TECHBRIEF

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.fhwa.dot.gov/research

Safety Evaluation of Access 

Management Policies and 

Techniques

FHWA Publication No.: FHWA-HRT-15-038

FHWA Contact: Wei Zhang, HRDS-10, (202) 493-3317, 

wei.zhang@fhwa.dot.gov

This document is a technical summary of the Federal 

Highway Administration report, Safety Evaluation of Access 

Management Policies and Techniques, FHWA-HRT-14-057.

Introduction

Access management is the process that provides (or manages) 

access to land development while simultaneously preserving 

the flow of traffic on the surrounding road network for safety, 

capacity, and speed. Access management provides important 

benefits to the transportation system. These benefits have been 

increasingly recognized at all levels of government, and a 

growing number of States, cities, counties, and planning regions 

are managing access by requiring driveway permit applications 

and establishing where new access should be allowed. They 

are also closing, consolidating, or improving driveways, median 

openings, and intersections as part of their access management 

implementation strategy. However, these decisions are often 

challenged for various reasons.

Additional information is needed to help guide decisions related 

to access management. This information will help agencies 

better explain the safety and operational benefits of their policies and practices. Previous studies and empirical evidence 

have shown positive operational and safety benefits associated 

with good access management practices. While the operational 

effects of access management have been investigated quantitatively through different modeling and analysis approaches, 

there have been few scientifically rigorous evaluations to 

quantify safety effectiveness, particularly for corridor access 

management. The Federal Highway Administration initiated this 

study to help fill some of the research gaps, namely quantifying 

the safety impacts of corridor access management decisions. 

Study Objective and Scope

The objective of this research was to develop corridor-level 

crash prediction models to evaluate the potential safety effects 

of access management strategies. Agencies can apply the 
2

algorithms to assess the safety impacts of their 

decisions related to access management.

The intent of this study was to focus on corridors 

based on functional classification, area type, and 

land use. All corridors included in this study are 

functionally classified as arterials and fall under 

one of nine area type/land use scenarios. Table 1 

identifies the nine area type/land use categories 

and provides a definition for each area type. 

Residential and commercial areas are characterized by the type of development but are also 

differentiated by the type and distribution of 

vehicle types accessing the areas. Residential 

areas serve mainly passenger cars, and commercial areas serve a larger proportion of heavy 

vehicles. Commercial areas are generally defined 

as those areas with office buildings and other 

businesses that operate primarily during normal 

business hours on weekdays. Commercial areas, 

as defined in this study, do not include large 

shopping centers (e.g., malls) that have a larger 

percentage of trips on the weekends. Mixed-use 

area types are defined as those areas with a 

balanced mix of both commercial and residential 

establishments and access points. Figure 1 and 

figure 2 provide examples of corridors included 

in the study.

Overview of Strategies

Table 2 identifies the access management strategies considered in this research project and 

notes the related access management safety 

principle.

Methodology

An observational cross-sectional study design 

was employed for this project. The safety impact 

of a given feature can be derived from a crosssectional study by comparing the safety of a 

group of sites with that feature to the safety of a 

group of sites without that feature. Multiple variable regression models were used to estimate 

the effects of one feature while controlling for 

other characteristics that vary among the sites. 

These cross-sectional models are also called 

crash prediction models, which are mathematical 

equations that relate crash frequency to site characteristics. While cross-sectional models provide 

a means to estimate the safety impacts of access 

management strategies, potential issues need to 

be addressed. The following potential sources of 

bias were identified in this study with an explanation of how they were addressed or dismissed:

• Selection of appropriate functional form: 

Generalized linear modeling (GLM) 

techniques were applied to develop 

corridor-level crash prediction models. 

A log-linear relationship was specified 

using a negative binomial error structure, 

following the state-of-the-art in modeling 

crash data. The appropriate model form for 

each variable was determined following a 

review of the data.

Area Type Land Use

Urban: Metropolitan area with population of at least 

250,000.

Residential

Commercial

Mixed Use

Suburban: Nearby areas with population of 50,000 to 

250,000.

Residential

Commercial

Mixed Use

Urbanizing: Areas with build out-plans to reach or exceed 

population of 50,000.

Residential

Commercial

Mixed Use

Table 1. Area type and land use categories.
3

Figure 2. Example of a suburban arterial in a commercial area.

Figure 1. Example of an urban arterial in a residential area.
4

• Accounting for State-to-State differences: 

Data from four regions were used to 

develop the crash prediction models, and 

indicator variables were included in the 

models to identify the respective region for 

each corridor.

• Correlation among independent variables: 

The correlation matrix of the estimated 

parameters was examined to determine the 

extent of correlation among independent 

variables. Several access management 

strategies were highly correlated. As such, 

it was necessary to develop multiple models 

with subsets of the independent variables 

rather than one single model with all 

variables.

• Over-fitting of prediction models: Relatively 

few parameters were included in the 

final models due to the correlation issue. 

As such, over-fitting was dismissed as a 

potential issue.

• Low sample mean and sample size: 

Corridor-level models help overcome 

issues related to low sample mean 

because each site (i.e., corridor) typically 

experiences multiple crashes per year. 

Sample size was addressed during the early 

planning stages of the study, and more than 

600 mi of data were obtained to provide a 

large database for analysis. When possible, 

3 years of crash data were obtained to 

further increase the sample size.

