Evaluation of Lane Reduction “Road Diet”

Measures and Their Effects on 

Crashes and Injuries

“ROAD DIETS” ARE OFTEN CONVERSIONS OF FOUR-LANE UNDIVIDED ROADS INTO THREE

lanes (two through lanes and a center turn lane), as shown in figure 1. The fourth

lane may be converted to bicycle lanes, sidewalks, and/or on-street parking. In

other words, existing space is reallocated; the overall area remains the same.

Under most average daily

traffic (ADT) conditions

tested, road diets have minimal effects on vehicle

capacity, because left-turning vehicles are moved

into a common two-way

left-turn lane.(1,2) However, for road diets with

ADTs above approximately 20,000 vehicles,

there is a greater likelihood that traffic congestion will increase

to the point of diverting traffic to alternate routes.

Road diets can offer potential benefits to both vehicles and pedestrians.

On a four-lane street, drivers change lanes to pass slower vehicles

(such as vehicles stopped in the left lane waiting to make a left turn).

In contrast, drivers’ speeds on two-lane streets are limited by the

speed of the lead vehicle. Thus, road diets may reduce vehicle speeds

and vehicle interactions during lane changes, which potentially

could reduce the number and severity of vehicle-to-vehicle crashes.

Pedestrians may benefit because they have fewer lanes of traffic to

cross, and because motor vehicles are likely to be moving more

slowly. The Federal Highway Administration (FHWA) report

Safety Effects of Marked vs. Unmarked Crosswalks at Uncontrolled

Locations found that pedestrian crash risk was reduced when

pedestrians crossed two- and three-lane roads, compared to roads

with four or more lanes.(3)

Although road diet advocates enumerate these potential

crash-related benefits, there has been limited research concerning such benefits. This study was designed to help fill

this gap.

The Highway Safety Information System

(HSIS) is a multi-State safety database that

contains crash, roadway inventory, and traffic

volume data for a select group of States. The

participating States, California, Illinois, Maine,

Michigan, Minnesota, North Carolina, Ohio,

Utah, and Washington, were selected based on the

quality of their data, the range of data available,

and their ability to merge the data from the various

files. The HSIS is used by FHWA staff, contractors,

university researchers, and others to study current

highway safety issues, direct research efforts, and 

evaluate the effectiveness of accident countermeasures.

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike • McLean, VA 22101-2296

SUMMARY REPORT

Figure 1. A representative road diet.

1
Databases Used

This study evaluated road diets at several locations in California and Washington.

Because both States are part of FHWA’s Highway Safety Information System (HSIS),

researchers believed that the necessary high-quality crash data for a large number of

crash, roadway, and vehicle variables would be available from computerized HSIS

files. However, because all the road diets were found to be on non-State routes, the

data were collected manually from local agency files.

Research Design

Researchers selected a treatment and comparison group, and obtained data for two

time periods: one before and one after treatment installation. The road diets (i.e.,

treatment sites) were matched with four-lane streets that were otherwise similar

(i.e., comparison sites). Researchers then obtained crash data for four groups: 

1) Road diets—before period. 

2) Road diets—after period. 

3) Comparison sites—before period. 

4) Comparison sites—after period.

Most comparison sites were four-lane undivided roads that were near the

road diets (such as a parallel road one or two blocks away, or a perpendicular road). A few comparison sites were four-lane undivided sections of

the same road beyond the location where the road diet was installed. The

comparison sites were selected to be similar to the road diets in terms of

roadway functional class, type of development (e.g., commercial or residential), speed limit, intersection spacing, and access control. 

Site Selection

Local traffic engineers in California and Washington were contacted to

determine where road diets were located. Road diets were identified in

eight cities: Bellevue, WA, Mountain View, CA, Oakland, CA,

Sacramento, CA, San Francisco, CA, San Leandro, CA, Seattle, WA, and

Sunnyvale, CA. These are not the only cities in California and

Washington that have road diets, but they are the locations that the local

traffic engineers provided.

