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Assessing Interactions Between Access Management Treatments and Multimodal Users (2018)

Chapter: Chapter 4: Findings and Applications

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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
×
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
×
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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Suggested Citation:"Chapter 4: Findings and Applications." National Academies of Sciences, Engineering, and Medicine. 2018. Assessing Interactions Between Access Management Treatments and Multimodal Users. Washington, DC: The National Academies Press. doi: 10.17226/25344.
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34 C H A P T E R 4 : F I N D I N G S A N D A P P L I C A T I O N S Findings and Applications Introduction This chapter describes the results of the research that are suitable for practical application. The first section describes the predictive models that were developed for evaluating the effect of selected access management (AM) techniques on the safety or operation of various travel modes. The second section provides an overview of the Guide for the Analysis of Multimodal Corridor Access Management that was developed during this project. The predictive models developed in this project are incorporated in the Guide to facilitate their implementation. Development of Performance Relationships This section describes the performance relationships (i.e., predictive models) that were developed for two AM techniques. The first section describes the relationships developed for the right-turn deceleration technique. The original title of this technique is 4a. Install right-turn deceleration lane. The second section describes the relationships developed for the TWLTL and non-traversable median types. These relationships are intended to support the evaluation of the following AM techniques: 2a. Install non- traversable median on undivided highway, 2b. Replace TWLTL with non-traversable median, and 3c. Install continuous two-way left-turn lane on undivided highway. Right-Turn Deceleration The specific travel modes and associated performance relationships of interest to the right-turn deceleration technique are identified in Table 11. As indicated by the grey shaded cells in the table, performance relationships were found in the literature for three of the eight travel mode/performance measure categories. As a result, the focus of this project was on the development of new relationships for the remaining five travel mode/performance measure categories. All of the relationships identified in the table are documented in the Guide for the Analysis of Multimodal Corridor Access Management. The relationships that were developed for this project are also described in Appendix G, using the equation numbers provided in the table.

35 Table 11. Performance relationships to evaluate right-turn deceleration lane installation. Performance Measure Category Performance Relationship (PR) by Travel Mode1 Pedestrian Bicycle Transit Truck Operations 1. LOS score. Use HCM MMLOS method. 2. Intersection delay. HCM ped. delay method Intersection delay. Appendix G, Equation 5. Intersection delay. Appendix G, Equation 6. Intersection delay. Appendix G, Equation 7. Safety 1. Ped. crash frequency. Potts et al. model. 2. Safety index. Carter et al. model. Safety index. Carter et al. model. Transit-related conflict frequency. Appendix G, Equation 9 and Equation 10. Truck-related conflict frequency. Appendix G, Equation 11 and Equation 12. Note: 1. Relationships identified in grey shaded cells refer to predictive methods described in the referenced document. For convenience, these methods are summarized in the Appendix of the Guide for the Analysis of Multimodal Corridor Access Management. Acronyms used in the table are defined as follows: HCM – Highway Capacity Manual (TRB, 2016). MMLOS – multimodal level of service. The equations identified in the first row of Table 11 were developed to predict the operational performance of the corresponding travel mode. Table 12 indicates the trend in predicted performance associated with each travel mode and equation variable. Specifically, the table identifies the independent variables in the equations and the effect of these variables on bicycle, transit, or truck delay. For example, signal cycle length is shown in the third row of the table. For each of the three travel modes identified, an increase in cycle length was found to increase delay. Thus, an increase in cycle length is associated with a decrease in operational performance. Table 12. Operations model variables – right-turn deceleration. Factor Effect of a Change in Factor on Performance Bicycle Transit Truck Add right-turn deceleration lane improve perf. ▲ improve perf. ▲ improve perf. ▲ Increase right-turn decel. lane length — improve perf. ▲ improve perf. ▲ Increase signal cycle length decrease perf.↓ decrease perf.↓ decrease perf.↓ Allow right-turn-on-red operation — improve perf. ▲ improve perf. ▲ Increase approach traffic volume — decrease perf.↓ decrease perf.↓ Increase right-turn percentage decrease perf.↓ decrease perf.↓ improve perf. ▲ Increase bicycle volume — — — Increase truck percentage — — — Increase bus stop frequency — — decrease perf.↓ Increase bus dwell time — decrease perf.↓ — Note: 1. “—” factor has no effect on operational performance.

