An Analysis Tool for 

Making Informed 

Safety Decisions

SUMMARY REPORT

Safety Evaluation Of Discontinuing 

Late-Night Flash Operations at 

Signalized Intersections

Background

During late-night flash (LNF) mode (from late night to early morning hours), traffic signals flash 

yellow for one road (typically, the major road), requiring caution but no stopping, and flash red for 

the other road (typically, the minor road), requiring drivers to stop and then proceed through the 

intersection after yielding to the traffic on the major road. The intent of LNF is to reduce energy 

consumption and delay during periods of low traffic demand. However, in recent years, many 

agencies have begun replacing LNF with normal phasing operation because of safety concerns.

The safety impacts of replacing LNF with normal phasing operation have been studied since the 

1980s. Gaberty and Barbaresso analyzed crash data at 59 four-leg intersections in Oakland County, 

MI, where the nighttime flash mode was replaced with normal phasing operation.(1) Results indicated 

a 91-percent reduction in angle crashes and a 95-percent reduction in injury right-angle crashes. 

However, it was not clear whether high-crash locations were selected for the change and whether the 

results may have been biased due to regression to the mean (RTM). Similarly, Polanis evaluated the 

safety of removing LNF from 19 sites in Winston-Salem, NC, using a naïve before–after method and 

concluded that nighttime right-angle crashes decreased by 78 percent.(2) Srinivasan et al. conducted a 

before–after evaluation of LNF conversion using the empirical Bayes (EB) method based on a small 

sample of 12 intersections in Winston-Salem, NC.(3) The EB method was used to specifically address 

the possible bias due to RTM. The authors concluded that nighttime crashes decreased by 35 percent, 

and nighttime angle crashes decreased by 34 percent. More recently, Murphy conducted an evaluation 

of 67 intersections in North Carolina using a before–after EB method but without using data on 

traffic volumes.(4) Murphy found that for sites where LNF was discontinued, there was a 27-percent 

reduction in nighttime crashes, a 23-percent reduction in injury and fatal crashes, and a 48-percent 

reduction in frontal-impact crashes.(4)

It is clear that while all the previous studies seem to indicate that removing LNF (and replacing it 

with normal phasing operation) will reduce crashes at night, each study had at least one limitation—

possible bias due to RTM was not explicitly addressed, the sample was small, or traffic volumes were 

not considered. The objective of this effort was to evaluate the effect of eliminating LNF operations 

at signalized intersections using state-of-the-art methods and to address the noted limitations. The 

goal was to include an adequate sample of locations for which traffic volume data were available.

Overview of Analysis Methods

For the past two decades, the EB method has been used successfully to conduct before–after evaluations.(5) To evaluate safety treatments with the EB approach, the before period crash experience at 

treated sites is used in conjunction with a negative binomial crash prediction model for untreated 

reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared with the crashes observed after treatment to evaluate the effect of 

the treatment. This approach accounts for possible bias due to RTM that can result from the natural 

tendency to select high-crash locations for treatment.

With the availability of the software package WinBUGS, the Bayesian method, also called the fully 

Bayesian (FB) approach, has been suggested by a few recent studies as a useful alternative to the EB 

approach for the following reasons (see references 6, 7, 8, 9, and 10). The FB approach can be applied 

even if the reference group is limited. The FB approach is also better in accounting for uncertainty 

in the data used and provides more detailed causal inferences and more flexibility in selecting crash 

count distributions. In addition, it can more explicitly account for spatial correlation and correlation 
2

between crash types. FB methods can be implemented in a 

univariate or multivariate form. The multivariate FB method can account for correlation between the crash types that 

are evaluated in a study. This study used both the EB and 

FB before–after evaluation methods to examine the safety 

effect of eliminating LNF operations. Further details about 

the two methods are provided in Lan and Srinivasan.(11)

Data

The North Carolina Department of Transportation (NCDOT) 

provided data for 67 signalized intersections where LNF 

had been removed between 2001 and 2006. (These are the 

same sites that Murphy used in an earlier evaluation.(4)) In 

addition, NCDOT identified 395 intersections that remained 

on LNF operation from 2000 to 2007—this group served as 

reference sites for the evaluation. NCDOT staff extracted 

crash data by crash type and severity for years 2000 to 2007 

for treatment and reference groups from the Traffic Engineering Accident Analysis System (TEAAS). Each crash 

that occurred at the treatment sites was examined closely 

by NCDOT staff to ensure that it was accurately coded. 