Table 2. Strategies/policies in relation to access management safety principles(1)

Access Management 

Strategy/Policy Limit Conflicts Separate Conflicts Reduce Conflicts

Access Spacing

Unsignalized access spacing •

Traffic signal spacing •

Interchange crossroad 

spacing •

Corner clearance •

Roadway Cross-Section

Median type: TWLTL •

Median type: Nontraversable median •

Median type: Replace 

TWLTL with non-traversable 

median

• •

Directional median opening •

Median opening spacing •

Property Access

Frontage/backage roads • •

Internal cross-connectivity • •

TWLTL = Two-way left-turn lane.
5

• Aggregation, averaging, or incompleteness 

in data: Data were obtained from various 

sources and supplemented with field 

measurements to ensure a relatively 

complete and accurate dataset. While 

multiple years of crash data were used 

in the analysis, the number of years was 

also included as an independent variable 

to account for the multiple years of data 

(i.e., the model prediction results in crashes 

per year). A maximum of 3 years of data 

were used for any site.

• Temporal and spatial correlation: 

Temporal correlation may arise if multiple 

observations are used for the same entity. 

In this case, multiple years of data were 

used for each corridor, but these data were 

aggregated into a single observation, since 

the maximum was limited to 3 years, and 

the number of years was included as an 

independent variable. Spatial correlation 

is a potential issue, but the corridors were 

selected from four regions and relatively 

dispersed within each of the regions. 

Indicator variables were included to 

account for similarities within regions.

• Endogenous independent variables: 

Endogeneity arises when one or more of 

the independent variables depend on the 

dependent variable. For example, left-turn 

lanes may be installed due to the frequency 

of left-turn crashes at an intersection. A 

cross-sectional model that predicts crash 

frequency based on the presence of leftturn lanes and other factors may conclude 

that left-turn lanes increase crashes. Similar 

examples could be drawn for other access 

management strategies. In this study, 

endogeneity was not considered to be 

a substantial threat because data were 

aggregated at the corridor level.

• Omitted variable bias: It is difficult to 

completely account for the potential effects 

of omitted variable bias in an observational 

cross-sectional study. In this case, omitted 

variable bias was addressed to the extent 

possible by carefully considering the 

roadway and traffic characteristics to be 

included in the models. Detailed data were 

collected for each corridor, and numerous 

variables were tested for suitability in the 

models. There was the potential for omitted 

variable bias due to other factors such as 

weather, driver population, and vehicle 

fleet, but a regional indicator variable was 

included in the models to help to account 

for differences among the regions. 

Data Collection

Detailed data were collected for more than 

600 mi of corridors across four regions of the 

United States. The regions included North 

Carolina (Raleigh, Cary, and Wake Forest), 

Minnesota (St. Paul and Minneapolis), Northern 

California (Oakland, Sacramento, San Francisco, 

and San Jose), and Southern California (Los 

Angeles and San Diego). This section identifies 

the procedures for selecting corridors, collecting and verifying data, and merging the various 

sources of data for analysis.

Identifying Candidate Corridors

State and local agencies were contacted to solicit 

candidate corridors for inclusion in the study. 

Guidance was provided on what constituted 

suitable corridors to assist the State and local 

agencies with this process. The critical factors for 

corridor selection included the following:

• No major construction activity during the 

study period.

• Availability of crash, traffic volume, and 

roadway inventory data.

• Arterial functional classification (e.g., 

principal arterial, minor arterial). 

• At least one of the target access management strategies is displayed.

• Area type of urban, suburban, or urbanizing.

• Land use of residential, commercial, or 

mixed use. 
6

Collecting Highway Safety Information System 

(HSIS) Data

HSIS contains readily available crash, roadway, 

and traffic volume data for select States. By 

design, the three States included in this study are 

all members of HSIS. The HSIS guidebooks were 

examined, and any potentially useful variables 

were requested. At the time of the data request, 

the most recent year of available data was 2008. 

As such, the study period for this project was 

from 2006 to 2008. 

Corridor Screening

Three rounds of screening were employed to 

ensure that there were no major construction 

activities or changes along the corridors during 

the study period. Initial screening was conducted 

by the participating agencies through a review of 

construction records. The study team performed 

a second phase of screening using HSIS data, 

comparing specific variables across years to 

detect changes that would indicate construction activity (e.g., number of lanes, lane width, 

shoulder width, median type, median width, and 

mileposts). The team performed a third round of 

screening using historical aerial imagery. They 

identified high-resolution aerial imagery for 

the identified corridors from the United States 

Geological Survey National Seamless Server. By 

comparing historical aerial images with current 

conditions, the team was able to identify where 

changes had taken place during the study period. 

Supplemental Data Collection and Verification

The data obtained through HSIS were verified and augmented with additional data from 

video collected during field visits and aerial 

imagery. Detailed information such as lighting presence, visual clutter, and posted speed 

limit were obtained from the field videos. 

Aerial imagery was used to verify the land use, 

number of through lanes, and median type for 

each corridor. Aerial imagery was also used to 

collect information that was unavailable from 

HSIS such as the frontage type, presence of 

frontage or backage road, extent of internal 

cross-connectivity, condition of pavement 

markings, and access points (location, type, 

and density).

Setup of Geographic Information System (GIS) 

Database to Facilitate Data Collection

ArcGISTM feature classes were created for signalized intersections, unsignalized intersections, 

driveways, and medians. This process allowed 

data collectors to insert symbols representing 

these objects on the aerial images of the corridors. Data fields were created for each object so 

its characteristics could be noted. The characteristics collected for each object are summarized 

in table 3.

Figure 3 provides an example of these objects 

for a 1-mi section of a study corridor (California 

Route 1). In total, the study corridors contained 

more than 1,500 signalized intersections, 3,500 

unsignalized intersections, and 15,000 driveways. 

Post-Processing

Data were obtained in various formats. Some 

information was provided in Microsoft® Word 

documents (e.g., area type, land use, and frontage type), specifying the beginning and ending 

points of each corridor. HSIS data were provided 

in Microsoft® Excel format. General corridor 

information and specific attributes for signalized 

intersections were identified in the video logs. 

