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OCR for page R1
NATIONAL
NCHRP REPORT 546
COOPERATIVE
HIGHWAY
RESEARCH
PROGRAM
Incorporating Safety into
Long-Range Transportation
Planning
OCR for page R2
NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM
NCHRP REPORT 546
Incorporating Safety into
Long-Range Transportation Planning
SIMON WASHINGTON
IDA VAN SCHALKWYK
SUDESHNA MITRA
University of Arizona
Tucson, AZ
MICHAEL MEYER
ERIC DUMBAUGH
Georgia Institute of Technology
Atlanta, GA
And
MATTHEW ZOLL
Tucson, AZ
S UBJECT A REAS
Planning, Administration, and Environment · Operations and Safety · Public Transit · Freight Transportation
Research Sponsored by the American Association of State Highway and Transportation Officials
in Cooperation with the Federal Highway Administration
TRANSPORTATION RESEARCH BOARD
WASHINGTON, D.C.
2006
www.TRB.org
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TRANSPORTATION RESEARCH BOARD EXECUTIVE COMMITTEE 2005 (Membership as of November 2005)
OFFICERS
Chair: John R. Njord, Executive Director, Utah DOT
Vice Chair: Michael D. Meyer, Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology
Executive Director: Robert E. Skinner, Jr., Transportation Research Board
MEMBERS
MICHAEL W. BEHRENS, Executive Director, Texas DOT
ALLEN D. BIEHLER, Secretary, Pennsylvania DOT
LARRY L. BROWN, SR., Executive Director, Mississippi DOT
DEBORAH H. BUTLER, Vice President, Customer Service, Norfolk Southern Corporation and Subsidiaries, Atlanta, GA
ANNE P. CANBY, President, Surface Transportation Policy Project, Washington, DC
JOHN L. CRAIG, Director, Nebraska Department of Roads
DOUGLAS G. DUNCAN, President and CEO, FedEx Freight, Memphis, TN
NICHOLAS J. GARBER, Professor of Civil Engineering, University of Virginia, Charlottesville
ANGELA GITTENS, Vice President, Airport Business Services, HNTB Corporation, Miami, FL
GENEVIEVE GIULIANO, Director, Metrans Transportation Center, and Professor, School of Policy, Planning, and Development,
USC, Los Angeles
BERNARD S. GROSECLOSE, JR., President and CEO, South Carolina State Ports Authority
SUSAN HANSON, Landry University Professor of Geography, Graduate School of Geography, Clark University
JAMES R. HERTWIG, President, CSX Intermodal, Jacksonville, FL
GLORIA JEAN JEFF, Director, Michigan DOT
ADIB K. KANAFANI, Cahill Professor of Civil Engineering, University of California, Berkeley
HERBERT S. LEVINSON, Principal, Herbert S. Levinson Transportation Consultant, New Haven, CT
SUE MCNEIL, Professor, Department of Civil and Environmental Engineering, University of Delaware, Newark
MICHAEL R. MORRIS, Director of Transportation, North Central Texas Council of Governments
CAROL A. MURRAY, Commissioner, New Hampshire DOT
MICHAEL S. TOWNES, President and CEO, Hampton Roads Transit, Hampton, VA
C. MICHAEL WALTON, Ernest H. Cockrell Centennial Chair in Engineering, University of Texas, Austin
LINDA S. WATSON, Executive Director, LYNX--Central Florida Regional Transportation Authority
MARION C. BLAKEY, Federal Aviation Administrator, U.S.DOT (ex officio)
JOSEPH H. BOARDMAN, Federal Railroad Administrator, U.S.DOT (ex officio)
REBECCA M. BREWSTER, President and COO, American Transportation Research Institute, Smyrna, GA (ex officio)
GEORGE BUGLIARELLO, Chancellor, Polytechnic University, and Foreign Secretary, National Academy of Engineering (ex officio)
J. RICHARD CAPKA, Acting Administrator, Federal Highway Administration, U.S.DOT (ex officio)
THOMAS H. COLLINS (Adm., U.S. Coast Guard), Commandant, U.S. Coast Guard (ex officio)
JAMES J. EBERHARDT, Chief Scientist, Office of FreedomCAR and Vehicle Technologies, U.S. Department of Energy (ex officio)
JACQUELINE GLASSMAN, Deputy Administrator, National Highway Traffic Safety Administration, U.S.DOT (ex officio)
EDWARD R. HAMBERGER, President and CEO, Association of American Railroads (ex officio)
DAVID B. HORNER, Acting Deputy Administrator, Federal Transit Administration, U.S. DOT (ex officio)
JOHN C. HORSLEY, Executive Director, American Association of State Highway and Transportation Officials (ex officio)
JOHN E. JAMIAN, Acting Administrator, Maritime Administration, U.S.DOT (ex officio)
EDWARD JOHNSON, Director, Applied Science Directorate, National Aeronautics and Space Administration (ex officio)
ASHOK G. KAVEESHWAR, Research and Innovative Technology Administrator, U.S.DOT (ex officio)
BRIGHAM MCCOWN, Deputy Administrator, Pipeline and Hazardous Materials Safety Administration, U.S.DOT (ex officio)
WILLIAM W. MILLAR, President, American Public Transportation Association (ex officio)
SUZANNE RUDZINSKI, Director, Transportation and Regional Programs, U.S. Environmental Protection Agency (ex officio)
ANNETTE M. SANDBERG, Federal Motor Carrier Safety Administrator, U.S.DOT (ex officio)
JEFFREY N. SHANE, Under Secretary for Policy, U.S.DOT (ex officio)
CARL A. STROCK (Maj. Gen., U.S. Army), Chief of Engineers and Commanding General, U.S. Army Corps of Engineers (ex officio)
NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM
Transportation Research Board Executive Committee Subcommittee for NCHRP
J. RICHARD CAPKA, Federal Highway Administration MICHAEL D. MEYER, Georgia Institute of Technology
JOHN R. NJORD, Utah DOT (Chair) ROBERT E. SKINNER, JR., Transportation Research Board
JOHN C. HORSLEY, American Association of State Highway MICHAEL S. TOWNES, Hampton Roads Transit, Hampton, VA
and Transportation Officials C. MICHAEL WALTON, University of Texas, Austin
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NATIONAL COOPERATIVE HIGHWAY RESEARCH NCHRP REPORT 546
PROGRAM
Systematic, well-designed research provides the most effective Project 8-44
approach to the solution of many problems facing highway
administrators and engineers. Often, highway problems are of local ISSN 0077-5614
interest and can best be studied by highway departments ISBN 0-309-08846-1