Annual average daily traffic (AADT) data were extracted 

from AADT maps by NCDOT staff. NCDOT also provided 

information on the number of legs and area type (rural or 

urban). In addition, Highway Safety Research Center staff 

used Google MapsTM to verify the number of legs for all the 

treatment and reference sites. 

Three intersections that were on freeway ramps were 

excluded because AADT data were not available for at 

least one of the legs. In addition, three other treatment sites 

were excluded because the treatment was implemented in 

2007, and NCDOT did not provide crash data for subsequent years. Finally, 61 treatment sites were used for 

further study. With respect to signal operations at the 

treatment sites in the before period, 32 of them had LNF 

from 11 p.m. to 6 a.m., 20 of them had LNF from midnight 

to 6 a.m., and the other 9 sites had LNF periods sometime 

between 9:10 p.m. and 6:20 a.m. Among the 395 reference 

sites that remained on LNF operation from 2000 to 2007, 

one group (Group 1) had LNF from 11 p.m. to 6 a.m., while 

the other group (Group 2) had LNF from midnight to 6 a.m. 

Total crashes, injury and fatal crashes, and frontal-impact 

crashes in LNF periods were investigated as part of the 

evaluation. Summary statistics for the treatment sites are 

shown in table 1. NCDOT indicated that there were at 

least two possible reasons for removing LNF: (1) policy 

decision—a jurisdiction decides to remove LNF as a 

policy, or (2) crash history during the LNF operation.

Implementation of the EB Method

To estimate the crash modification factor (CMF) associated 

with a treatment using the EB method, the following steps 

are required: 

• Develop reliable safety performance functions (SPF) 

using data from the reference group. Negative binomial (NB) regression is the most common method 

for developing SPFs. The SPF estimating process 

also develops annual calibration factors that are used 

to account for trends such as changes in crash reporting, 

weather, demographics, and vehicle population.

• Obtain the EB estimate of the crashes in the before 

period of the treatment (after correcting for possible 

bias due to RTM) by combining the estimates from 

the SPF with the observed crashes.

• Estimate the EB expected value of the crashes that 

would have occurred in the after period if the treatment had not been implemented.

• Estimate the CMF based on the observed crashes in 

the after period, the estimate of the EB expected value 

of the crashes, and the variance of the EB estimate.

Note: The crash statistics refer to crashes that occurred during 

late night. Nighttime traffic volumes were not available for this 

evaluation.

Table 1. Summary statistics for treated intersections.

VARIABLE MEAN MINIMUM MAXIMUM

Years before 4.46 2 6

Years after 2.13 0.58 4.58

Total crashes/site-year 

before 1.77 0.00 6.75

Total crashes/site-year 

after 0.66 0.00 3.75

Injury and fatal crashes/

site-year before 1.05 0.00 4.75

Injury and fatal crashes/

site-year after 0.30 0.00 2.25

Frontal impact crashes/

site-year before 1.40 0.00 6.50

Frontal Impact crashes/

site-year after 0.35 0.00 3.00

Major road AADT before 22,103 2,550 59,000

Major road AADT after 21,281 3,000 48,500

Minor road AADT before 7,941 1,000 22,375

Minor road AADT before 7,983 1,000 23,333
3

Because the focus is to estimate the safety effects of the 

conversion at night, it is necessary to reliably estimate the 

predicted number of crashes at night. However, the number 

of crashes at night in the reference groups was not sufficient to develop reliable SPFs. In addition, as mentioned 

in the previous section, there was not always a one-to-one 

correspondence between the LNF time period in the treatment sites (before LNF removal) and the LNF period in 

the reference groups. SPFs were first estimated using 24-h 

data from the reference groups and then adjusted to predict 

the number of crashes during the before period (i.e., LNF 

period) in the treatment sites. Adjustment factors were 

estimated based on the percentage of crashes at night in the 

reference and treatment groups, and the time period of the 

LNF in the before period of the treatment groups and the 

reference groups. Further details about the adjustment 

factors are available from Lan and Srinivasan.(11)

Implementation of the FB Method

The FB method could not be applied to four of the sites 

because of missing data in certain years for these sites. 