36 The equations identified in the second row of Table 11 were developed to predict the safety performance of the corresponding travel mode. Table 13 indicates the trend in predicted performance associated with each travel mode and equation variable. Specifically, the table identifies the independent variables in the equations and the effect of these variables on transit- or truck-related conflict frequency. For example, right-turn deceleration lane length is shown in the second row of the table. For each of the two travel modes identified, an increase in lane length was found to be associated with a reduction in conflict frequency. Thus, an increase in lane length is associated with improved safety performance. Table 13. Safety model variables – right-turn deceleration. Factor Effect of a Change in Factor on Performance Transit Truck Add right-turn deceleration lane presence decrease perf.↓ improve perf. ▲ Increase right-turn deceleration lane length improve perf. ▲ improve perf. ▲ Increase signal cycle length decrease perf.↓ decrease perf.↓ Allow right-turn-on-red operation — decrease perf.↓ Increase approach traffic volume decrease perf.↓ decrease perf.↓ Increase right-turn percentage improve perf. ▲ decrease perf.↓ Increase bicycle volume decrease perf.↓ decrease perf.↓ Increase truck percentage — decrease perf.↓ Increase bus stop frequency decrease perf.↓ decrease perf.↓ Increase bus dwell time decrease perf.↓ — Note: 1. “—” factor has no effect on safety performance. The first row of Table 13 indicates that the addition of a right-turn deceleration lane will increase transit-related conflict frequency. This trend relates to the increased difficulty that a local bus has returning to the through lanes after pulling into the right-turn lane to pick up passengers. TWLTL vs. Non-Traversable Median The specific travel modes and associated performance relationships of interest to the TWLTL and non- traversable median types are identified in Table 14. As indicated by the grey shaded cells in the table, performance relationships were found in the literature for four of the eight travel mode/performance measure categories. As a result, the focus of this project was on the development of new relationships for the remaining four travel mode/performance measure categories. All of the relationships identified in the table are documented in the Guide for the Analysis of Multimodal Corridor Access Management. The relationships that were developed for this project are also described in Appendix G, using the equation numbers provided in the table.

37 Table 14. Performance relationships to evaluate TWLTL and non-traversable median installations. Performance Measure Category Median Type1 Performance Relationship (PR) by Travel Mode2 Pedestrian Bicycle Transit Truck Operations TWLTL LOS score. Use HCM MMLOS Method. Travel speed. Appendix G, Equation 13. LOS score. Use HCM MMLOS Method. Travel speed. Appendix G, Equation 15. NTM 1. LOS score. Use HCM MMLOS method. 2. Delay. HCM ped. delay method Travel speed. Appendix G, Equation 14. LOS score. Use HCM MMLOS Method . Travel speed. Appendix G, Equation 16. Safety TWLTL Ped. crash rate. Bowman crash rate model. No effect.3 Transit-related crash frequency. Appendix G, Equation 61 to Equation 74. Truck-related crash frequency. Appendix G, Equation 75 to Equation 91. NTM Ped. crash rate. Bowman crash rate model. No effect.3 Notes: 1. TWLTL – two-way left-turn lane median. NTM – non-traversable median. 2. Relationships identified in grey shaded cells refer to predictive methods described in the referenced document. For convenience, these methods are summarized in the Appendix of the Guide for the Analysis of Multimodal Corridor Access Management. Acronyms used in the table are defined as follows: HCM – Highway Capacity Manual (TRB, 2016). MMLOS – multimodal level of service. 3. Research by Alluri et al. (2012) indicates that the conversion from TWLTL to raised-curb median reduced bicycle- related crashes by 4.5 percent; however, the reduction was not statistically significant due to small sample size. The equations identified in the first row of Table 14 were developed to predict the operational performance of the corresponding travel mode. Table 15 indicates the trend in predicted performance associated with each travel mode and equation variable. Specifically, the table identifies the independent variables in the equations and the effect of these variables on bicycle or truck speed. For example, signal cycle length is shown in the second row of the table. For each of the four travel mode/median-type combinations shown, an increase in cycle length was found to be associated with a reduction in speed. Thus, an increase in cycle length is associated with a decrease in operational performance. Table 15. Operations model variables–TWLTL vs. non-traversable median. Factor Effect of a Change in Factor on Performance Bicycle Truck NTM TWLTL NTM TWLTL Increase segment length improve perf. ▲ improve perf. ▲ improve perf.▲ improve perf.▲ Increase signal cycle length decrease perf.↓ decrease perf.↓ decrease perf.↓ decrease perf.↓ Increase access density — — decrease perf.↓ decrease perf.↓ Increase thru. traffic volume — — decrease perf.↓ decrease perf.↓ Increase bicycle volume — — — — Increase truck percentage — — decrease perf.↓ decrease perf.↓ Note: “—” factor has no effect on operational performance.