Researchers identified candidate comparison sites by reviewing maps

and talking to local traffic engineers. The final list of sites contained 30

road diets and 50 total matching comparison sites in 8 cities. Because

of missing crash data, only 12 road diets and 25 comparison sites are

included in this analysis, as described below. The road diet sections

ranged from 0.13–4.09 kilometers (km) (0.08–2.54 miles (mi)). The

comparison sites sections ranged from 0.21–4.88 km (0.13–3.03 mi). 

Data Collection

Local traffic engineers provided crash and ADT data for the road

diets and comparison sites. At most locations, 1 or more years of

data were obtained for each of the before and after periods. The

actual time periods varied considerably from site to site, depending on how much data the city had available and when the road

diet was installed. Because all four seasons were represented,

seasonal variations in crashes were taken into account.

2

Methodology
As noted above, researchers

initially obtained crash data

for 30 road diets and 50 comparison sites. However,

many locations had small

sample sizes of crashes

because of short 

segment lengths, short data

periods, or low ADTs.

Therefore, a subset of 12

road diets (2,068 crashes)

and 25 comparison sites

(8,556 crashes) was chosen

for the analyses that are reported here. These locations generally had segment lengths of at least 0.81

km (0.50 mi). The road diets and comparison sites

were placed into 11 groups, each consisting of 1 or 2

road diets and the matching comparison site(s). The

road diets and comparison sites in each group were

located in the same city, thereby accounting for possible differences in crash-reporting practices among

cities. Table 1 lists the cities, the number of road

diets and comparison sites, and the number of crashes. The before and after analyses were divided into

four categories:

1) Crash frequencies.

2) Crash rates.

3) Crash severity.

4) Crash types.

Before and After Crash Frequencies

This analysis used 10 groups, with 11 road diets

and 24 comparison sites. One road diet and one

comparison site in Seattle, WA, were excluded

because of inconsistent before and after periods.

When researchers pooled data from all 10 groups, a

somewhat higher percent of crashes at the comparison sites occurred in the after period than at the road

diet sites (41.0 percent vs. 35.8 percent). A CochranMantel-Haenszel test of overall significance across

the 10 groups was statistically significant (χ2

1df =

7.5307, p = 0.0061). The estimated risk ratio indicates the percent of crashes at road diet sites in the

after period to be about 6 percent less likely than a

crash at a comparison site, with 95-percent confidence limits of 0.003 and 0.106. Thus, on average,

crash frequencies at road diets in the after period

were approximately 6 percent lower than at the

corresponding comparison sites.

Where before and after ADTs were available,

researchers examined changes on road diets and

comparison sites to determine if motorists were

diverting off road diets and onto comparison sites.

The ADTs on four road diets sites increased by 6.4

percent. A slightly higher increase of 8.0 percent

occurred on the 9 matching comparison sites. Thus,

for the sites included in this analysis, any road diet

diversionary effect was limited. Instead, the dominant phenomenon was an overall increase in ADT

as a result of population growth and other factors. 

Crash Rates

The crash rate analysis included 8 groups with 

reliable ADT data—8 road diets and 14 comparison

sites. The ADTs on the road diets ranged from

8,133–5,658 in the before period and from

8,300–16,482 in the after period. The ADTs on 

the comparison sites ranged from 5,480–24,183 in

the before period and from 7,006–26,100 in the

after period.

Negative binomial regression models were fit to the

crash frequencies at each site. The explanatory variables were traffic volume (millions of vehicles), city

or alternatively group, site type (road diet or comparison site), time period (before or after), and a sitetype-by-time-period interaction. Segment length was

included as a constant factor (i.e., the number of

crashes on a segment was proportional to its length).

More information about negative binomial regression models can be found in McCullagh and Nelder.(4)

(A later attempt was made to use Empirical Bayes

before/after methods as described by Hauer, but was

not possible due to sample limitations.(5)) 

Table 1. Study sites and crashes used in the analysis.

CITY NUMBER OF SITES NUMBER OF CRASHES

Road Diet Comparison* Road Diet Comparison*

Bellevue, WA 1 2 134 307

Mountain View, CA 1 2 20 134

Oakland, CA 2 5 443 2,067

San Francisco, CA 2 5 450 1,339

Seattle, WA 5 9 969 4,485

Sunnyvale, CA 1 2 52 224

TOTAL 12 25 2,068 8,556

* Each road diet had one or more comparison sites.