individually or in cooperation with their state universities and
Library of Congress Control Number 2005936945
others. However, the accelerating growth of highway transportation
develops increasingly complex problems of wide interest to © 2006 Transportation Research Board
highway authorities. These problems are best studied through a
coordinated program of cooperative research. Price $29.00
In recognition of these needs, the highway administrators of the
American Association of State Highway and Transportation
Officials initiated in 1962 an objective national highway research
program employing modern scientific techniques. This program is
supported on a continuing basis by funds from participating
member states of the Association and it receives the full cooperation
and support of the Federal Highway Administration, United States NOTICE
Department of Transportation.
The project that is the subject of this report was a part of the National Cooperative
The Transportation Research Board of the National Academies
Highway Research Program conducted by the Transportation Research Board with the
was requested by the Association to administer the research
approval of the Governing Board of the National Research Council. Such approval
program because of the Board's recognized objectivity and reflects the Governing Board's judgment that the program concerned is of national
understanding of modern research practices. The Board is uniquely importance and appropriate with respect to both the purposes and resources of the
suited for this purpose as it maintains an extensive committee National Research Council.
structure from which authorities on any highway transportation
The members of the technical committee selected to monitor this project and to review
subject may be drawn; it possesses avenues of communications and
this report were chosen for recognized scholarly competence and with due
cooperation with federal, state and local governmental agencies, consideration for the balance of disciplines appropriate to the project. The opinions and
universities, and industry; its relationship to the National Research conclusions expressed or implied are those of the research agency that performed the
Council is an insurance of objectivity; it maintains a full-time research, and, while they have been accepted as appropriate by the technical committee,
research correlation staff of specialists in highway transportation they are not necessarily those of the Transportation Research Board, the National
matters to bring the findings of research directly to those who are in Research Council, the American Association of State Highway and Transportation
a position to use them. Officials, or the Federal Highway Administration, U.S. Department of Transportation.
The program is developed on the basis of research needs Each report is reviewed and accepted for publication by the technical committee
identified by chief administrators of the highway and transportation according to procedures established and monitored by the Transportation Research
departments and by committees of AASHTO. Each year, specific Board Executive Committee and the Governing Board of the National Research
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to the National Research Council and the Board by the American
Association of State Highway and Transportation Officials.
Research projects to fulfill these needs are defined by the Board, and
qualified research agencies are selected from those that have
submitted proposals. Administration and surveillance of research
contracts are the responsibilities of the National Research Council
and the Transportation Research Board. Published reports of the
The needs for highway research are many, and the National
NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM
Cooperative Highway Research Program can make significant
contributions to the solution of highway transportation problems of are available from:
mutual concern to many responsible groups. The program,
however, is intended to complement rather than to substitute for or Transportation Research Board
duplicate other highway research programs. Business Office
500 Fifth Street, NW
Washington, DC 20001
and can be ordered through the Internet at:
Note: The Transportation Research Board of the National Academies, the
National Research Council, the Federal Highway Administration, the American
Association of State Highway and Transportation Officials, and the individual
http://www.national-academies.org/trb/bookstore
states participating in the National Cooperative Highway Research Program do
not endorse products or manufacturers. Trade or manufacturers' names appear
herein solely because they are considered essential to the object of this report. Printed in the United States of America
OCR for page R5
The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished schol-
ars engaged in scientific and engineering research, dedicated to the furtherance of science and technology
and to their use for the general welfare. On the authority of the charter granted to it by the Congress in
1863, the Academy has a mandate that requires it to advise the federal government on scientific and techni-
cal matters. Dr. Ralph J. Cicerone is president of the National Academy of Sciences.
The National Academy of Engineering was established in 1964, under the charter of the National Acad-
emy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its administration
and in the selection of its members, sharing with the National Academy of Sciences the responsibility for
advising the federal government. The National Academy of Engineering also sponsors engineering programs
aimed at meeting national needs, encourages education and research, and recognizes the superior achieve-
ments of engineers. Dr. William A. Wulf is president of the National Academy of Engineering.