38 The fourth row of Table 15 indicates that an increase in traffic volume decreases truck performance by reducing truck speed. This trend is logical and consistent with the fundamental speed-flow relationship. It suggests that as traffic volume increases, truck speed decreases. In contrast, an increase in traffic volume is associated with an increase in bicycle speed. This trend likely indirectly reflects the improved signal coordination that is typically provided to the high-volume travel direction. Bicycles are likely to benefit from this improved coordination. The equations that were developed for this project to predict safety performance are summarized in Table 16. The table information identifies the independent variables in the equations and the effect of these variables on transit- or truck-related crash frequency. For example, segment length is shown in the first row of the table. For each of the four travel mode/median-type combinations shown, an increase in segment length was found to be associated with an increase in crash frequency. Thus, an increase in segment length is associated with a decrease in safety performance. Table 16. Safety model variables – TWLTL vs. non-traversable median. Factor Effect of a Change in Factor on Performance Transit Truck NTM TWLTL NTM TWLTL Increase segment length decrease perf.↓ decrease perf.↓ decrease perf.↓ decrease perf.↓ Increase AADT volume decrease perf.↓ decrease perf.↓ decrease perf.↓ decrease perf.↓ Increase AADT transit volume decrease perf.↓ decrease perf.↓ — — Increase AADT truck volume — — decrease perf.↓ decrease perf.↓ Increase lane width improve perf. ▲ improve perf. ▲ improve perf.▲ improve perf.▲ Increase shoulder width improve perf. ▲ improve perf. ▲ improve perf.▲ improve perf.▲ Increase bicycle lane width improve perf. ▲ improve perf. ▲ improve perf.▲ improve perf.▲ Increase median width improve perf. ▲ — improve perf.▲ — Increase access density decrease perf.↓ decrease perf.↓ decrease perf.↓ decrease perf.↓ Note: “—” factor has no effect on safety performance. The trends in Table 16 indicate that an increase in lane, shoulder, bicycle, or median width is associated with a reduction in transit- and truck-related crash frequency. In contrast, an increase in access density is indicated to be associated with an increase in crash frequency. All of these trends are consistent with the effect of these design characteristics on the frequency of crashes in the overall traffic stream. Development of a Guide for the Analysis of Multimodal Corridor Access Management Overview The Guide for the Analysis of Multimodal Corridor Access Management (Guide) developed by this research provides practitioners with a single source of information about the general and specific impacts of a given access management technique on various roadway users. The Guide also documents for researchers the areas where gaps in knowledge remain about the impact of a given technique on specific

39 travel modes. The Guide draws from two main sources: (1) the literature reviewed during the course of the research, and (2) the new and updated performance relationships, described above, developed by the research. The result is a summary of the available knowledge about the interactions of more than 70 access management techniques with the operations and safety of motorized vehicles, pedestrians, bicyclists, buses, and trucks. Where available, the Guide identifies and describes quantitative tools and methods that can be used to evaluate the magnitude of the interaction of a given access management technique on a specific travel mode. In other cases, no quantitative tools exist, but qualitative relationships have been documented in the literature; these are also summarized in the Guide. Finally, where no research has been conducted on the interaction between a specific access management technique and a specific mode; this lack of knowledge is also documented in the Guide and can serve as a starting point for identifying future research needs. Organization of the Guide Access Management Technique Groups Each section of the Guide addresses one of 19 groups of related access management techniques, with each group containing between one and eight techniques. Each section is organized similarly, with information presented in order from the most general (performance summaries) to more detailed (qualitative descriptions of performance trends) to the most detailed (descriptions of available tools for quantifying specific operations and safety interactions by mode). Finally, an appendix provides detailed guidance on applying the most common tools available for quantifying the interactions of access management techniques. The 19 groups of techniques covered in the Guide consist of: 1. Restrict Left-turn Movements at an Access Point 2. Non-traversable Medians 3. Continuous Two-Way Left-Turn Lanes 4. Frontage and Service Roads 5. Unsignalized Median Openings 6. Traffic Signal Spacing 7. Number and Spacing of Unsignalized Access Points 8. Interchange Areas 9. Left-Turn Lanes 10. Right-Turn Lanes 11. Driveway Channelization 12. Alternative Intersections and Interchanges 13. Parking and Stopping Restrictions 14. Roundabouts 15. Driveway Sight Distance 16. One-Way Driveways 17. Driveway Width 18. Driveway Vertical Geometry 19. Driveway Throat Length

