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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
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| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-14-057 Date: February 2018 |
Publication Number: FHWA-HRT-14-057 Date: February 2018 |
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Access management (AM) is the process that provides (or manages) access to land development while preserving safety, capacity, and speed on the surrounding road network. A growing number of agencies have included closing, consolidating, or improving driveways, median openings, and intersections as part of their AM implementation strategy. However, these same agencies are often challenged to provide rigorous justifications that explain the safety benefits of their policies, practices, and strategies.
The objective of this research was to develop crash prediction models for evaluating the safety effects of corridor AM policies and strategies on urban, suburban, and urbanizing arterials. Corridor-level crash prediction models were developed using more than 600 mi of detailed corridor data from four different regions in the United States. Agencies can use the crash prediction models to assess the safety impacts of their decisions related to corridor AM.
Monique R. Evans, P.E., CPM
Director, Office of Safety, Research
and Development
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.
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Technical Report Documentation Page
| 1. Report No.
FHWA-HRT-14-057 |
2. Government Accession No.
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3 Recipient's Catalog No.
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| 4. Title and Subtitle
Safety Evaluation of Access Management Policies and Techniques |
5. Report Date
February 2018 |
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| 6. Performing Organization Code
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| 7. Author(s)
Frank Gross, Craig Lyon, Bhagwant Persaud, Jerome Gluck, Matt Lorenz, and Scott Himes |
8. Performing Organization Report No.
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| 9. Performing Organization Name and Address
Vanasse Hangen Bustlin, Inc. (VHB) |
10. Work Unit No. (TRAIS)
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| 11. Contract or Grant No.
DFTH61-09-C-00026 |
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| 12. Sponsoring Agency Name and Address
Office of Safety Research and Development |
13. Type of Report and Period Covered
Final Report: October 2009-October 2013 |
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| 14. Sponsoring Agency Code
HRDS-10 |
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| 15. Supplementary Notes
The Contracting Officer’s Representative of this project is Dr. Wei Zhang (HRDS-10). Technical panel members include Dr. Joe Bared and Neil Spiller. |
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| 16. Abstract
Access management (AM) is the process that provides (or manages) access to land development while preserving safety, capacity, and speed on the surrounding road network. These benefits have been increasingly recognized at all levels of government, and a growing number of agencies 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 AM implementation strategy. However, these decisions are often challenged for various reasons, and there have been few scientifically rigorous evaluations to quantify the safety effects of corridor AM. As such, there is a need to provide additional information to help rationalize decisions related to AM so that agencies can better explain the safety benefits of their policies and practices. This study seeks to fill some of the safety-related research gaps—namely, to quantify the safety impacts of corridor AM decisions.
The objective of this research was to evaluate the safety effects of corridor AM policies and strategies on urban, suburban, and urbanizing arterials. Crash prediction models were developed using more than 600 mi of detailed corridor data from four different regions in the United States. The crash prediction models were estimated using generalized linear modeling. Agencies can use the crash prediction models to assess the safety impacts of their decisions related to corridor AM. |
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| 17. Key Words
Access management, safety analysis, crash prediction models |
18. Distribution Statement
No restrictions. This document is available to the public through NTIS: National Technical Information Service, Springfield, VA 22161. |
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| 19. Security Classification (of this report) Unclassified |
20. Security Classification (of this page) Unclassified |
21. No. of Pages
176 |
22. Price
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| Form DOT F 1700.7 (8-72) | Reproduction of completed page authorized |
SI* (Modern Metric) Conversion Factors
Figure 1. Illustration. Unsignalized driveway spacing.
Figure 2. Illustration. Comparison of uniform and nonuniform signal spacing (figure 5 in Gluck, Levinson, and Stover, 1999).(3)
Figure 3. Illustration. Intersection physical area versus functional area (adapted from Transportation Research Circular 456, figure 4).(4)
Figure 4. Illustration. Allowable traffic movements before and after raised median installation (figure 30 in Gluck, Levinson, and Stover, 1999).(3)
Figure 5. Illustration. Potential frontage road configuration.
Figure 6. Illustration. Improved access configuration with cross connectivity.
Figure 7. Graph. Relationship between total access points per mile and crash rate (figure 24 in Gluck, Levinson, and Stover, 1999).(3)
Figure 8. Graph. Relationship between access points per mile and crash rate (figure 26 in Gluck, Levinson, and Stover, 1999).(3)
Figure 9. Graph. Relationship between signals per mile and crash rate (figure 6 in Gluck, Levinson, and Stover, 1999).(3)
Figure 10. Photo. Example of an urban arterial in a residential area.
