashes of a Motorcycle and 

a Single Passenger Vehicle, FARS 1995-2001

Time of Day

Target

2-Vehicle

Motorcycle

Crashes

Single 

Passenger

Vehicle 

Crashes

Total

Daytime 62 3,360 3,422

Nighttime 30 4,573 4,603

Total 92 7,933 8,025

Source: NHTSA, NCSA, FARS

Table 9 shows the cross tabulation of the target and single passenger vehicle crashes under daytime and 

nighttime conditions for the comparison group of passenger vehicles without DRLs.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 17

Table 9

Passenger Vehicles w/o DRLs Involved in Fatal 2-Vehicle Crashes of a Motorcycle and

a Single Passenger Vehicle, FARS 1995-2001

Time of Day

Target

2-Vehicle

Motorcycle

 Crashes

Single

Passenger

Vehicle 

Crashes

Total

Daytime 239 10,058 10,297

Nighttime 86 13,413 13,499

Total 325 23,471 23,796

Source: NHTSA, NCSA, FARS

DRL Effectiveness in Fatal Crashes of a Passenger Vehicle with a Motorcycle - Results

The effectiveness, based on the simple odds, of DRLs in preventing two-vehicle opposite direction 

crashes between a passenger vehicle and a motorcycle during daylight is estimated to be 23.2 percent 

with (p = 0.065).

Passenger vehicle type may influence the effectiveness of DRLs. To examine this issue, vehicle types 

were included in the logistic fit of the data. The results are similar. The effectiveness, based on the 

simple odds, of DRLs in preventing two-vehicle opposite direction crashes between a passenger vehicle 

and a motorcycle during daylight is estimated to be 22.6 percent with (p = 0.074) when adjusting for 

vehicle type.

Conclusions

The effectiveness of daytime running lamps, based on the simple odds, was analyzed in the preceding 

sections using data from FARS and NASS/GES from calendar years 1995 to 2001. FARS and 

NASS/GES data show that during the period of the study 1995 to 2001, DRLs reduced daylight two 

passenger vehicle opposite-direction crashes by about 5 percent. DRLs have also been shown to 

reduce fatal opposite direction crashes between a motorcycle and a passenger vehicle by 23 percent. 

The results for two-vehicle daytime opposite-direction crashes are statistically significant at the p < 0.10 

level, although one would prefer a statistical level of

p < 0.05. 

FARS data were also used to estimate the effectiveness, based on the simple odds, of DRLs in reducing 

pedestrian/cyclist fatalities in single-vehicle fatal crashes. The analysis shows that DRLs reduced 

pedestrian/cyclist fatalities by more than 12 percent. These results are highly significant at a statistical 

level of p = 0.002.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 18

This analysis is based on US historical data and does not reflect what will happen in the future. 

The techniques used do not predict the crash reducing effectiveness of DRLs if the entire fleet is 

equipped with DRLs nor if drivers become habituated to DRLs. These are limitations of historical crash 

data.

As additional data become available it may be appropriate to further investigate the effectiveness of 

DRLs in a variety of crash configurations including pedestrian and motorcycle crashes.
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 19

Appendix A

Analytic Approach

The primary analytic approach used to estimate the effectiveness, E, of daytime running lamps is based 

on the generalized simple odds. The effectiveness, based on the simple odds approach, is defined as:

E =1- eß

Where ß is the coefficient of the following equation:

TC_DT = ß*DRL + Si ?i *Xi + error

Where: TC_DT = 1 if the crash is a target crash that occurred during the day, 0 otherwise and

DRL = 0 if the vehicle has DRLs, otherwise 1. A bivariate logistic fit of the data is 

calculated using a maximum likelihood estimate. FARS data can be analyzed using SAS®, however, 

since NASS/GES data come from a complex survey rather than a census, SUDAAN had to be used to 

estimate the variance and significance of the estimated coefficients.

In the event that one does not need to control for variables such as vehicle type, the Xi terms are zero 

and an arithmetic approach to calculate the effectiveness exists. In this case, the effectiveness, E is 

equivalent to:

E = 1-(ODRL/OCMP)

Where 

O = TD/(CD+TN+CN)

and is evaluated for both the vehicles equipped with DRLs, ODRL, and the vehicles in the comparison 

group without DRLs, OCMP.