The Institute of Medicine was established in 1970 by the National Academy of Sciences to secure the
services of eminent members of appropriate professions in the examination of policy matters pertaining
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The National Research Council was organized by the National Academy of Sciences in 1916 to associate
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emy, the Council has become the principal operating agency of both the National Academy of Sciences
and the National Academy of Engineering in providing services to the government, the public, and the
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respectively, of the National Research Council.
The Transportation Research Board is a division of the National Research Council, which serves the
National Academy of Sciences and the National Academy of Engineering. The Board's mission is to promote
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COOPERATIVE RESEARCH PROGRAMS STAFF FOR NCHRP REPORT 546
ROBERT J. REILLY, Director, Cooperative Research Programs
CRAWFORD F. JENCKS, Manager, National Cooperative Highway Research Program
RONALD D. McCREADY, Senior Program Officer
EILEEN P. DELANEY, Director of Publications
NCHRP PROJECT 8-44 PANEL
Field of Transportation Planning--Area of Forecasting
TOM BRIGHAM, HDR Alaska, Inc., Anchorage, AK (Chair)
PHILIP B. DEMOSTHENES, Parametrix, Denver, CO
PRESTON J. ELLIOTT, Parsons Brinckerhoff, Nashville, TN
CYNTHIA A. GALLO, Massachusetts Bay Transportation Authority
DENISE JACKSON, Michigan DOT
KATHLEEN F. KRAUSE, FHWA
DANIEL MAGRI, Louisiana DOTD
EDWARD A. MIERZEJEWSKI, Center for Urban Transportation Research, Tampa, FL
CARMINE PALOMBO, Southeast Michigan Council of Governments
JILL L. HOCHMAN, FHWA Liaison
LORRIE LAU, FHWA Liaison
KEN LORD, FTA Liaison
MARLENE MARKISON, NHTSA Liaison
KIMBERLY FISHER, TRB Liaison
RICHARD PAIN, TRB Liaison
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This report describes the transportation planning process and discusses where and
FOREWORD how safety can be effectively addressed and integrated into long-range planning at the
By Ronald D. McCready state and metropolitan levels. This guidance manual should be especially useful to fed-
Senior Program Officer eral, state DOT, MPO, and local transportation planners, as well as other practitioners
Transportation Research and stakeholders concerned with addressing safety within transportation systems plan-
Board ning, priority programming, and project development planning.
National transportation policies and programs emerging out of the Intermodal Sur-
face Transportation Efficiency Act (ISTEA) and the Transportation Equity Act for the
21st Century (TEA-21) require transportation plans and decisions at the state and met-
ropolitan levels to take safety into account more directly. While safety is often men-
tioned in plan policies and goals, the short- and long-range planning and programming
processes rarely include safety initiatives and commitments in a comprehensive man-
ner. Further, the data collection, analytical support methods, performance monitoring,
and decision collaboration normally carried out as part of the planning process for facil-
ities and services do not adequately include safety.
Presently, within long-range transportation planning at the state and metropolitan
levels, current conditions, performance, and impacts can be assessed as the basis for
predicting future implications of plan alternatives in terms of system capacity, travel
demand, system condition, economic conditions, population, and land use. We can pre-
dict the impacts of pavement preservation and the future condition of highway conges-
tion and capacity deficiencies. Regarding safety, we can describe the current accident
and fatality rates and project them into the future; however, we cannot accurately pre-
dict future safety implications associated with transportation system improvements.
Similarly, while we can estimate, if not accurately predict, future effectiveness of var-
ious safety countermeasures, we are not able to assess their collective implications or
performance expectations on a systemwide basis. Thus, long-range transportation plan-
ning processes at the state and metropolitan levels need better analytical tools to iden-
tify current and likely future safety deficiencies and methods to address those deficien-
cies. Further, processes to create and promote communication and collaboration
between safety and transportation planning practitioners are essential in order to inte-
grate safety into long-range transportation planning and decision making. This need is
particularly acute because current national policy requires these long-range planning
processes to improve the safety and security of the transportation system for motorized
and non-motorized users.
The objective of this research was to develop a guidance manual for practitioners
that identifies and evaluates alternative ways to more effectively incorporate and inte-
grate safety considerations in long-range statewide and metropolitan transportation
planning and decision-making processes. The research encompasses the full range of
OCR for page R8
safety implications of facility and geometric improvements, capacity improvements,
operational improvements, population growth and other demographic issues, land use
decisions, and human behavior-related issues associated with all surface transportation
modes. It also includes recommendations for improvements to the tools, methods, and
procedures that support systems, corridor, and project planning.