Analysis

Results

3
The results showed highly significant

variation in crash rates with traffic volume and city, and lesser variation with

site type (i.e., treatment vs. comparison).

The city-by-city variations noted are probably the result of different operational

conditions and crash-reporting practices. 

Figure 2 shows the distributions of crash

rates (per million vehicle miles) for road

diets and comparison sites in the before

and after periods. The distributions of

crash rates at the road diets were less variable than those at the comparison sites.

The mean crash rates at the road diets

were slightly lower than at the comparison sites. At both the road diets and comparison sites, the crash rates decreased

from the before period to the after period.

The extent of the decrease was virtually

the same at both road diets and comparison sites, and was not statistically significant. Thus, this rate analysis did not indicate a significant safety effect.

Crash Severity

The crash severity analysis included 10

groups, with 10 road diets and 20 comparison sites. The total number of crashes was 7,919. Because of local reporting

practices in San Francisco, CA, many

property damage only (PDO) crashes were not reported. Therefore,

two road diets and five comparison sites in that city were excluded

from this analysis.

A crash was classified as PDO if no injuries and no fatalities

occurred. Otherwise, it was classified as injury and fatality. Overall,

approximately 63 percent (5,007) of the crashes resulted in no

injuries or fatalities. The remaining 37 percent (2,912) of the crashes had at least one injury or fatality. These percentages were quite

similar for both road diets and comparison sites in both the before

and after time periods. However, injury rates varied somewhat

from city to city and among the matched groups of sites. To

account for this variation, a logistic regression model was fit to the

injury severity data (no injury or injury/fatality). The explanatory variables in the model were: 

1) Matched group.

2) Site type (road diet or comparison site).

3) Time period (before or after).

4) Site-type-by-time-period interaction. 

McCullagh and Nelder provide an explanation of logistic

regression models.(4)

Figure 2. Crash rates per million vehicle miles for road 

diets and comparison sites.

4

Crashes/Million Vehicle Miles
The results showed that group was the only significant factor (χ2

9df = 347.69, 

p < 0.0001). Crash severity was virtually the same at road diets and comparison sites,

and did not change from the before to the after time period. The city-by-city variations

are most likely the result of different crash-reporting practices in each city.

It was thought that injury and fatal crashes would decrease on road diets relative to

comparison sites, assuming lower vehicle speeds on road diets in the after period.

However, before-and-after speed data were not available, so it cannot be determined

if vehicle speeds changed. It is possible that road diets could have resulted in fewer

serious (i.e., “A”) injuries and more moderate or minor (“B” or “C”) injuries, relative to comparison sites. Such a result would not be apparent from the crash data,

because information on the specific level of injury was not contained in the data. 

Crash Types

Three crash types—angle, rear-end, and sideswipe—accounted for about 80 percent of all crashes (figure 3). Although the crash type distributions were quite

similar for the site-type-by-time-period interaction, angle collisions were somewhat higher for the road diets, and perhaps decreased somewhat less in the

after period, relative to the comparison sites. 

To investigate this, a logistic regression model was fit to a crash type variable

(angle vs. all other) using the same explanatory variables as the crash severity

model. The results from this model again indicated a highly 

significant effect due to group (χ2

9df = 199.24, p < 0.0001). Site type was

also statistically significant (χ2

1df = 13.24, p = 0.0003), with the proportion

of angle collisions higher on road diets than on comparison sites. Neither

time period nor the site-type-by-time-period interaction was significant 

(p = .5862 and p = .9575, respectively). A

similar model showed the proportion of rear-end

crashes to be higher for the comparison sites

than for the road diets, again with no significant

period or interaction effects. The only significant effect in a model for sideswipe crashes was

the effect due to group.

It is not clear why the crash type distributions

were different between the road diets and the

comparison sites, because crash severity was

virtually the same at road diets and comparison

sites. One possible reason is that such differences do exist from one roadway section to

another because of variations in the numbers

of driveways and intersections, vehicle speeds,

vehicle mix, area type, and other factors. It may

be that cities selected roadway sections for road

diet installation at least partly because of such

factors. The variations in the crash type distributions among groups may be the result of how

each city classifies and reports crashes. For

example, all of the California cities included

angle/turning crashes in the total number of

right angle crashes, but in Bellevue, WA, and

Seattle, WA, angle/turning and right angle

crashes were two separate crash types. 