TD is the number of vehicles/persons in Targeted crashes during Daylight.

CD is the number of vehicles/persons in Comparison crashes during Daylight.

TN is the number of vehicles/persons in Targeted crashes at Night.

CN is the number of vehicles/persons in Comparison crashes at Night. 

In this simplified case, for FARS data, the variance of ln (1-E), can be estimated as the sum of the 

squared of the reciprocals of the four groups of observations. That is:
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 20

VAR [ln(1-E)]  ̃ [1/TDDRL]

2

 +[1/(CDDRL + TNDRL + CNDRL)]2 + 

[1/TDCMP]

2

 +[1/(CDCMP + TNCMP + CNCMP)]2

This technique to estimate the variance of the ln(1-E) does not apply to weighted survey data, which 

requires complex software such as SUDAAN.

Logistic Regression Estimates Using the Simple Odds

Note that, with the exception of Table A-4, the value of p for the coefficient of DRL is < 0.1.

Table A-1

DRL Effectiveness in Fatal Two-Vehicle Crashes Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 1.4450 0.0242 3,577.07 <0.0001

DRL 0.947 -0.0541 0.0278 3.79 0.0515

Source: NHTSA, NCSA, FARS, SAS®

Table A-2

DRL Effectiveness in Fatal Two-Vehicle Crashes / Adjusted for Vehicle Type

Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 1.4902 0.0270 3,053.61 < 0.0001

DRL 0.949 -0.0523 0.0279 3.52 0.0606

Sport Utility 1.211 0.1917 0.0356 29.01 <0.0001

Van 0.475 -0.2938 0.0442 44.11 <0.0001

Light Trucks 0.817 -0.2025 0.0283 51.10 <0.0001

Source: NHTSA, NCSA, FARS, SAS®
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 21

Table A-3

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A -0.3579 0.05 3.33 0.0751

DRL 0.948 -0.0529 0.03 3.33 0.0751

Source: NHTSA, NCSA, NASS/GES, SUDAAN

Table A-4

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes / Adjusted for Vehicle Type

Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A -0.3779 0.13 3.74 0.0017

DRL 0.956 -0.0445 0.03 2.34 0.1333

Sport Utility 1.099 0.0941 0.05 4.30 0.0441

Van 0.826 -0.1906 0.08 6.13 0.0173

Light Trucks 1.089 0.0856 0.05 3.53 0.0672

Source: NHTSA, NCSA, NASS/GES, SUDAAN

Table A-5

DRL Effectiveness in Fatal Single-Vehicle – Pedestrian/Cyclist Crashes

Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 3.0883 0.0384 6,476.45 <0.0001

DRL 0.876 -0.1318 0.0435 9.19 0.0024

Source: NHTSA, NCSA, FARS, SAS®

Table A-6

DRL Effectiveness in Fatal Single-Vehicle – Pedestrian/Cyclist Crashes

Adjusted for Vehicle Type Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 3.1449 0.0427 5,424.56 <0.0001

DRL 0.871 -0.1377 0.0437 9.94 0.0016

Sport Utility 1.231 0.2082 0.0527 15.58 <0.0001

Van 0.812 -0.2086 0.0586 12.69 0.0004
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 22

Light Trucks 0.752 -0.2853 0.0445 41.08 <0.0001

Source: NHTSA, NCSA, FARS, SAS®

Table A-7

DRL Effectiveness for Two-Vehicle Fatal Crashes

Involving a Motorcycle and a Passenger Vehicle Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 4.8552 0.1275 1,450.52 <0.0001

DRL 0.768 -0.2645 0.1431 3.42 0.0645

Source: NHTSA, NCSA, FARS, SAS®

Table A-8

DRL Effectiveness for Two-Vehicle Fatal Crashes

Involving a Motorcycle and a Passenger Vehicle Adjusted for Vehicle Type

Based on Simple Odds

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 5.1603 0.1478 1,224.38 <0.0001

DRL .0774 -0.2564 0.1436 3.19 0.0741

Sport Utility 0.627 -0.4664 0.1572 8.80 0.0030

Van 0.499 -0.6960 0.2044 11.60 0.0007

Light Trucks 0.557 -0.5846 0.1408 17.25 <0.0001

Source: NHTSA, NCSA, FARS, SAS®
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 23