Under NCHRP Project 8-44, "Incorporating Safety into Long-Range Transporta-
tion Planning," researchers at the University of Arizona and the Georgia Institute of
Technology focused on safety issues within the long-range transportation planning
processes of state DOTs and metropolitan planning organizations (MPOs) and included
the following: (1) a comprehensive review of recent literature on safety and how it is
addressed in long-range transportation planning; (2) a review of federal regulations and
guidance on safety issues in the planning process; and (3) case studies to synthesize
notable current practice in safety planning. A planning process was developed that
describes how and when various methods can best be applied in developing systems-
level transportation plans. The process addresses decision-making relationships; tech-
nical requirements (e.g., data and analytical methods); necessary staffing capabilities;
public involvement; interagency coordination; financial commitments; and methods for
tying the systems-planning considerations to more detailed processes such as corridor
planning, subarea planning, modal development planning, priority programming, and
project development. The guidance manual presents descriptions of a variety of ana-
lytical tools and software applications for conducting various safety analyses. It also
describes PLANSAF, a tool developed as part of the research to forecast safety effects
at the traffic analysis zone (TAZ) level or higher. Appropriate applications of the tool
are discussed in this appendix. Finally, guidance is provided for MPOs or DOTs to
develop their own set of safety forecasting models at the TAZ level.
The guidance manual, contained on the accompanying CRP-CD-62, is presented
in an interactive electronic format for easy use as a tool for planning practitioners.
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CONTENTS CRP-CD-62
SUMMARY
CHAPTER 1 Introduction
CHAPTER 2 What Is Meant by Safety as It Relates to Transportation Planning?
CHAPTER 3 Why Is Safety an Important Issue for the Transportation
Planning Process?
CHAPTER 4 Institutional Context for Incorporating Safety into
Transportation Planning
CHAPTER 5 The Transportation Planning Process
CHAPTER 6 Incorporating Safety Considerations into Transportation Planning
CHAPTER 7 Putting It All Together
REFERENCES
APPENDIX A Example State Safety Initiatives
APPENDIX B Federal Highway Safety Program Guidance
APPENDIX C Safety Tools
APPENDIX D Developing a Planning-Level Forecasting Model (PLANSAFE)
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National Cooperative Highway Research
Program: NCHRP 8-44
Guidance:
Incorporating Safety into Long-Range
Transportation Planning
By:
Simon Washington1 Michael Meyer2
Ida van Schalkwyk1 Eric Dumbaugh2
Sudeshna Mitra1
and
Mathew Zoll3
of Arizona, Tucson, AZ
1 University
2 Georgia Institute of Technology, Atlanta, GA
3 Consultant, Tucson, AZ
Prepared For the National Cooperative Highway Research Program, Transportation Research Board,
National Research Council, Washington, D.C.
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Incorporating Safety into Long-Range Transportation-Planning
APPENDIX D DEVELOPING A PLANNING LEVEL
FORECASTING MODEL (PLANSAFE)
Appendix C described the application of a PLANSAFE model for forecasting
crashes at the planning level. The focus in Appendix C was on forecasting crashes
(total, fatal, pedestrian, etc.) in future periods or for build scenarios for use in
planning applications. Primary uses include the setting of safety performance targets
and for feedback on development and/or growth scenarios.
This Appendix, in contrast, provides the details necessary to develop (as opposed
to apply) a planning level forecasting model. This appendix is intended to serve as a
resource for an agency that has both the desire and ability to develop their own set of
regression models for forecasting safety at the planning level. The motivation for
such an undertaking would be the desire to increase the confidence in the
relationships captured in the models using local or regional data instead of data from
other regions (Pima County, Maricopa County, and Michigan State).
This section is organized as follows. First, the limitations of planning level safety
forecasting models are described. The data requirements for such a model are then
discussed, followed by software requirements and required expertise. Development
of the datasets is followed by a discussion of the development of the statistical
models. Detailed development of the planning level safety predictions models is then
provided. Finally the methodology for GIS processing required to develop the
datasets are discussed.
LIMITATIONS OF PLANNING LEVEL SAFETY FORECASTING MODEL
A safety model at the planning level is fundamentally different than corridor and
site level safety models with which most safety professionals are familiar. The
differences need illumination so that model misuses are avoided. Following are the
limitations of these models.
· the model can only be used at a TAZ area level: it can not be used for corridor or
project-level-related assessments and analysis,
· the model is not suitable for bolstering arguments for or against particular safety,
land use, or transportation investments. In other words these models are
predictive in nature and intend to inform the analyst as to when certain outcomes
will occur; however, it they are not explanative models that describe why certain
outcomes occur.
· a geo-coded road network and linked accident and other transportation data
(refer to the section discussing data requirements) are required to develop the
model,
· the creation of the data sets necessary to develop the model requires the
transformation of census block group data to TAZ area which requires GIS
expertise,
· the modelling requires the careful identification of independent variables and the
selection of these variables requires considerable statistical modelling expertise,
and
· special expertise is required to prepare the dataset and to develop the model
(refer to Exhibit 87).
The model uses the linear regression model with logarithmic transformation of
the dependent variable. This distribution is sensitive to any correlation between
variables in the model and the selection of independent variables is therefore
essential for the successful development of this model. The professional can use a
147 Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE)
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Incorporating Safety into Long-Range Transportation-Planning
correlation matrix to assist with in the selection of independent variables during the
model development process.
DATA REQUIREMENTS OF PLANNING LEVEL SAFETY FORECASTING
MODEL
Both the development and use of the prediction model requires data by traffic
analysis zone (TAZ). TAZs are the smallest analysis unit. Larger units can be
analyzed by aggregating TAZs. For example, a change to a commute corridor that
impacts numerous TAZs can be modeled by considering the impacts of the project on
all affected TAZs.