Other information was identified and stored in 

the form of ArcGISTM feature datasets (e.g., intersections and driveways). Due to the multiple 

formats, a post-processing step was required to 

transform these data sources into a well-integrated database to serve as the basis for statistical analysis. The following tasks were performed 

as part of the post-processing:

• General corridor information: A spreadsheet 

was developed and populated to identify the 

general corridor data, including a unique 

corridor ID, route number, beginning 

milepost, ending milepost, beginning 

cross-street, ending cross-street, area type, 

and land use for each corridor. The video 

logs and aerial images were then used to 

populate additional data fields for each 

corridor. HSIS data were then appended to 

identify crashes along each corridor.
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Object Type Characteristics

Driveways • Type (commercial or residential)

• Movements permitted (limited movement or full 

movement)

Median openings and crossovers Presence of left-turn lane

Unsignalized intersections • Type (two-way stop control, all-way stop control, or 

roundabout

• Presence of left-turn lane(s) on mainline

• Presence of right-turn lane(s) on mainline

• Presence of left-turn lane(s) on cross street

• Presence of right-turn lane(s) on cross street

• Movements permitted (right in right out, left from major 

only, or full)

• Maximum number of through lanes on the cross street

Signalized intersections • Number of approaches

• Presence of left-turn lane(s) on mainline

• Presence of right-turn lane(s) on mainline

• Presence of left-turn lane(s) on cross street

• Presence of right-turn lane(s) on cross street

• Presence of non-traditional accomodation of left turns

• Maximum number of through lanes on the cross street

Table 3. Objects and characteristics coded in ArcGISTM.

Figure 3. Example of point objects for a 1-mi corridor.
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• Segmentation: Within each corridor, new 

segment links were created whenever 

variables changed. The beginning and 

ending mileposts were identified for each 

individual link. The links were aggregated 

based on area type and land use to create 

the study corridors, and each study corridor 

was assigned a unique ID. Specifically, 

each study corridor was consistent with 

respect to area type and land use, but other 

variables were allowed to change.

• Access density, spacing, and corner 

clearance: The ArcGISTM files were used 

to query access-related information for 

each study corridor. Several variables 

were created, including the total number 

of specific access points (driveways, stopcontrolled intersections, and signalized 

intersections), minimum spacing, maximum 

spacing, number with/without right-turn 

lanes, number with/without left-turn lanes, 

and number with turning restrictions (full or 

limited movement).

• Median openings and crossovers: The 

ArcGISTM files were used to query median 

opening information for each study corridor 

with a median. The total number of median 

openings was identified for each study 

corridor along with the number of median 

openings with and without left-turn lanes.

• Interchange-related spacing: The ArcGISTM 

files were used to query interchangerelated information for each study corridor. 

Specifically, the ramp location and type 

were identified for each interchange. 

For off-ramps, the distances to the first 

downstream driveway (on right), the 

first median opening allowing left 

turns, and the first major intersection 

were measured. For on-ramps, the 

distances from the last driveway (on right) 

and major intersection were measured.

The segmentation process required that links 

be combined into study corridors to achieve a 

reasonable length for analysis. In this way, some 

variables were summed over all links making 

up a study corridor (e.g., number of driveways). 

In other cases, new variables reflecting the 

percentage of the total length were created 

(e.g., number of lanes). The average annual daily 

traffic (AADT) and percentage truck variables 

were calculated as weighted averages, weighting by the lengths of the links within a corridor.

Summary Statistics

The result of the data collection and postprocessing totalled 245 corridors representing 

over 600 mi and approximately 6,500 crashes. 

Table 4 presents corridor mileage by area type 

and land use. Table 5 presents a summary of the 

crashes that occurred along the study corridors 

from 2006 to 2008. The summary statistics are 

based on corridor-level data and represent the 

crash density (i.e., number of crashes per mile 

per year) for various crash types. The crash types 

are defined in the Results section.

Analysis

GLM techniques were applied to estimate the 

models. A negative binomial error structure 

was specified, following the state-of-the-art in 

modeling crash data. The negative binomial 

structure is now recognized as being more 

appropriate for crash counts than the normal 

distribution that is assumed in conventional 

regression modeling. Crash counts per year 

by crash type were used as estimates of the 

dependent variable, while corresponding roadway characteristics and traffic volume data 

were used as the independent variables. 

Scenario Commercial Mixed Use Residential Subtotal

Urban 79.1 92.4 48.7 220.2

Suburban 64.3 119.7 57.0 241.0

Urbanizing 63.8 31.9 62.4 158.3

Subtotal 207.4 244.1 168.2 619.5

Table 4. Mileage of corridors by area type and land use.
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Crash Type Region Corridors Minimum Maximum Mean

Standard 

Deviation

Total

North Carolina 74 0.84 195.31 28.57 25.91

Northern 

California 61 0.18 64.52 20.16 15.27

Southern 

California 51 1.14 108.99 23.29 19.66

Minnesota 59 3.10 140.26 33.32 27.55

Injury

North Carolina 74 0.00 40.74 7.22 5.70

Northern 

California 61 0.00 24.29 8.34 6.35

Southern 

California 51 0.33 33.61 10.34 6.55

Minnesota 59 1.19 52.39 11.07 10.80

Rear-end

North Carolina 74 0.00 69.63 12.91 11.76

Northern 

California 61 0.00 25.57 8.85 7.07

Southern 

California 51 0.00 43.60 9.60 9.27

Minnesota 59 0.33 67.83 16.13 16.33

Right-angle

North Carolina 74 0.00 52.10 4.31 6.39

Northern 

California 61 0.00 16.67 2.32 3.09

Southern 

California 51 0.00 14.99 2.46 2.77

Minnesota 59 0.36 27.64 5.72 6.10

Turning

North Carolina 74 0.00 24.44 4.94 4.73

Northern 

California 61 0.00 17.18 5.23 4.23

Southern 

California 51 0.33 35.88 7.19 6.29

Minnesota 59 0.00 23.93 2.49 3.52

Table 5. Crash density summary statistics (crashes per mile per year).
10

The first step in the analysis process was to 

develop a model using only the AADT as a predictor variable and both the number of years and 

corridor length as offset variables. The general 

form of this model is given by figure 4.