Appendix B

Alternate Analytic Approach

This section is included at the request of the reviewers of the paper. The odds ratio is easier to 

understand for inexperienced analysts than the simple odds and, like the simple odds, attempts to 

control for a variety of factors other than the presence or absence of DRLs. Unfortunately, when using 

the odds ratio, the estimated effectiveness of DRLs is extremely sensitive to small changes encountered 

in real world crash data and none of the results were statistically significant. This does not mean that 

DRLs do not reduce target crashes during the daytime. It just means that the odds ratio technique does 

not detect these changes over the inherent background noise of the data system. 

The effectiveness, based on the odds ratio, is defined as:

E =1- eß

Where ß is the coefficient of the following equation:

LIGHT = ß*(DRL x CRASH) + a1*DRL + a2*CRASH + Si ?i*Xi + error

Where: LIGHT = 1 if the crash occurred during the day, 0 otherwise

DRL = 0 if the vehicle has DRLs, otherwise 1.

CRASH =1 if the crash is a target crash, and 0 if the crash is a comparison crash.

A bivariate logistic fit of the data is calculated using a maximum likelihood estimate. FARS data can be 

analyzed using SAS®, however, since NASS/GES data come from a complex survey rather than a 

census, SUDAAN had to be used to estimate the variance and significance of the estimated coefficients.

In the event that one does not need to control for variables such as vehicle type, the Xi terms are zero 

and an arithmetic approach to calculate the effectiveness exists. In this case, the effectiveness, E is 

equivalent to:

E = 1-(? DRL/? CMP)

Where 

? = (TD/CD)/(TN/CN)

and is evaluated for both the vehicles equipped with DRLs, ? DRL, and the vehicles in the comparison 

group without DRLs, ? CMP.

In this simplified case, for FARS data, the variance of ln (1-E), can be estimated as the sum of the 

squares of the reciprocals of the eight groups of observations. That is:
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 24

VAR [ln(1-E)]  ̃ [1/TDDRL]

2

 + [1/CDDRL]

2

 + [1/TNDRL]

2

 + [1/CNDRL]

2

 + 

 [1/TDCMP]

2

 + [1/CDCMP]

2 + [1/TNCMP]2 + [1/CNCMP]2

Note that VAR [ln(1-E)] is much larger for the odds ratio than for the simple odds. As a result, the 

values of p, for each of the evaluated crash types in this study, are larger than 0.1, therefore the null 

hypothesis cannot be rejected and there is no reason to believe that the results, based on the odds ratio, 

did not occur by chance.

Using the data of Tables 2 and 3, the estimates of effectiveness of DRLs are calculated using the odds 

ratio. The result for two-vehicle opposite direction fatal crashes is –6.3 percent with 

(p=0.229). When adjusting for vehicle type, the result is –6.3 percent with (p=0.235). The values of p 

in both cases are larger than 0.1, therefore the null hypothesis cannot be rejected and there is no reason 

to believe that the results, based on the odds ratio, did not occur by chance. 

Using the data of Tables 4 and 5, the estimates of effectiveness of DRLs are calculated using the odds 

ratio. The result for two-vehicle opposite direction non-fatal crashes is –7.9 percent with (p=0.186). 

When adjusting for vehicle type, the result is –7.6 percent with (p=0.202). The values of p in both 

cases are larger than 0.1, therefore the null hypothesis cannot be rejected and there is no reason to 

believe that the results, based on the odds ratio, did not occur by chance. 

Using the data of Tables 6 and 7, the estimates of effectiveness of DRLs are calculated using the odds 

ratio. The result for fatal single-vehicle pedestrian/cyclist crashes is 3.8 percent with 

(p=0.498). When adjusting for vehicle type, the result is 4.6 percent with (p=0.415). The values of p in 

both cases are larger than 0.1, therefore the null hypothesis cannot be rejected and there is no reason to 

believe that these results, based on the odds ratio, did not occur by chance. 