The models require data sets referring to geographical areas such as census block
groups and transportation facility datasets in geospatial information systems.
Geographical information systems (GIS) are used extensively to develop the data sets
in support of these models. GIS layers in the development of the prediction model
include:
· The TAZ areas that makes up the area for the prediction model, as defined by the
transportation agencies of the area,
· Tracts and/or block groups as defined by the U.S. Census (the use of block
groups is recommended) with the associated demographics, socio-economics
and other data,
· The entire road network of the area: i.e., including facilities managed by the state,
counties, regional agencies, and local agencies,
· The federal functional classification of the entire road network of the area,
· The vehicle miles traveled on the road network on the area (can be calculated by
generating known section lengths and multiplying it with known section traffic
volumes),
· Bike facilities and routes,
· Transit facilities,
· Unique accident record identification numbers for accidents for a minimum of
one year and ideally three years, and
· Locations of institutions such as schools and police stations.
The details for the development of these datasets are described later in this
section.
SOFTWARE REQUIREMENTS
The analyst develops the model by using GIS software and statistical analysis
software, such as LIMDEP, SPLU.S., GENSTAT, SPSS, SAS, aML, etc. The researchers
at the University of Arizona used ArcGIS, and LIMDEP for the development of
models described in this section and in Appendix C.
REQUIRED EXPERTISE
The estimation of planning level safety forecasting models requires the following
expertise.
Development of datasets. GIS software-related expertise is required for the
preparation of data needed in the development of the model. The individual will
have to perform various types of GIS processing to assign data to the TAZ areas and
have a fair knowledge of vector and raster modeling and spatial analysis in the GIS
environment.
Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE) 148
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Incorporating Safety into Long-Range Transportation-Planning
Development of the models. The development of the statistical models, using the
dataset created for the model, requires experience in statistical modelling and
transportation safety. Knowledge about basic hypothesis testing, regression and the
ability to evaluate a model using goodness-of-fit are basic requirements. As the
development requires the use of statistical software such as LIMDEP or STATA, the
individual also has to be able to use the software and interpret the results provided
by the software. The individual should also be knowledgeable in the field of
transportation safety as the evaluation of the variables in the generated models
requires an understanding of the relationships between the variables and accident-
related variables.
DETAILED DEVELOPMENT OF A SAFETY PREDICTION MODEL AT THE
TAZ LEVEL
Exhibit 99 depicts the process that the analyst follows to develop the planning
level safety prediction model. The process consists of three basic steps:
· data collection,
· development of a dataset containing variables used in modelling, and
· development/estimation of the statistical models used for forecasting.
All of these activities support development of the planning level safety
prediction model. Before one begins this process, it is important to recognize that the
ultimate model drives all the activities preceding it. So, a review of the safety model
and what factors are thought to affect safety at the aggregate level is worthwhile at
this point.
Safety, as defined by total crashes, severe crashes, injury crashes, pedestrian
crashes, and bicycle crashes are influenced by numerous factors. These factors must
be viewed in the framework of aggregated data and crashes cannot be examined in
isolation. Exhibit 89 lists potential variables that may capture the underlying effects
listed in the first column. For example, weather is known to affect crashes, with wet,
ice, and snow affecting crashes considerably. At the TAZ level, the proportion of wet
pavement days may help to capture the variability in crashes observed within a TAZ.
Similarly, high risk driving populations are involved in crashes more frequently than
average drivers. Identifying the proportion of high risk drivers residing within a TAZ
may help to capture some of this effect--predominately those crashes that occur close
to home (which is a significant proportion). The list of variables listed in the table is
meant to provide a basis from which TAZ data collection is conducted. The list is not
exhaustive, but captures most of the major factors involved with crashes at the TAZ
level.
149 Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE)
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Incorporating Safety into Long-Range Transportation-Planning
Exhibit 99: Process
followed to develop DATA a) Collect road network related
PLANSAFE by TAZ for COLLECTION data: usually includes local,
planning level safety county and state road network
prediction & use dynamic segmentation to
assign mileage and other
attributes to the particular TAZ
Prepare a dataset by TAZ
b) Collect census related data by
area using GIS
block group and assign with
technologies such as
GIS technologies to the TAZ
dynamic segmentation and
polygons. Prepare a dataset
spatial joining.
with potential socio-economic
and demographic variables by
TAZ.
c) Collect crash data for at least
one year and develop a dataset
with potential crash related
variables by TAZ zone.
a) Prepare a correlation matrix
IDENTIFY
for the dataset using software
INDEPENDENT
Develop a set of such as Limdep or Stata
VARIABLES
independent variables using b) Identify variables that do not
FOR
road network characteristics, correlate with other variables
USE IN THE
socio-economic and c) Prepare dataset with
MODEL
demographics, and crash independent variables that
history can potentially be used for the
model
DEVELOP
THE MODEL
Develop an initial model using a set of independent variables generated in
previous step using Lindep or Stata and linear regression with the
transformation of the dependent variable.