The next step was to investigate additional variables. This investigation involved entering each 

variable one at a time such that only AADT and 

the new variable of interest were included. The 

estimated parameter and its standard error were 

examined to determine the following:

• If the direction and magnitude of effect are 

logical.

• If the estimate is close to being statistically 

significant.

• If the estimated dispersion parameter 

improved significantly. The properties of 

the dispersion parameter are such that 

lower values indicate a better fit. 

Alternate model forms were explored using the 

procedure described by Hauer and Bamfo.(2) It 

was determined that the exponential model 

form is appropriate due to its flexibility, and 

this form was retained for development of the 

final models. 

Pearson correlation statistics were computed for 

each independent variable and all crash types 

per mile-year. The correlation matrix was not the 

primary driver of model-building but helped to 

identify those variables most associated with 

the different crash types. The correlation matrix 

also helped identify independent variables that 

were highly correlated. High correlation between 

independent variables can be problematic in 

developing models. Specifically, the inclusion of highly correlated variables can lead to 

illogical results. While this issue can be avoided 

by omitting a highly correlated variable, this 

omission limits the practicality of the results 

when determining the safety impacts of the omitted variable. To overcome this challenge, a series 

of models were estimated with various combinations of variables, which helped address issues 

related to correlation and provide information 

for all variables of interest. 

Following the development of preliminary 

models, feedback was requested from a steering committee as to which of the variables were 

most desired from a practical perspective. Not 

all variables could be included in the models 

due to sample size limitations and correlation 

between potential explanatory variables. As 

such, the steering committee was asked to 

identify the explanatory variables that would 

be most useful to practitioners. The following 

variables were indicated to be most important 

for practical use according to the feedback:

• Adjacent land use (i.e., no development, 

partial development, or full development).

• Driveway density.

• Median type (i.e., undivided, two-way leftturn-lane, or divided).

• Number of median openings.

• Signalized intersection density.

• Speed limit.

All of these variables were included in various 

models except for posted speed limit. Vehicle 

speed was related to the severity of a crash, 

but the posted speed limit was not included in 

these models because it was statistically insignificant after accounting for other variables. Posted 

speed tends to be highly correlated with other 

variables such as access density and frontage 

type, which is likely why it could not be included 

in the final models. It is also possible that posted 

speed is not providing an accurate representation of the actual speeds (i.e., operating speed 

may be a better alternative for capturing the 

impacts of speed).

Figure 4. General form of crash prediction model.
11

Results

One or more models were successfully calibrated for each land use type and crash type 

combination. The three land use types are mixed 

use, commercial, and residential. The crash 

types include total, injury, turning, rear-end, and 

right-angle crashes. Note that individual models 

by crash type cannot be summed to estimate 

total crashes. Also, each State has specific crash 

codes, and, as such, the definitions vary slightly. 

The crash types are identified in table 6 with the 

associated definitions for each region.

In the modeling phase, the treatment of area 

type and regional variables required further resolution. Within each land use type (i.e., mixed 

use, commercial, and residential), each corridor 

was identified as being located within an urban, 

suburban, or urbanizing area. All area types were 

combined within the respective land use type in 

order to develop reliable models. A factor variable was included in each model to account for 

any differences due to area type, but the differences were minor and statistically insignificant. 

This is not to say that there was no difference in 

crash patterns by area type, but the data did not 

allow this relationship to be quantified. It is also 

likely that area type is better described by other 

variables in the model. For example, the traffic 

volume, number of lanes, access density, and 

frontage development can be used to describe 

the characteristics of a corridor and are more 

quantitative than defining a corridor as urban, 

suburban, or urbanizing. As such, area type was 

not included as an independent variable in the 

final models.

An indicator variable was included in each model 

to identify the region in which the corridor is 

located (i.e., North Carolina, Minnesota, Northern 

California, or Southern California). This variable 

accounts for differences between regions such as 

those related to crash reporting practices, driver 

demographics, weather, and other non-accessCrash Type Definition

Total All regions: Defined as all crashes

Injury All regions: Defined as KABC on KABCO scale

Turning

• California: Defined as any involved vehicle making a 

turn

• Minnesota: Defined as left turn or right turn

• North Carolina: Defined as rear-end turn, left-turn safe 

roadway, left-turn different roadway, right-turn same 

roadway, right-turn different roadway*

Rear-end

• California and Minnesota: Defined as rear-end

• North Carolina: Defined as rear-end slow or stop and 

rear-end turn*

Right-angle

• California: Defined as broadside and no vehicle was 

turning

• Minnesota: Defined as right angle

• North Carolina: Defined as angle

Table 6. Crash type definitions.

KABCO = KABCO injury severity scale, where K = Fatal, A = Incapacitating injury, B = Non-incapacitating injury, C = Possible 

injury, and O = Property damage only.

*North Carolina crashes coded as rear-end turn crashes are included in both rear-end and turning crashes. Because the 

specific crash types cannot be summed to get total crashes, it was decided that double-colunting should not pose a 

problem for the crash type models.
12

related factors affecting reported crashes. The 

factors for Northern and Southern California 

were similar and sufficiently close to be considered as one region. Similarly, the factors for 

Minnesota and North Carolina were sufficiently 

similar to consider them as one region. The 

aggregate regions help to increase sample sizes 

within the models (i.e., two regions instead 

of four) and reflect the similarities between 

the aggregated regions. Note that the models 

presented in this section include a variable to 

identify the applicable region. Users should 

select an applicable region based on a comparison between the corridor of interest and the 

summary statistics in the full report, and not 

based on geographic proximity.