Using the data of Tables 8 and 9, the estimates of effectiveness of DRLs are calculated using the odds 

ratio. The result for crashes of a passenger vehicle with a motorcycle is 26.0 percent with 

(p=0.284). When adjusting for vehicle type, the result is 22.0 percent with (p=0.335). The values of p 

in both cases are larger than 0.1, therefore the null hypothesis cannot be rejected and there is no reason 

to believe that the results, based on the odds ratio, did not occur by chance. 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 25

Logistic Regression Estimates Using the Odds Ratio

Note that the value of p for the coefficient of DRLxCRASH is always larger than 0.1. Therefore the 

null hypothesis cannot be rejected and the estimates, based on the odds ratio, do not improve our 

understanding of the effectiveness of DRLs. However, if the estimate of effectiveness is larger than 20 

percent, the estimates, based on the odds ratio, are similar to the estimates calculated using the simple 

odds, albeit not statistically significant.

Table B-1

DRL Effectiveness in Fatal Two-Vehicle Crashes Based on Odds Ratio

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.3082 0.227 184.01 <0.0001

DRLxCRASH 1.063 0.0608 0.0506 1.45 0.2291

DRL 0.980 -0.0204 0.0263 0.60 0.4381

CRASH 0.363 0.3082 0.0441 527.08 <0.0001

Source: NHTSA, NCSA, FARS, SAS®

Table B-2

DRL Effectiveness in Fatal Two-Vehicle Crashes / Adjusted for Vehicle Type

Based on Odds Ratio

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.4052 0.0247 269.55 < 0.0001

DRLxCRASH 1.063 0.0606 0.0507 1.43 0.2315

DRL 0.998 -0.0019 0.0264 0.01 0.9435

CRASH 0.361 -1.0193 0.0442 531.82 <0.0001

Sport Utility 0.738 -0.3036 0.0273 123.75 <0.0001

Van 0.604 -0.5044 0.0689 167.83 <0.0001

Light Trucks 0.920 -0.0836 0.0237 12.43 <0.0004

Source: NHTSA, NCSA, FARS, SAS®
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 26

Table B-3

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes Based on Odds Ratio

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A -0.1343 0.08 222.88 <0.0001

DRLxCRASH 1.079 0.0763 0.06 1.81 0.1259

DRL 0.944 -0.0574 0.05 1.56 0.2184

CRASH 0.253 -1.3725 0.07 383.31 <0.0001

Source: NHTSA, NCSA, NASS/GES, SUDAAN

Table B-4

DRL Effectiveness in Non-Fatal Two-Vehicle Crashes / Adjusted for Vehicle Type

Based on Odds Ratio

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A -0.0957 0.08 141.85 <0.0000

DRLxCRASH 1.076 0.0735 0.06 1.68 0.2024

DRL -0.954 -0.0467 0.05 0.97 0.3294

CRASH 0.253 -1.3728 0.07 387.03 <0.0000

Sport Utility 0.921 -0.0825 0.04 5.20 0.0276

Van 0.718 -0.3320 0.06 33.31 <0.0000

Light Trucks 1.089 0.0856 0.03 5.05 0.0237

Source: NHTSA, NCSA, NASS/GES, SUDAAN
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 27

Table B-5

DRL Effectiveness in Fatal Single-Vehicle – Pedestrian/Cyclist Crashes

Based on Odds Ratio

Parameter Odds Ratio Estimate Standard 

Error

Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.2577 0.0168 235.46 <0.0001

DRLxPERSON 0.962 -0.0389 0.0575 0.46 0.4984

DRL 0.987 -0.0134 0.0193 0.42 0.4877

PERSON 0.876 -0.1318 0.0435 9.19 0.0024

Source: NHTSA, NCSA, FARS, SAS®

Table B-6

DRL Effectiveness in Fatal Single-Vehicle – Pedestrian/Cyclist Crashes

Adjusted for Vehicle Type Based on Odds Ratio

Parameter Odds Ratio Estimate Standard 

Error

Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.4387 0.0186 554.10 <0.0001

DRLxPERSON 0.954 -0.0472 0.0579 0.66 0.4153

DRL 1.031 0.0309 0.0195 2.51 0.1133

PERSON 1.238 0.2137 0.0509 17.62 <0.0001

Sport Utility 0.619 -0.4790 0.0208 532.62 <0.0001

Van 0.461 -0.7749 0.0269 831.38 <0.0001

Light Trucks 0.846 -0.1667 0.0206 65.46 <0.0001

Source: NHTSA, NCSA, FARS, SAS®
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 28