Hence, the FB method was applied to 57 sites. Unlike 

the EB method, the FB method does not need to use 24-h 

crash data for the evaluation and was applied using only 

the crash data from the nighttime period. The FB method 

was implemented in both univariate and multivariate forms 

using Poisson-gamma and Poisson lognormal models with 

two time effects functions (time trend and time multiplier 

models) to account for trends. In the implementation of 

the FB method, the best model was selected based on the 

deviance information criterion (DIC), which is discussed in 

Spiegelhalter et al.(6,12) In general, the model with a lower 

DIC value is considered better.

Results and Discussion

Table 2 shows the results of the before–after evaluations. 

It is important to note that the CMFs are for crashes that 

are expected to occur during late night conditions when the 

signal is operating in late night flash mode. Results include 

those from (1) the before–after EB evaluation, (2) the best 

models from the univariate FB evaluation, and (3) the best 

models from the multivariate FB evaluation. To directly 

compare the results from the EB and FB methods, the 

results shown are based on the information from the 

57 sites for which the FB method could be applied.

The table shows that the results from the EB method are 

almost identical to the univariate FB method. The EB 

method, however, required data from both day and night 

to estimate the SPFs and adjustment factors to account for 

the differences between the reference and treatment groups. 

The FB method (univariate FB or multivariate FB), on the 

other hand, did not need such information and was more 

straightforward to formulate and implement. Moreover, 

the FB method offers more flexibility in selecting the functional form of the relationship between crash frequency and 

site characteristics, and the ability to address uncertainty 

in the data. Compared with the univariate FB method, the 

multivariate Poisson-log normal (MVPLN) model provided 

better results based on much lower DIC values. Furthermore, the MVPLN model is able to account for the correlation between the crash types. 

For this particular data set, the MVPLN model was favored, 

and the recommended CMFs are 0.52, 0.47, and 0.43 for 

nighttime total, injury and fatal, and frontal-impact crashes, 

respectively. Discontinuing LNF operations results in substantial reductions in crashes during late night periods. 

Future research should investigate the conditions under 

which removing LNF may be more or less beneficial, and 

possibly develop crash modification functions. Data on 

nighttime traffic volumes are needed to develop more 

precise results.

Table 2. Crash modification factors from the before–after 

evaluations.

EVALUATION METHOD

CRASH TYPE

EB—

Univariate

Negative 

Binomial

FB—

Univariate

PoissonGamma 

(DIC=6,773†)

FB—

Multivariate

PoissonLog Normal 

(DIC=6,392)

Total LNF 

crashes/

site-year 

before CMF 

S.E. 

of 

CMF CMF

S.E. 

of 

CMF CMF

S.E. 

of 

CMF

Total 0.60 0.07 0.60 0.07 0.52 0.06

Injury & 

Fatal 0.59 0.10 0.61 0.11 0.47 0.08

Frontal 

Impact 0.52 0.08 0.52 0.08 0.43 0.07

†DIC for the univariate case was the sum of the DIC values 

from the models estimated for the three crash types.

CMF: crash modification factor

S.E.: standard error
AUGUST 2013

FHWA-HRT-13-069

HRDS-20/08-13(750)E McLean, VA 22101-2298 6300 Georgetown Pike Turner-Fairbank Highway Research Center Research, Development, and Technology www.HSISinfo.org 

References

1. Gaberty II, M.J., and J.C. Barbaresso, “A Case Study of the Accident Impacts of Flashing Signal 

Operations Along Roadways,” ITE Journal, 57(7), pp. 27–28, 1987.