Figure 3. Distribution of crash types for road diets 

and comparison sites.

5
Summary of Findings

The key findings of this study are summarized below and in table 2.

1) Crash frequencies at road diets in the after period were approximately 

6 percent lower than at the corresponding comparison sites.

2) Crash rates did not change significantly from the before period to the after

period. Although crash rates were lower at road diets than at comparison

sites, road diets did not perform better or worse (from the before period to

the after period) relative to comparison sites.

3) Road diet conversions did not affect crash severity.

4) Road diet conversions did not result in a significant change in crash types.

This study found that a significantly lower (approximately 6 percent)

proportion of crashes occurred at road diets in the after period than

at comparison sites in the after period. However, no significant

change was found in crash rate decreases between road diets and

comparison sites. Thus, one may expect that converting a roadway

segment from four-lane undivided to three lanes likely would

reduce total crashes by 6 percent or less. Road diets were no better

or worse than comparison sites with regard to crash severity. 

Additional research is needed to determine whether the crash

reductions observed on road diets can be attributed to lower

speeds, fewer conflicts, or possibly other factors. There is a need

for future safety and operational studies, under a range of traffic volumes and other conditions, to help identify the situations

where road diets would be appropriate. In addition, traffic

operations and capacity must be considered fully at a given site

before implementing road diets and other lane reduction

measures. 

FOR MORE INFORMATION

FHWA-HRT-04-082

HRDS-06/3-04(1.7M)E

REFERENCES

1. Burden, D. and P.

Lagerwey. Road Diets:

Fixing the Big Roads.

http://www.walkable.org/

download/rdiets.pdf.

Accessed July 25, 2001.

2. Welch, T. “The Conversion

of Four-Lane Undivided

Urban Roadways to ThreeLane Facilities.” Presented at

the Transportation Research

Board / Institute for

Transportation Engineers

Urban Street Symposium,

Dallas, TX, June 28–30, 1999.

3. Zegeer, C.V., J.R. Stewart, H.F.

Huang, and P. Lagerwey. Safety

Effects of Marked vs. Unmarked

Crosswalks at Uncontrolled

Locations: Executive Summary and

Recommended Guidelines. FHWARD-01-075. Federal Highway

Administration, U.S. Department of

Transportation, McLean, VA, 2001.

4. McCullagh, P. and J.A. Nelder.

Generalized Linear Models. Chapman

and Hall, London, United Kingdom,

1989.

5. Hauer, E. Observational Before-After

Studies in Road Safety. Pergamon

Press, Elsevier Science Ltd., Oxford,

United Kingdom, 1997. 

This research was conducted by Herman F.

Huang, J. Richard Stewart, and Charles V.

Zegeer of the University of North Carolina

Highway Safety Research Center. The final

report, Evaluation of Lane Reduction “Road Diet”

Measures on Crashes and Injuries, can be found

in Transportation Research Record 1784.

For more information about HSIS, contact 

Carol Tan, HSIS Program Manager, HRDS,

202–493–3315, carol.tan@fhwa.dot.gov.

Visit us on the Web

at www.tfhrc.gov

Visit us on the Web

at www.tfhrc.gov

6

Conclusions and Recommendations

Table 2. Summary of findings.

ANALYSIS

CATEGORY COMPARISON

Road Diets Before Period After Period

Before vs. Comparison Sites Road Diets vs. Road Diets vs.

After Before vs. After Comparison Sites Comparison Sites

Crash Reduction in No change No difference Road diets lower

frequency after period

Crash rates No change No change Road diets lower Road diets lower

Crash severity No change No change No difference No difference

Crash type No change No change Difference: Difference:

1. Road diets had 1. Road diets had

a higher percentage a higher percentage

of angle crashes. of angle crashes.

2. Road diets had 2. Road diets had

a lower percentage a lower percentage

of rear-end crashes. of rear-end crashes.