Table B-7

DRL Effectiveness for Two-Vehicle Fatal Crashes

Involving a Motorcycle and a Passenger Vehicle Based on Odds Ratio

Parameter Odds Ratio Estimate Standard 

Error

Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.3082 0.0227 184.01 <0.0001

DRLxCRASH 0.760 -0.2851 0.2568 1.15 0.2842

DRL 0.980 -0.0204 0.0263 0.60 0.4381

CRASH 0.356 -1.0341 0.2236 21.40 <0.0001

Source: NHTSA, NCSA, FARS, SAS®

Table B-8

DRL Effectiveness for Two-Vehicle Fatal Crashes

Involving a Motorcycle and a Passenger Vehicle Adjusted for Vehicle Type 

Based on Odds Ratio

Parameter Odds Ratio Estimate Standard Error Wald

Chi-Square

Pr > ChiSq

p

Intercept N/A 0.4268 0.0254 282.23 <0.0001

DRLxCRASH 0.780 -0.2485 0.2577 0.93 0.3349

DRL 1.003 0.0030 0.0264 0.01 0.9087

CRASH 0.355 -1.0363 0.2242 21.36 <0.0001

Sport Utility 0.691 -0.3695 0.0309 143.26 <0.0001

Van 0.529 -0.6362 0.0460 190.93 <0.0001

Light Trucks 0.905 -0.0993 0.0283 12.27 0.0005

Source: NHTSA, NCSA, FARS, SAS®
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 29

Appendix C

The following SAS® code was used to partition FARS 1996 vehicle crashes. The code for the 

NASS/GES is similar.

/* COMPARISON CRASHES SINGLE VEHICLE CRASHES */

LIBNAME FARS96 'L:\FARSSAS\FARS96';

DATA CRASH;

 SET FARS96.ACCIDENT(KEEP = ST_CASE LGT_COND VE_FORMS MAN_COLL 

WEATHER);

LENGTH TGT_CRSH $8;

* IF TWO VEHICELES CRASH AND;

* HEAD-ON OR SIDESWIPE DIFFERENT DIRECTIONS;

IF (VE_FORMS EQ 2) AND

((2 EQ MAN_COLL) OR (6 EQ MAN_COLL))

THEN TGT_CRSH ='MUL TGT';

/* ELSE SINGLE VEHICLE CRASHES */

ELSE IF (VE_FORMS EQ 1) THEN TGT_CRSH = 'SINGLE';

ELSE DELETE;

*DEFINE THE DICHOTOMOUS VARIABLE D_CRASH;

IF (VE_FORMS EQ 2) AND

((2 EQ MAN_COLL) OR (6 EQ MAN_COLL))

THEN D_CRASH = 1;

/* ELSE SINGLE VEHICLE CRASHES */

ELSE IF (VE_FORMS EQ 1) THEN D_CRASH = 0;

ELSE DELETE;

LENGTH LIGHT $7;

*IF DAYLIGHT DAWN OR DUSK;

IF (LGT_COND EQ 1 OR 4 LE LGT_COND LE 5) THEN LIGHT = 'DAYTIME';

*IF DARK OR DARK AND LIGHTED;

ELSE IF (2 LE LGT_COND LE 3) THEN LIGHT = 'NIGHT'; 
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 30

ELSE DELETE;

* DEFINE THE DICHOTOMOUS VARIABLE D_LIGHT;

IF (LGT_COND EQ 1 OR 4 LE LGT_COND LE 5) THEN D_LIGHT = 1;

ELSE IF (2 LE LGT_COND LE 3) THEN D_LIGHT = 0; 

* DEFINE THE DICHOTOMOUS VARIABLE MUL_DAY;

* THIS IS FOR THE SIMPLE ODDS CALCULATION;

IF (D_CRASH = 1 AND D_LIGHT = 1) THEN MUL_DAY =1;

ELSE MUL_DAY = 0;
 National Center for Statistics and Analysis ♦ 400 Seventh St., S.W., Washington, D.C. 20590 31

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