The models in this section are presented in one 

of two forms. In most cases, the model form 

is represented by figure 5. In these cases, the 

result is expressed as crashes per mile per year. 

In other cases, the traffic volume variable is 

statistically insignificant, indicating a linear relationship between traffic volume and crashes. In 

these limited cases, the model form is reduced to 

figure 6, and the result is expressed as crashes 

per million vehicle-miles.

Where:

• Intercept is the coefficient estimated for the 

model to account for unobserved variables 

(see table 7 to table 9).

• Region is the coefficient estimated for 

the model when the applicable region is 

North Carolina or Minnesota; a value of 0 is 

used if the applicable region is Northern or 

Southern California (see table 7 to table 9).

• AADT is the annual average daily two-way 

traffic for the corridor.

• b is the coefficient estimated for the AADT 

term in the model (see table 7 to table 9).

• ci is a vector of coefficients estimated for 

the other independent variables included in 

the model (see table 7 to table 9).

• Xi is a vector of other independent variables 

included in the model (i.e., the specific 

roadway attributes such as access density).

• ACCDENS is the number of driveways plus 

unsignalized intersections per mile.

• MEDOPDENS is the number of median 

openings per mile.

• PROPDIV is the proportion of corridor length 

with divided median.

• PROPFULLDEV is the proportion of corridor 

length with full roadside development.

• PROPLANE1 is the proportion of corridor 

length with two lanes.

• PROPNODEV is the proportion of corridor 

length with no roadside development.

• PROPVC is the proportion of corridor length 

with visual clutter.

• PROPTWLTL is the proportion of corridor 

length with two-way left-turn lanes.

• SIGDENS is the number of signalized 

intersections per mile.

• UNSIGDENS is the number of unsignalized 

intersections per mile.

Figure 5. Crash prediction model with regional 

calibration.

Figure 6. Normalized crash prediction model with 

regional calibration.
13

Variables

Total Injury Turning Rear-End Right-Angle

1 2 3 1 2 1 2 3 1 1 2 3

Intercept -3.1845

(1.9550)

-3.2905

(1.8743)

-0.8926

(0.5021)

-3.5700

(1.7816)

-1.7775

(0.5964)

-2.1083

(0.4338)

-2.0792

(0.3963)

-0.4146

(0.7632)

-3.3091

(0.6700)

-5.8048

(1.9472)

-5.2671

(2.1768)

-2.1485

(0.6851)

Region 1.1410

(0.2316)

1.0533

(0.2086)

0.6166

(0.1013)

0.5695

(0.1980)

0.2465

(0.0931)

0.9647

(0.2843)

0.8015

(0.2354)

-0.3163

(0.1301)

0.8113

(0.1136)

1.8390

(0.2616)

1.2134

(0.2457)

1.2344

(0.1377)

AADT 0.5187

(0.1819)

0.5266

(0.1738)

0.3766

(0.0468)

0.5010

(0.1659)

0.3880

(0.0558)

0.2179

(0.0729)

0.5015

(0.0618)

0.4656

(0.1856)

0.5678

(0.2103)

0.2433

(0.0648)

ACCDENS 0.0053

(0.0044)

0.0088

(0.0061)

0.0112

(0.0051)

MEDOPDENS 0.1901

(0.0884)

PROPDIV -0.4710

(0.3461)

PROPFULLDEV 0.6787

(0.1846)

PROPLANE1 -0.5185

(0.3789)

-0.6376

(0.3796)

-0.5814

(0.3582)

-0.6623

(0.1404)

-0.5548

(0.1713)

PROPNODEV -0.4252

(0.2268)

-0.3159

(0.2201)

-0.5890

(0.2827)

PROPVC

PROPTWLTL

SIGDENS 0.1095

(0.0607)

0.0957

(0.0594)

0.1239

(0.0556)

0.1865

(0.0754)

0.1797

(0.0742)

0.0621

(0.0380)

0.2284

(0.0637)

UNSIGDENS 0.0471

(0.0224)

0.0582

(0.0323)

Dispersion (k) 0.5073 0.4897 0.5165 0.4248 0.4151 0.7920 0.7780 0.7791 0.6098 0.5585 0.6796 0.7674

Table 7. Model coefficients and standard error (in parentheses) by crash type for mixed use corridors.
14

Variables

Total Injury Turning Rear-End Right-Angle

1 2 1 2 3 4 1 2 1 1 2

Intercept -0.7017

(0.6873)

-0.6854

(0.5010)

-2.0602

(0.7991)

-0.9792

(0.8386)

0.2127

(0.7288)

-1.9690

(0.5862)

-0.9816

(0.9366)

0.0085

(1.1277)

-3.0651

(0.6691)

-1.6746

(0.9312)

-1.9023

(0.6838)

Region 0.8353

(0.1883)

0.6166

(0.1013)

0.4672

(0.1815)

0.2383

(0.1497)

0.6769

(0.1559)

0.3056

(0.0923)

-0.2548

(0.2101)

0.8113

(0.1136)

1.4756

(0.2388)

1.2344

(0.1377)

AADT 0.3094

(0.0660)

0.3766

(0.0468)

0.3649

(0.0766)

0.3225

(0.0797)

0.2705

(0.0697)

0.3751

(0.0548)

0.1650

(0.0960)

0.1947

(0.1068)

0.5015

(0.0618)

0.1238

(0.0912)

0.2433

(0.0648)