40 Description Each Guide section begins with a general description of the characteristics of the techniques included in that group, along with a photo or illustration depicting the main characteristic(s) of the group. Multimodal Operations and Safety Performance Summary This subsection contains two tables, organized by access management technique, travel mode, and the areas of operations and safety. The first table presents general performance trends associated with each technique in the group. Possible trends are: improved performance, decreased performance, mixed performance, unchanged performance, and no relationship documented. The “mixed performance” category is used when either (1) a technique produces both positive and negative interactions with a particular mode, or (2) a technique in some cases has no interaction and in other cases has an interaction. The “unchanged performance” category is used when the interaction has been studied and no change in performance was documented. The second table documents whether the performance trends in the first table are based on quantitative or qualitative relationships, or whether no relationship has been documented. Table 17 shows an example of these tables for the technique of installing a continuous two-way left-turn lane (TWLTL) on a roadway. Table 17. Example performance summary tables from the Guide. Access Management Technique General Performance Trends Associated with Technique Operations Safety Install continuous TWLTL ↑  ↓  ↓  ↕  ↑  ↕  ↓  ↓      Notes:   ↑ = improved performance, ↓ = decreased performance, ↕ = mixed performance, and  = no relationship  documented.  Entries for buses and trucks indicate relationships specific to those modes; motor vehicle relationships also apply.  TWLTL = two‐way left‐turn lane.  Access Management Technique Documented Performance Relationships for Technique Operations Safety Install continuous TWLTL           Notes:    = quantitative relationship,  = qualitative relationship, and  = no relationship documented.  Entries for buses and trucks indicate relationships specific to those modes; motor vehicle relationships also apply.  TWLTL = two‐way left‐turn lane.  The performance trend information presented in the summary tables does not indicate the magnitude of the interaction, either by itself or relative to other techniques. In some cases, the answer depends on other factors. The practitioner should consult the detailed information presented later in each section to make these determinations. Entries for the bus and truck modes reflect interactions specific to those modes. However, buses and trucks are also motor vehicles; therefore, the information presented for motor vehicles also applies to buses and trucks. However, in some cases, the magnitude of a particular interaction may be different for