Test the model by:
a) Determining the significance of each of the variables in the model
b) Determine whether the relationship provided by the model can be
logically explained
Repeat process and estimate a number of candidate models using
variations of variables and by adding, maintaining or dropping variables
based on tests required in previous step
Select the model with the best goodness-of-fit
Appendix D: Developing a Planning Level Safety Forecasting Model (PLANSAFE) 150
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Incorporating Safety into Long-Range Transportation-Planning
Major Contributing Factor Potential Aggregate (TAZ level) Variables that may capture
Exhibit 100: Major
effect of Major Factor (assumes time scale is year)
contributing factors in crashes
Weather Proportion of wet pavement days per year at the TAZ level and potential
Proportion of icy pavement days per year variables
Proportion of snow days per year
Proportion of fog/reduced visibility days per year
Proportion of sunny days per year
High risk driving populations Population/number of licensed drivers
Proportion of population between 16 and 24
Proportion of population over 60
Number of DUI arrests
Employed/unemployed workers
High risk non motorized Number of crosswalks
populations Number of schools (elementary, middle, high, college)
Percentage/mileage of sidewalks (of street mileage)
Percentage/mileage of bicycle facilities
Speed, design standards of Total street mileage
facilities, and access control Proportion of local road mileage
Proportion of collector road mileage
Proportion of arterial road mileage
Proportion of rural highway mileage (urban/rural)
Proportion of interstate (urban and rural)
Conflicts Number/proportion of signalized intersections
Number/proportion of stop-controlled intersections
Intersection density
Total area
DATA COLLECTION AND PREPARATION
During the data collection and preparation process, the analyst develops datasets
that tabulate the particular variable(s) per TAZ area. The major factors and their
associated variables (or similar ones) listed in Exhibit 100 serve as motivation for
obtaining certain information in the data collection phase.
The data collection effort for the TAZ based (planning level) safety prediction
model requires cooperation among the different transportation agencies in the
region. Data are collected at the different levels of agencies and sharing of data
between these agencies can present difficulties, it is therefore recommended that the
support of the state DOT, county and metropolitan/regional level be sought at the
start of the data collection process.
Typically, data will be gathered from the State DOT, the included counties, and,
in some cases, metropolitan/regional/local agencies. In some areas, there may also
be other agencies to consider and data sources will vary from area to area.
Typical data per TAZ area considered for inclusion into the model are:
· road network mileage by federal functional classification,
· accident data: a variety of variables can be generated varying from degree of
injuries sustained in the accidents, number of injuries and fatalities, or accident
types,
· census data: population, age distribution within a TAZ (e.g., number of
individuals age 17 and younger), employment, housing units: vacant and
occupied, persons with disabilities, etc., and
· traffic volume data: vehicle miles traveled.
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This section describes the data preparation process, the development of a dataset
for modeling, and the creation of a crash prediction model.
Data Preparation
As listed in Exhibit 100 the model development process uses information related
to the census, the road network, and historical accident data. The development of the
model requires a data matrix by TAZ area number.
During the data preparation process GIS technology is utilized to develop the
datasets. Specific issues that arise with respect to GIS are described in the next
subsection of this appendix. The ArcGIS environment is used but similar processing
can be performed in other GIS environments as the description is intended to provide
the sequence for processing operations in command line or graphical user interface
environments; or for scripted batch processing. Refer to the section titled Using GIs in
the Development of the Planning Level Safety Forecasting Model for a discussion of the
GIS processing procedures.
This section describes the four different data categories that can be considered for
a PLANSAFE model.
Road Network Data
During the development of the model, the following road network information
per TAZ, among others, the analyst can consider the following as potential variables:
· total mileage per functional class of all the roads, i.e., all state, county, regional,
and local streets,
· total number of intersections,
· positions of bus stop and transit facilities,
· mileage of bike facilities,
· portions of signalized and stop controlled intersections, and
· population and vehicle-miles-traveled.
Vehicle miles traveled by TAZ area is recognized as an important element of the
development of accident prediction models and the researchers recommended that
the data collection efforts for the Highway Performance Monitoring System (FHWA)
can be used for this purpose unless the agency has VMT data available for all the
road sections. It is also possible, however, that population serves as a sufficient
exposure metric, as it is probably more accurate than VMT in its measurement.
Having both may be the best approach for model testing and refinement.
VMT may be approximated by multiplying average annual daily traffic (AADT)
for a particular road section by the length of the road section. This requires that the
analyst ensures that road segments that make up the road network be provided with
a unique segment identifier that can be linked to a unique road segment identifier
within the HPMS data set. In some cases it may be necessary to obtain the HPMS
data on a county level and also on a state level to ensure that such unique route
identifiers exist.
Careful attention needs to be paid during the assignment of mileage to the
different TAZ areas to ensure that arcs representing the road network do not get lost
due to complex GIS-related calculations. It would therefore be valuable to calculate
the total mileage per functional class for the entire area and then for the different
TAZ areas and compare the total mileage per class with the sum of the mileage per
class per TAZ values to ensure that all the sections are included in the dataset.
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Census Data
The U.S. Census data for SF1 and SF3 is used to identify potential variables
related to socio-economic, demographic, and employment data.
Based on the case studies presented as part of this section, it is recommended
that the census data be transformed from a block-group level to the TAZ level.