ACCDENS 0.0069

(0.0048)

0.0085

(0.0047)

0.0110

(0.0052)

0.0165

(0.0064)

MEDOPDENS

PROPDIV

PROPFULLDEV 0.6787

(0.1846)

PROPLANE1 -0.6047

(0.2631)

-0.6244

(0.2566)

-0.5245

(0.1430)

-0.7328

(0.3577)

-0.5548

(0.1713)

PROPNODEV -0.4252

(0.2268)

-0.6472

(0.3040)

-0.6967

(0.4150)

PROPVC 0.5421

(0.1990)

PROPTWLTL

SIGDENS 0.1002

(0.0523)

0.0566

(0.0512)

0.1075

(0.0300)

0.1995

(0.0660)

0.0621

(0.0380)

0.1532

(0.0658)

UNSIGDENS

Dispersion (k) 0.4890 0.5165 0.4406 0.4228 0.4739 0.3951 0.7140 0.7802 0.6098 0.7288 0.7674

Table 8. Model coefficients and standard error (in parentheses) by crash type for commercial corridors.
15

Variables

Total Injury Turning Rear-End Right-Angle

1 2 3 1 2 1 2 3 1 2 3 1 2

Intercept -0.5615

(0.7076)

-1.3644

(0.4953)

-1.1048

(0.4876)

-2.7357

(0.8556)

-2.7379

(0.9147)

-1.1275

(1.1225)

-0.9528

(0.7286)

-0.7154

(0.7477)

-3.8941

(0.9816)

-2.6180

(1.0221)

-3.3056

(0.6549)

-1.4079

(1.0732)

-2.1173

(0.6540)

Region 0.4443

(0.1533)

0.6850

(0.1107)

0.6166

(0.1013)

0.1656

(0.1423)

0.2303

(0.1603)

-0.6520

(0.2073)

-0.1651

(0.1339)

-0.3163

(0.1301)

0.5803

(0.1984)

0.5406

(0.1865)

0.8113

(0.1136)

0.8858

(0.2180)

1.1970

(0.1314)

AADT 0.3094

(0.0673)

0.3883

(0.0463)

0.3766

(0.0468)

0.4189

(0.0820)

0.4615

(0.0867)

0.1826

(0.1059)

0.1759

(0.0708)

0.2179

(0.0729)

0.5392

(0.0945)

0.4782

(0.0967)

0.5015

(0.0618)

0.1332

(0.1051)

0.1768

(0.0639)

ACCDENS 0.0032

(0.0022)

0.0052

(0.0028)

0.0044

(0.0028)

MEDOPDENS

PROPDIV

PROPFULLDEV 0.3371

(0.2317)

0.3720

(0.2273)

0.4295

(0.3125)

PROPLANE1 -0.5479

(0.1702)

-0.4040

(0.1669)

-0.6125

(0.1715)

-0.8174

(0.2078)

-0.5548

(0.1713)

-0.3633

(0.2383)

PROPNODEV -0.4252

(0.2268)

-0.5890

(0.2827)

PROPVC

PROPTWLTL -0.5600

(0.2439)

SIGDENS 0.1262

(0.0629)

0.2081

(0.0539)

0.2244

(0.0818)

0.1821

(0.0426)

0.1675

(0.0864)

0.0621

(0.0380)

0.2267

(0.0750)

0.2084

(0.0390)

UNSIGDENS 0.0635

(0.0283)

Dispersion (k) 0.3277 0.5181 0.5165 0.2663 0.3220 0.5792 0.7030 0.7791 0.5541 0.4803 0.6098 0.5555 0.6790

Table 9. Model coefficients and standard error (in parentheses) by crash type for residential corridors.
16

In either case, the same general procedure is 

followed to select an appropriate model and 

compute the predicted crashes. Further discussion of model selection and related examples 

are provided in the full report. The model coefficients and dispersion parameters are provided in 

table 7 through table 9. The dispersion parameter (k) is provided to help select an appropriate 

model when multiple options are available. The 

following factors (in priority order) may be considered in the selection of an appropriate model 

if more than one option is available:

1. Availability of data to apply the model.

2. Statistical significance of the coefficients for 

the variables of interest. (The standard error 

is shown in parentheses under the respective 

coefficient.)

3. Dispersion parameter (k). (A smaller value 

indicates a better fitting model.)

Once a model is selected, the parameter estimates and the characteristics of the corridor 

of interest are used to compute the predicted 

crashes for the corridor. The following example 

provides sample results and a demonstration of 

how a given model can be used to compute the 

predicted crashes. Further details and examples 

are provided in the full report.

Example

Assume an analyst would like to predict the total 

number of crashes for a mixed-use corridor in 

North Carolina, and model 1 for total crashes is 

selected from table 7. The corridor is described 

by the following characteristics: AADT of 

25,000 vehicles per day, 10 signalized intersections, 30 unsignalized intersections, and 80 driveways. The total corridor length is 2.5 mi, of which 

0.625 mi is two lanes. Figure 5 presented the 

model form and sample estimates for the example problem.

The intercept and region are constants, and the 

region coefficient is included in this case since 

the corridor of interest is similar to the corridors in North Carolina. The intercept and region 

coefficients are summed and included in the first 

exponential term of the model. The traffic volume is identified for the scenario of interest and 

input as the AADT term in the model. The coefficient for traffic volume is identified from the 

table and input as the b term in the model. The 

appropriate values for relevant access management variables are then identified and input as 

the X1 – Xn terms in the model. Finally, the corresponding coefficients for access management 

variables are identified from the table and input 

as the c1 to cn terms in the model. (Note that 

the coefficients c1 to cn correspond to predictor 

variables X1 to Xn, respectively.)