Figure 11. Photo. Example of a suburban arterial in a commercial area.
Figure 12. Illustrations and photos. Verifying HSIS data with aerial photos.(15)
Figure 13. Screen shot. Verifying data with aerial imagery.(16)
Figure 14. Photo. Verifying data with video.
Figure 15. Image. Example of point objects for a 1-mi corridor.(17)
Figure 16. Photo. Data collection equipment used for field visit.
Figure 17. Equation. General form of crash prediction model.
Figure 18. Equation. Crash prediction model with regional calibration.
Figure 19. Equation. Normalized crash prediction model with regional calibration.
Figure 20. Equation. Formula to estimate effects of variables of interest for existing conditions.
Figure 21. Equation. Formula to estimate w.
Figure 22. Equation. Formula to estimate the annual expected crash frequency (EB estimate).
Figure 23. Equations. Calculation of predicted right-angle crashes/year (existing).
Figure 24. Equations. Calculation of predicted right-angle crashes/year (proposed).
Figure 25. Equations. Effect of ACCDENS: predicted total crashes/year (existing).
Figure 26. Equations. Effect of ACCDENS: predicted total crashes/year (proposed).
Figure 27. Equations. Effect of PROPNODEV: predicted total crashes/year (existing).
Figure 28. Equations. Effect of PROPNODEV: predicted total crashes/year (proposed).
Figure 29. Equations. Total crashes: predicted total crashes/year (existing).
Figure 30. Equations. Total crashes: predicted crashes/year (proposed).
Figure 31. Equations. Turning crashes: predicted turning crashes/year (existing).
Figure 32. Equations. Turning crashes: predicted turning crashes/year (proposed).
Figure 33. Equations. Right-angle crashes: predicted right-angle crashes/year (existing).
Figure 34. Equations. Right-angle crashes: predicted right-angle crashes/year (proposed).
Figure 35. Equations. Baseline predicted right-angle crashes/year (existing alternative A).
Figure 36. Equations. Estimate of w.
Figure 37. Equations. Expected right-angle crashes/year (existing alternative A).
Figure 38. Equation. Estimate EB correction factor.
Figure 39. Equations. Predicted right-angle crashes/year (existing alternative A).
Figure 40. Equations. Predicted right-angle crashes/year (proposed alternative B).
Figure 41. Equations. Predicted right-angle crashes/year (proposed alternative C).
Figure 42. Equations. Adjusted predicted right-angle crashes/year for existing condition.
Figure 43. Equations. Expected right-angle crashes/year for proposed condition.
Figure 44. Equations. Baseline predicted right-angle crashes/year (existing).
Figure 45. Equation. Estimate of the impacts of the variables of interest for existing conditions.
Figure 46. Equations. Estimation of multipliers.
Figure 47. Equations. Adjusted predicted right-angle crashes/year (existing alternative A).
Figure 48. Equations. Estimation of multipliers.
Figure 49. Equations. Adjusted predicted right-angle crashes/year (proposed alternative B).
Figure 50. Equation. Estimated calibration factor.
Figure 51. Equation. Original multiplier.
Figure 52. Equation. Estimation of calibrated multiplier.
Figure 53. Equations. Minnesota crash prediction model.
Table 1. Prioritization of AM policies and techniques.
Table 2. Strategies/policies in relation to AM safety principles (adapted from V.G. Stover, 2007).(1)
Table 3. Area type and land use categories.
Table 4. Potential issues and opportunities related to cross-sectional studies.
Table 5. Objects and characteristics coded in ArcGIS™.
Table 6. Number of corridors by area type and land use.
Table 7. Mileage of corridors by area type and land use.
Table 8. Crash type definitions.
Table 9. North Carolina mileage by area type and land use.
Table 10. Summary statistics for North Carolina independent variables.
Table 11. Summary statistics for North Carolina dependent variables.
Table 12. Northern California mileage by area type and land use.
Table 13. Summary statistics for Northern California independent variables.
Table 14. Summary statistics for Northern California dependent variables.
Table 15. Southern California mileage by area type and land use.
Table 16. Summary statistics for Southern California independent variables.
Table 17. Summary statistics for Southern California dependent variables.
Table 18. Minnesota mileage by area type and land use.
Table 19. Summary statistics for Minnesota independent variables.
Table 20. Summary statistics for Minnesota dependent variables.
Table 21. Overview of mixed-use models by crash type.
Table 22. Overview of commercial models by crash type.
Table 23. Overview of residential models by crash type.
Table 24. Relevant models by crash type of interest—mixed land use.