Census data is not reported by a sub-area where the data can be personally
identifiable, i.e., variables with low frequencies in an area may be presented as zero
values in the data from the census. This causes false zeros in block data. The tract
areas, on the other hand, is large compared to the TAZ area and is therefore expected
to generalize the data too much when it is transformed to the TAZ area.
Census data can either be downloaded from the U.S. Census website through the
American Fact Finder web page at http://factfinder.census.gov/ or by creating
datasets by using the U.S. Census 2000 Data Engine CD's that are available per state
per SF1 and SF3. NCHRP 8-48 is currently reviewing the use of the new American
Community Survey data for transportation-planning and can potentially be a source
of data for the development of the prediction model.
In some cases transit and other transportation studies generate data that can be
used in the development of the model. These data are generally available per census
tract and in these cases can be transformed into TAZ level data.
The next section presents step-by-step instructions to transform census block-
group data or data per tract or other sub area to TAZ areas in ArcGIS (refer to the
section titled Using GIS in the Development of the Planning Level Prediction Model).
In the GIS environment, the block group data are assumed uniform and the
assignment to the TAZ is done using proportion per area of overlap.
Institutions
The number of relevant institutions per TAZ, such as police stations, schools,
colleges, and universities are considered as potential variables for the model. The
final section of this appendix provides step-by-step instructions to calculate the
frequencies of each of these institutions per TAZ area.
Accident History
Accident data is geo coded in a number of different ways and the GIS
environment is used to generate the outcome variables that are considered during the
model development process.
The analyst uses a shape file containing the point events, i.e., accidents, by
unique accident report number, together with a shape file containing the TAZ
boundaries, to generate of a data set that contains the unique accident report number
and the TAZ area it is located in (refer to the step-by-step instructions to calculate the
frequencies of each of these institutions per TAZ. The data set can then be used to
generate a table of frequencies of accidents per TAZ by summarizing the data points
per TAZ.
Accident-related variables to be investigated as possible accident outcome
predictions: accident severity, injuries sustained in the accident, pedestrian involved
crashes, fatal crashes, and other accident-related variables.
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Development of a dataset containing modelling variables
The next step in the process of developing a planning level safety prediction
model is the development of a data set containing independent variables. It is
recommended that a correlation matrix be prepared to assist the statistical specialist
in this effort. The correlation matrix is helpful for identifying which variables are
capturing essentially the same or similar underlying phenomenon. The use of
variables described in previous sections will motivate the development of this
variable list. This step requires the use of database management software such as MS
Excel, Access, or other database management system. Finally, prior to modelling, all
variables should be examined individually to determine whether the variables make
sense. Reasonable checks for reasonableness include computing means, medians,
modes, maximums, and minimum values of all variables in the database. Often times
coding and transcription errors can be detected during this process so as to avoid
negative influences on the modelling results.
Development of Crash Prediction Model
The researchers of NCHRP 8-44 developed a safety prediction model by using
the following approach and assumptions:
· Accident count distribution. Accident counts are assumed to be well
approximated by the negative binomial distribution when observed per unit area
or per unit time (e.g., crashes at intersections for one year each). A linear
regression model with logarithmic transformation of the count data will produce
a satisfactory model when data are aggregated at the TAZ level (i.e., lots of
intersections, road segments, etc.) and TAZs are of varying sizes. Mean crash
frequencies are thought to vary across TAZs due to unobserved characteristics of
the TAZs.
· Simultaneity of accident occurrences. Simultaneous model estimation techniques
may be used to model the simultaneity of the accident occurrences (see
Washington, Karlaftis, and Mannering, 2004, "Statistical and Econometric
Methods for Transportation Data Analysis", Chapman Hall, for details on
simultaneous model estimation techniques). This need arises due to the likely
correlation of error terms across crash prediction models. If modeled separately
(and not simultaneously) the coefficients will be inefficient.
· Variables maintained due to statistical significance and agreement with
expectation. Variables are maintained in the models by determining the
significance level (95% is accepted as a minimum) and by assessing whether the
relationships between the particular variable and accident outcome, including
direction of the effect, agrees with theoretical expectations of accident outcomes.
· Error terms correlated across models. The error terms in the models are thought
to consist of omitted variables and measurement errors. Omitted variables are
assumed to affect all accident injury outcomes (e.g., fatal, serious, slight, total
injuries) and the original error term in the model is not correlated to the
observable variables.
· Contemporaneous correlation. During model estimation additional information
from contemporaneous correlation is used. The simultaneous equations are
solved by using system estimation methods such as the three-stage least squares.
· Simultaneous negative binomial equations. An iterative estimation process is
followed using a likelihood maximization algorithm until convergence is
achieved and parameters are estimated
· Measurement of Goodness of Fit. The goodness of fit for the simultaneous model
system is assessed using the R2 statistic, and individual t-statistics for variables.
P P
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Modelling trial and error is used to derive meaningful and useful models.
Knowledge of transportation safety is used to derive a model that is consistent and in
agreement with current knowledge of motor vehicle crashes and safety.
U.S.ING GIS IN THE DEVELOPMENT OF THE PLANNING LEVEL SAFETY
FORECASTING MODEL
The Planning Level Safety Prediction Model requires the analyst to perform
various calculations within the GIS environment. The purpose of this section is to
describe a general methodology for the processing of data within the GIS
environment.