In this case, the model coefficients from 

table 7, model 1, are intercept (-3.1845), 

region (1.1410), AADT (0.5187), ACCDENS 

(0.0053), SIGDENS (0.1095), and PROPLANE1 

(-0.5185). The AADT is 25,000 vehicles per 

day. Predictor variables X1 to Xn are defined 

as follows: 

• ACCDENS = (80 driveways + 30 

unsignalized)/2.5 mi = 44.0 access 

points/mi.

• SIGDENS = 10 signals/2.5 mi = 4.0 signals/mi.

• PROPLANE1 = 0.625 mi of two-lane/2.5 mi 

= 0.25.

The predicted number of crashes per mile per 

year is computed as seen in figure 5. With the 

values used in model 1, crashes per mile per year 

would be computed as seen in figure 7:

In the previous example, a model was 

selected and applied to predict the number 

of total crashes for a corridor with specific 

Figure 7. Example of using crash prediction model 1 in 

table 7.
17

characteristics. This example is just one potential use of the corridor crash prediction models. 

The following discussion identifies the two basic 

applications of the corridor crash prediction 

models from this research:

1. Assess the relative safety effects of one or 

more contemplated strategies (or combinations). This situation applies to both existing 

corridors and new construction. The following two scenarios explain how the models 

can be applied:

• Predicted crashes: The models can 

be used to estimate the number of 

crashes for the corridor under both 

existing and proposed conditions. 

The results can then be compared 

to estimate the change in predicted 

crashes as various features change. 

This comparison is particularly useful 

if the expected AADT changes among 

the alternative conditions.

• Percent change in crashes: The 

models can be used to compare the 

relative effects of various strategies 

without predicting crashes for a 

given scenario. In this case, there is 

no direct consideration of the costs 

of alternative strategies or strategy 

combinations. For this application, 

the individual coefficients from the 

selected model(s) are used to derive 

the effects of the variable(s) of 

interest. For each variable, the relative 

effect is derived as exp(coefficient). 

For example, consider the impacts 

of signal density (SIGDENS) on total 

crashes for a mixed-use corridor. The 

coefficient from table 7, model 1, is 

0.1095. The relative effect of SIGDENS is 

exp(0.1095) = 1.12, which indicates that 

total crashes are expected to increase 

by 12 percent along this corridor for 

every unit increase in signal density 

(e.g., one to two signals per mi). For 

a change from one to three signals 

per mi, the expected change in crashes 

would be a 24 percent increase.

2. Compare the benefits and costs of two 

or more alternative strategies or strategy 

combinations in order to select the most 

appropriate alternative. This situation applies 

to existing corridors with an available crash 

history. The observed crash history is used in 

conjunction with the predicted crashes from 

the selected model(s) to obtain the expected 

number of crashes for the scenarios of interest. This number will be referred to as the 

Empirical Bayes (EB) estimate, which corrects for several potential sources of bias, 

including variables that are not in the model. 

A correction factor is computed as the ratio 

of the EB estimate and the model estimate, 

which can then be used to adjust model predictions for proposed scenarios.

The full report provides additional guidance on 

the selection and application of the most appropriate models depending on the intended application. Six typical scenarios are discussed in 

detail, and sample problems are provided in the 

full report to further illustrate the application of 

models in the six scenarios. The six scenarios 

include the following:

1. All variables of interest are available in one 

(and only one) land-use model for each crash 

type of interest.

2. All variables of interest appear in multiple 

models for the same land use and crash type 

of interest (i.e., choose the most appropriate 

model from multiple options). 

3. Variables of interest appear in different models for the same land use and crash type of 

interest (i.e., using a combination of models 

to assess the impacts of multiple variables 

because some variables of interest are in one 

model while other variables of interest are in 

another model).

4. Variables of interest appear in models for different crash types (i.e., assessing the impacts 

of a variable over different crash types).
18

5. Variables of interest are available for a given 

crash type but not for the land-use type of 

interest (i.e., extrapolating the impacts of a 

variable on a given crash type from models 

related to a different land use). 

6. Variables of interest do not appear in any 

models for any crash type or land use.

The following guiding principles are common to 

all scenarios.

• If necessary, models may be extrapolated 

with caution across land-use types for a 

given crash type. However, models for 

one crash type may not be extrapolated to 

another crash type.

• In some situations, it may not be possible to 

estimate the impacts of a strategy for all or 

some crash types.

• If the objective of the analysis is to compare 

the benefits and costs of alternatives, then 

the models should be calibrated for the 

local jurisdiction when possible using the 

EB method.

Recommendations

The results of this project will help users better understand the safety implications of their 

decisions related to access management. 

Specifically, users can apply the models to 

assess the relative safety effects of one or more 

contemplated strategies (or combinations), or 

they can compare the benefits and costs of 

two or more alternative strategies (or combinations). It is recommended that a safety 

evaluation software tool be developed to help 

users select and apply an appropriate model or 

set of models. Functional specifications were 

developed as part of this project to facilitate the 

development of such a tool. The specifications 

include a detailed description of the model 

selection process and identify the required and 

optional inputs as well as default values for the 

various scenarios included in this study. 

Conclusions

This research was performed to develop 

corridor-level crash prediction models to estimate and analyze the safety effects of selected 

access management techniques for different 

area types, land uses, roadway variables, and 

traffic volumes. More than 600 mi of detailed 

corridor data were collected across four regions 

of the United States to facilitate the model estimation process. It was not possible to develop 

a single model for each crash-type and land-use 

scenario due to the strong correlations among 

many of the variables of interest. As a result, 

41 crash prediction models were estimated for 

specific land-use and crash-type scenarios. In 

most cases, multiple models are presented for 

each land-use and crash-type scenario in which 

the alternate models contain subsets of access 

management strategies in an attempt to account 

for strong correlations among variables. A fourstep process is provided in the full report to 

guide users through the model selection and 

application process, but it is envisioned that a 

basic software tool will be developed to simplify 

this process based on functional specifications. 