Table 25. Relevant models by crash type of interest—commercial land use.
Table 26. Relevant models by crash type of interest—residential land use.
Table 27. Correlation coefficients for MINSPCSIG (model coefficient, p-value).
Table 28. Correlation coefficients for NOLTLSIG (model coefficient, p-value).
Table 29. Driveway density CMFs inferred from the Highway Safety Manual predictive models for multivehicle crashes on urban four-lane undivided and divided arterials.(22)
Table 30. Comparison of implied CMFs for SIGDENS.
Table 31. HSIS data obtained for California.
Table 32. HSIS data obtained for Minnesota.
Table 33. HSIS data obtained for North Carolina.
Table 34. Alternate model 1 for mixed-use total crashes.
Table 35. Alternate model 2 for mixed-use total crashes.
Table 36. Alternate model 3 for mixed-use total crashes.
Table 37. Alternate model 1 for mixed-use injury crashes.
Table 38. Alternate model 2 for mixed-use injury crashes.
Table 39. Alternate model 1 for mixed-use turning crashes.
Table 40. Alternate model 2 for mixed-use turning crashes.
Table 41. Alternate model 3 for mixed-use turning crashes.
Table 42. Alternate model 1 for mixed-use rear-end crashes.
Table 43. Alternate model 2 for mixed-use rear-end crashes.
Table 44. Alternate model 1 for mixed-use right-angle crashes.
Table 45. Alternate model 2 for mixed-use right-angle crashes.
Table 46. Alternate model 3 for mixed-use right-angle crashes.
Table 47. Alternate model 1 for commercial total crashes.
Table 48. Alternate model 2 for commercial total crashes.
Table 49. Alternate model 1 for commercial injury crashes.
Table 50. Alternate model 2 for commercial injury crashes.
Table 51. Alternate model 3 for commercial injury crashes.
Table 52. Alternate model 4 for commercial injury crashes.
Table 53. Alternate model 1 for commercial turning crashes.
Table 54. Alternate model 2 for commercial turning crashes.
Table 55. Alternate model 1 for commercial rear-end crashes.
Table 56. Alternate model 2 for commercial rear-end crashes.
Table 57. Alternate model 1 for commercial right-angle crashes.
Table 58. Alternate model 2 for commercial right-angle crashes.
Table 59. Alternate model 1 for residential total crashes.
Table 60. Alternate model 2 for residential total crashes.
Table 61. Alternate model 3 for residential total crashes.
Table 62. Alternate model 4 for residential total crashes.
Table 63. Alternate model 1 for residential injury crashes.
Table 64. Alternate model 2 for residential injury crashes.
Table 65. Alternate model 1 for residential turning crashes.
Table 66. Alternate model 2 for residential turning crashes.
Table 67. Alternate model 3 for residential turning crashes.
Table 68. Alternate model 4 for residential turning crashes.
Table 69. Alternate model 1 for residential rear-end crashes.
Table 70. Alternate model 2 for residential rear-end crashes.
Table 71. Alternate model 3 for residential rear-end crashes.
Table 72. Alternate model 1 for residential right-angle crashes.
Table 73. Alternate model 2 for residential right-angle crashes.
Table 74. Alternate model 3 for residential right-angle crashes.
Table 75. Summary statistics for North Carolina mixed-use land use.
Table 76. Summary statistics for Minnesota mixed-use land use.
Table 77. Summary statistics for Northern California mixed-use land use.
Table 78. Summary statistics for Southern California mixed-use land use.
Table 79. Summary statistics for North Carolina commercial land use.
Table 80. Summary statistics for Minnesota commercial land use.
Table 81. Summary statistics for Northern California commercial land use.
Table 82. Summary statistics for Southern California commercial land use.
Table 83. Summary statistics for North Carolina residential land use.
Table 84. Summary statistics for Minnesota residential land use.
Table 85. Summary statistics for Northern California residential land use.
Table 86. Summary statistics for Southern California residential land use.
Table 87. Correlation coefficients for mixed land use.
Table 88. Correlation coefficients for commercial land use.