· Creating census data sets per TAZ, i.e., distribution of demographic data in
block groups to TAZ areas by assuming uniformity of values in block groups,
· Creating accident data sets per TAZ, i.e., assignment of total road mileage to each
TAZ
· Creating road mileage and VMT summary sets for the road network by TAZ, i.e.,
association of accident events (points) with the TAZ.
The ArcGIS environment is used but similar processing can be performed in
other GIS environments as the description is intended to provide the sequence for
processing operations in command line or graphical user interface environments; or
for scripted batch processing.
Conceptual Framework
This section places the described methodologies within a conceptual framework
for conceptualizing the data processing.
The association of the attributes of TAZ by their spatial relationship with the
attributes of other spatial themes, such as traffic accidents and census block groups is
a fundamental function of GIS. Overlay functions handle the association of the
attributes of one feature class with those in another feature class. Once the attributes
are feature classes are associated the values of an attribute of one feature class can be
summarized by the values of another. For example, the summarization of
demographic data by TAZ to produce proportional population counts for each TAZ.
Since the transportation data (daily trip counts, etc.) are associated with the zones of
the TAZ, it is the proportional demographic data, for example, that will be associated
with the TAZ numbers. The proportional population counts can then, be summarized
by TAZ number for further statistical processing. One of the important assumptions
of this method is the uniform distribution of persons and person characteristics
within a census block group.
Methods
This section discusses the methodologies that could be used to perform the GIS
processing needed in the process of creating census, road mileage, and accident data
per TAZ.
Distribution of demographic data in block groups to TAZ areas
Census data sets can be obtained from the U.S. Census or the agency responsible
for the area. To enable the analyst to summarize census data per TAZ, the following
are needed:
· A shape file with the geographic boundaries of the census block groups for the
corresponding census data collection year this file should match the datum,
projection coordinate system and units of any other shape files. The boundaries
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are then associated with a database file, either in Microsoft Access or in dbf
format, that contains the census data.
· A shape file with the geographic boundaries of the TAZs for the area.
Exhibit 101 describes the data that are required to perform the GIS processing.
Exhibit 101: Data Type Name Description
required to distribute Feature class polygon block_groups U.S. Census Block Groups
demographic data in Feature class polygon TAZ Traffic Analysis Zones
block groups to TAZ
areas
The GIS processing steps are as follows:
1. Obtain required digital data sets with metadata
2. Verify spatial and attribute domains
3. Normalize spatial data sets to common projection and datum
4. Vertically integrate data sets
5. Calculate density for census block groups
6. Overlay TAZ and census block feature classes
7. Calculate population for unioned polygon feature class
8. Summarize counts by TAZ for output unioned feature class polygon
Assignment of total road mileage to each TAZ
Some of the variables considered during the development of a planning level
safety prediction model and subsequently required during the application of the
model, includes the length of roads within a particular TAZ with a particular
functional classification or characteristic. To generate such a data set, the analyst
needs the following:
· A shape file containing the TAZ boundaries
· A shape file containing the road network and associated characteristic values for
the road sections that makes up the road network.
Exhibit 102 describes the data that are required to perform the GIS processing.
Exhibit 102: Data Type Name Description
required to assign road Feature class polygon TAZ Traffic Analysis Zones
mileage to TAZ areas Feature Class line Street_network Line theme of road network
The GIS processing steps are as follows:
1. Obtain required digital data sets with metadata
2. Verify spatial and attribute domains
3. Normalize spatial data sets to common projection and datum
4. Vertically integrate data sets
5. Overlay street network and TAZ boundaries
6. Summarize counts by output intersected feature class line
7. Associate summary street length values with TAZ polygons
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Association of accident events (points) with the TAZ
In the planning level safety prediction model, the analyst uses the frequency of
accidents or severity of accidents or any other related events per TAZ. The analyst
therefore has to develop a data set that summarizes the particular data points within
each TAZ.
Exhibit 103 describes the data that are required to perform the GIS processing.
Type Name Description
Exhibit 103: Data required to
Feature class polygon TAZ Traffic Analysis Zones assign accidents to TAZ areas
Accident Location Data Accidents Database Table
The GIS processing steps are as follows:
1. Obtain required digital data sets with metadata.
2. Verify spatial and attribute domains.
3. Classify and scrub accident data for subprocessing procedures.
· Build route systems
· Calibrate route systems
· Create event theme for linear reference accidents
OR
· Verify reference theme for address matching
· Create address locator service
· Geocode addresses
4. Derive point feature class for georeferenced accident locations.
5. Overlay point feature class accidents on TAZ polygons.
6. Summarize accidents by TAZ number.
References
Dixon, Michael P., Brent Orton, and Karl Chang. GIS Input Processing
Methodologies for Transportation-planning Models.
http://www.featureanalyst.com/UserConf/papers/Orton/Orton%20GIS%20Pa
HTU
per.pdf (March 8, 2005).
UTH
O'Neill, Wende A. and Daniel Baldwin Hess. 1999. Using GIS to Evaluate a
New Source of Transportation Census Data: The American Community Survey.
Available at http://www.fcsm.gov/99papers/oneill.html. (March 9, 2005).
HTU UTH
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