Sample problems are provided in the full report 

to illustrate the various uses of the models and 

to demonstrate the model selection and application process. 

These models represent the first of their kind for 

evaluating the safety effects of access management strategies at the corridor level based on 

national data. While the results of this research 

will help advance the knowledge-base and state 

of the practice in access management, the crash 

prediction models are not without limitations. 

Specific limitations of the models include the 

following:

• Omitted variables: Ideally, a single crash 

prediction model would include all desired 

variables of interest. This model was not 

a preferred option in this study due to 

strong correlation among several of the 

independent variables. To overcome issues 

related to correlation, all variables could 
19

not be included in a single model. Other 

variables were omitted due to illogical 

effects and statistical insignificance. As a 

result, most models have few variables, 

and median type is not represented in most 

models.

• Inability to quantify effects of turning 

restrictions: Detailed data were collected 

to identify the type of access points (e.g., 

residential versus commercial driveway) 

and the associated turning restrictions 

(e.g., full movement, right in right out, and 

left from major only). Incorporating this 

information in the models proved difficult. 

Variables were created to represent these 

characteristics at the corridor level, but 

the results were statistically insignificant. 

While detailed data are available for each 

point, the models were not developed to 

assess the impacts of individual points 

(i.e., a specific driveway or intersection). As 

such, differences between full and limited 

movement access points and between 

three-legged and four-legged intersections 

are not clear from these models.

• Lack of volumes on cross streets and 

driveways: Traffic volume is a key variable 

in predicting crashes. The objective of this 

study was to develop corridor-level crash 

prediction models, so a weighted average 

of the traffic volume along the corridor was 

used to account for exposure. While the 

major road volume was included, the minor 

road volume and driveway volumes were 

not included. Variables such as land use, 

driveway density, and frontage development may serve as surrogates for traffic 

volume at minor roads and driveways.

• Inability to quantify effects of interchange 

cross-road spacing: Detailed data 

were collected to represent various 

characteristics of interchange crossroads 

(e.g., distance from ramp terminal to 

nearest turning opportunity); however, 

there were relatively few interchanges 

included in the dataset, and the results 

were statistically insignificant.

Based on the results of this research and lessons 

learned during the completion of the study, there 

are several opportunities for future research as 

follows:

• Increase sample size and regional diversity: 

There is an opportunity to increase the 

number of sites and years of data in the 

database. Increasing the sample size 

will likely improve the models and allow 

additional analysis of the variables of 

interest. Specifically, this increase could 

focus on resolving the shortcomings noted 

previously.

• Corroborate results: Cross-sectional 

methods are useful for developing crash 

prediction models, but there are several 

potential sources of bias as discussed in 

the methodology section. Rigorous beforeafter studies are preferred for estimating 

the effects of an individual strategy (e.g., 

access management characteristic). There is 

an opportunity to corroborate the results of 

these crash prediction models by collecting 

additional data to undertake before-after 

evaluations of each individual strategy.

• Separate models for non-driveway and 

driveway crashes: This study estimated 

models for a variety of crash types, including 

total, injury, turning, rear-end, and rightangle. It may be of interest to estimate 

additional models to explore the effects of 

specific access management strategies on 

driveway and non-driveway crashes. This 

estimation was not possible in this study 

due to the lack of specific information in the 

crash data (i.e., California does not indicate 

driveway-related crashes). Additional 

research could investigate the suitability 

of developing these separate models while 

considering the potential for extensive 

geographic diversity in how driveway and 

non-driveway crashes are defined.
20

Researchers—This study was performed by Vanasse Hangen Brustlin, Inc. For more information about 

this research, contact Wei Zhang, FHWA Project Manager, HRDS-10 at (202) 493-3317, 

wei.zhang@dot.gov. 

Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution is being made to the Divisions and Resource Center. 

Availability—The report may be obtained from the FHWA Product Distribution Center by e-mail to 

report.center@fhwa.dot.gov, fax to (301) 577-1421, phone to (301) 577-0818, or online at http://www.

tfhrc.gov/safety. 

Key Words—Access management, safety analysis, crash prediction models. 

Notice—This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they 

are considered essential to the objective of the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides high-quality 

information to serve the Government, industry, and public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and 

processes to ensure continuous quality improvement.

AUGUST 2015 FHWA-HRT-15-038

HRDS-10/08-15(WEB)E

• Develop Highway Safety Manual (HSM)- 

type algorithms: The HSM provides 

methods for estimating the expected 

number of crashes for individual 

intersections and homogeneous 

segments.(3) These estimates can be 

combined to estimate the crashes for a 

given corridor. The HSM uses a system 

of base models to predict crashes for 

an average scenario, and adjustment 

factors (i.e., crash modification factors) 

are used to adjust the base predictions 

to reflect actual conditions. The models 

developed in this study are corridor-level 

models, but there may be an opportunity 

to use these models as base models for 

average conditions and apply corridorlevel adjustment factors to reflect actual 

corridor conditions. Additional research 

could investigate the suitability of using 

these models for this purpose. 

References

1. Stover, V.G., Access Management Techniques 

and Practices: A Toolbox for the Practitioner, 

Teach America, 2007, revised 2009.

2. Hauer, E., and Bamfo, J.,“Two Tools for 

Finding What Function Links the Dependent 

Variable to the Explanatory Variables,” 

Proceedings of ICTCT 97 Conference, Lund, 

Sweden, November 5–7, 1997.

3. American Association of State Highway and 

Transportation Officials, Highway Safety 

Manual, 1st Ed., Washington, DC, 2010.