2. Polanis, S., “Right-Angle Crashes and Late-Night/Early-Morning Flashing Operation: 19 Case 

Studies,” ITE Journal, 72(4), pp. 26–28, 2002.

3. Srinivasan, R., F. Council, C. Lyon, F. Gross, N. Lefler, and B. Persaud, “Safety Effectiveness of 

Selected Treatments at Urban Signalized Intersections,” Transportation Research Record 2056, 

pp. 70–76, 2008.

4. Murphy, B., “An Evaluation of the Safety Effects of Modifying Late Night Flash Signal Operations 

in North Carolina.” Presented at the 88th Annual Meeting of the Transportation Research Board, 

Washington, DC, 2009.

5. Persaud, B. and C. Lyon, “Empirical Bayes Before–After Safety Studies: Lessons Learned From 

Two Decades of Experience and Future Directions,” Accident Analysis & Prevention, 39(3), 

pp. 546–555, 2007.

6. Spiegelhalter, D., A. Thomas, N. Best, and D. Lunn, WinBUGS Version 1.4 User Manual. MRC 

Biostatistics Unit, Cambridge, 2003. [Available at www.mrc-bsu.cam.ac.uk/bugs/winbugs/

manual14.pdf] 

7. Lan, B., B. Persaud, C. Lyon, and R. Bhim, “Validation of a Full Bayes Methodology for 

Observational Before–After Road Safety Studies and Application to Evaluation of Rural Signal 

Conversions,” Accident Analysis & Prevention, 41(3), pp. 574–580, 2009.

8. Pawlovich, M.D., W. Li, A. Carriquiry, and T. Welch, “Iowa’s Experience with “Road Diet” 

Measures: Impacts on Crash Frequencies and Crash Rates Assessed Following a Bayesian 

Approach,” Transportation Research Record 1953, 163–171, 2006.

9. Ma, J., K.M. Kockelman, and P. Damien, “A Multivariate Poisson-Lognormal Regression Model 

for Prediction of Crash Counts by Severity, Using Bayesian Methods,” Accident Analysis and 

Prevention, 40(3), pp. 964–975, 2008.

10. El-Basyouny, K. and T. Sayed, “Collision Prediction Models Using Multivariate PoissonLognormal Regression,” Accident Analysis and Prevention, 41(4), pp. 820–828, 2009.

11. Lan, B. and R. Srinivasan, “Safety Evaluation of Discontinuing Late Night Flash Operations at 

Signalized Intersections,” Transportation Research Record (in press). Presented at the 92nd 

Annual Meeting of the Transportation Research Board, Washington, DC, 2013.

12. Spiegelhalter, D.J., N.G. Best, B.P. Carlin, and A. Van der Linde, “Bayesian Measures of Model 

Complexity and Fit (With Discussion),” J. Roy. Statist. Soc. B. 64, pp. 583–640, 2002.

For More Information

The research was conducted by Bo Lan and Raghavan Srinivasan, of the University of North Carolina 

Highway Safety Research Center. Further details about the evaluation can be found in “Safety 

Evaluation of Discontinuing Late Night Flash Operations at Signalized Intersections,” which was 

presented at the 2013 Annual Meeting of the Transportation Research Board.(11) For more information 

about HSIS, please contact HSIS program managers Carol Tan, (202) 493-3315, carol.tan@dot.gov, 

or Ana Maria Eigen, (202) 493-3168, ana.eigen@dot.gov, at the FHWA.

The Highway Safety Information System (HSIS) is a safety database that contains crash, roadway inventory, and traffic volume 

data for a select group of States and cities. The participating States of California, Illinois, Maine, Minnesota, North Carolina, Ohio, 

and Washington and the city of Charlotte were selected based on the quality of their data, the range of data available, and their 

ability to merge the data from various files. The HSIS database also contains historic data from Michigan and Utah. The HSIS 

is issued by FHWA staff, contractors, university researchers, and others to study current highway safety issues, direct research 

efforts, and evaluate the effectiveness of crash countermeasures.