Table 89. Correlation coefficients for residential land use.
| 4D | four-lane divided arterial |
| 4U | four-lane undivided arterial |
| AADT | annual average daily traffic |
| AASHTO | American Association of State Highway and Transportation Officials |
| ACCDENS | number of driveways plus unsignalized intersections per mile |
| ADT | average daily traffic |
| AM | access management |
| AVGAADT | average of the annual average daily traffic |
| b | coefficient estimated for the annual average daily traffic term in the models |
| ci | vector of coefficients estimated for independent variables included in the models |
| CMF | crash modification factor |
| DRWYDENS | number of driveways per mile |
| EB | empirical Bayes |
| FHWA | Federal Highway Administration |
| GIS | Geographic Information System |
| GLM | generalized linear modeling |
| GPS | Global Positioning System |
| HSIS | Highway Safety Information System |
| ID | identity |
| k | dispersion parameter |
| MAXSPCSIG | maximum spacing of signalized intersections |
| MEDOPDENS | number of median openings per mile |
| MEDOPLT | number of median openings with a left-turn lane |
| MEDOPNOLT | number of median openings without a left-turn lane |
| MINSPCSIG | minimum spacing of signalized intersections |
| MVMT | million vehicle-miles traveled |
| NO3LEGFULLUNSIG | number of three-legged full-movement unsignalized intersections |
| NO3LEGLFMOUNSIG | number of three-legged unsignalized intersections with no left-turn movement from crossroad |
| NO3LEGLIMUNSIG | number of three-legged limited-movement unsignalized intersections |
| NO3LEGRIROUNSIG | number of three-legged right-in/right-out unsignalized intersections |
| NO3LEGSIG | number of three-legged signalized intersections |
| NO3LEGUNSIG | number of three-legged unsignalized intersections |
| NO4LEGFULLUNSIG | number of four-legged full-movement unsignalized intersections |
| NO4LEGLFMOUNSIG | number of four-legged unsignalized intersections with no left-turn movement from crossroad |
| NO4LEGLIMUNSIG | number of four-legged limited-movement unsignalized intersections |
| NO4LEGRIROUNSIG | number of four-legged right-in/right-out unsignalized intersections |
| NO4LEGSIG | number of four-legged signalized intersections |
| NO4LEGUNSIG | number of four-legged unsignalized intersections |
| NO5LEGSIG | number of five-legged signalized intersections |
| NO5LEGUNSIG | number of five-legged unsignalized intersections |
| NOCOMFULLDRWY | number of commercial full-movement driveways |
| NOCOMLIMDRWY | number of commercial limited-movement driveways |
| NODRWYS | number of driveways |
| NOLTLSIG | number of signalized intersections with a left-turn lane on the mainline |
| NOLTLUNSIG | number of unsignalized intersections with a left-turn lane |
| NOMEDOP | number of median openings |
| NOMEDOPLT | number of median openings with a left-turn lane |
| NOMEDOPNOLT | number of median openings without a left-turn lane |
| NORESFULLDRWY | number of residential full-movement driveways |
| NORESLIMDRWY | number of residential limited-movement driveways |
| NORTLSIG | number of signalized intersections with a right-turn lane on the mainline |
| NORTLUNSIG | number of unsignalized intersections with a right-turn lane |
| NOSIG | number of signalized intersections |
| NOUNSIG | number of unsignalized intersections |
| PC | personal computer |
| PROPDIV | proportion of corridor length with divided median |
| PROPFRONTRD | proportion of corridor length with a frontage road |
| PROPFULLDEV | proportion of corridor length with full roadside development |
| PROPLANE1 | proportion of corridor length with two lanes |
| PROPLANE2 | proportion of corridor length with three or four lanes |
| PROPLANE3 | proportion of corridor length with five or more lanes |
| PROPLIGHT | proportion of corridor length with illumination present |
| PROPLIMCONN | proportion of corridor length with limited connectivity on adjacent developments |
| PROPMODCONN | proportion of corridor length with moderate connectivity on adjacent developments |
| PROPNODEV | proportion of corridor length with no roadside development |
| PROPPARTDEV | proportion of corridor length with partial adjacent development |
| PROPPOORPVMNT | proportion of corridor length with a poor pavement condition |
| PROPSIGCONN | proportion of corridor length with significant connectivity on adjacent developments |
| PROPTWLTL | proportion of corridor length with two-way left-turn lane |
| PROPUNDIV | proportion of corridor length with an undivided median |
| PROPVC | proportion of corridor length with visual clutter |
| SIGDENS | number of signalized intersections per mile |
| SPCOFFLT | minimum spacing from off-ramp to available left turn onto mainline from same side of road |
| SPCOFFRT | minimum spacing from off-ramp to available right turn onto mainline from same side of road |
| SPCON | minimum spacing from on-ramp to available right turn onto mainline from same side of road |
| SPEED_ LIMIT | posted speed limit |
| TWLTL | two-way left-turn lane |
| UNSIGDENS | number of unsignalized intersections per mile |
| USGS | United States Geological Survey |
| vpd | vehicles per day |
| w | EB weight |
| Xi | vector of independent variables included in the model |