41 buses and trucks than for motor vehicles in general, or buses or trucks may experience additional interactions that motor vehicles do not. These differences are reflected in the entries in the bus and truck columns of the tables. General Trends Associated with Improvements This subsection describes in words the documented interactions between access management techniques and a given mode’s operations and safety. The source(s) of this information are also listed. Table 18 provides an example of the trend table associated with installing continuous TWLTLs. Table 18. Example trend table from the Guide for installing continuous TWLTLs. Mode Operations Safety   Motor vehicle free-flow and travel speeds increase by up to a few mph, depending on traffic volumes, the number of access points, and the proportion of the roadway with a TWLTL (1, 2). The motor vehicle crash rate decreases, but by a smaller amount compared to installing a non- traversable median (3). Creates the potential for overlapping left-turn movements (4).   Increased pedestrian delay and decreased pedestrian LOS at midblock pedestrian crossings where the crossing distance is increased as a result of the TWLTL. Small negative effect on pedestrian LOS due to increased motor vehicle speeds (2, 5). Increased pedestrian exposure when the crossing distance is increased as a result of the TWLTL; the TWLTL does not provide a pedestrian refuge (4). Similar vehicle-pedestrian crash rates as undivided highways (6). Increases in vehicle speeds may negatively affect pedestrian safety (7, 8).   Negative effect on bicycle LOS due to increased motor vehicle speeds (2, 5). Increases in vehicle speeds may negatively affect bicycle safety (7, 8).   Similar effects as for motor vehicles. May make access to midblock bus stops more difficult, as bus passengers generally need to cross the roadway at some point during a round trip (9, 10). No documented effect beyond that generally observed for motor vehicle traffic (for buses) and pedestrians (for boarding and alighting passengers).   Improved truck LOS due to improved speeds (11). No documented effect beyond that generally observed for motor vehicle traffic. Note:  Italic numbers indicate references in the Guide providing the source of the information.    LOS = level of service, TWLTL = two‐way left‐turn lane.  Quantitative Analysis Methods Where one or more quantitative analysis methods exist for a given combination of (1) mode and (2) operations or safety, this subsection provides information about those methods. For relatively simple methods, such as crash modification factors, the specific relationship is presented in the text, along with a reference to the source document. For more complex methods, such as those found in the Highway Capacity Manual, this subsection provides a reference to the source document and provides guidance on the potential magnitude of the relationship. The Guide’s appendix provides additional guidance on the quantitative methods that appear in multiple sections. Additional Information This subsection provides cross-references to additional sources of information about this group of access management techniques. These sources include:

42  Other Guide sections;  Specific chapters and sections within the Access Management Manual, 2nd Edition;  Specific chapters and sections within the Access Management Application Guidelines; and  NCHRP reports and other authoritative documents. Appendix Overview The appendix provides guidance on applying the quantitative analysis methods that appear most often in the Guide. These methods can be used to estimate performance measures that describe how access management techniques interact with the operations or safety performance of particular travel modes. The interactions of access management techniques with travel modes can be described in two ways. One way is by presenting absolute values of a performance measure (e.g., average motor vehicle speeds would increase from 26.7 to 28.2 mph following installation of a non-traversable median). Another way is by presenting the relative change in a performance measure (e.g., average motor vehicle speeds would increase by 1.5 mph following installation of a non-traversable median). Calculating relative changes is often easier than calculating absolute values, and the appendix focuses on demonstrating calculations that can be performed with nothing more than a scientific calculator. However, the appendix also provides guidance on where to turn for more information on performing complex calculations that require developing spreadsheets or applying specialized software. Methods Described in this Appendix The methods described in the appendix are identified in the following list.  Highway Capacity Manual (HCM) intersection delay methods. These methods estimate the average motor vehicle control delay for a turning movement, approach, and/or intersection as a whole for signalized intersections, unsignalized (i.e., minor street stop-controlled) intersections, roundabouts, interchange ramp terminals, and alternative intersection and interchange forms. They can be used to evaluate the effects of access management techniques that alter turning movement patterns, traffic volumes, or both (TRB, 2016).  HCM arterial speed estimation methods. These methods estimate the free-flow and average midblock running speeds of motor vehicle traffic along roadway links between signalized intersections or roundabouts. They also estimate the average travel speed of motor vehicles along longer sections of roadway, including the delays that occur at intersections. These methods can be used to evaluate the effects of access management techniques that alter the roadway geometry, number of access points, and/or on-street parking provisions (TRB, 2016).  HCM queue estimation methods. These methods provide the 95th-percentile back of queue and, sometimes, other percentile queues. They can be used in determining the size of an intersection’s influence area and in sizing turn lane lengths and driveway throat lengths (TRB, 2016).  HCM multimodal level of service (MMLOS) methods. These methods calculate pedestrian, bicycle, and transit level of service scores that estimate traveler satisfaction with quality of travel by these modes along a roadway section. In many cases, the effect of an access management technique on other modes is indirect, resulting from changes in average midblock motor vehicle speeds caused by the technique. However, in some cases, a particular access management technique may directly affect a non-auto mode’s level of service score (for example, the reduction or elimination of on-street parking) (TRB, 2016).  HCM pedestrian and bicycle delay methods. These methods calculate average pedestrian and bicycle delay at signalized intersections and roundabouts, along with average pedestrian delay crossing roadways at unsignalized locations. They can be used to evaluate the effects of access

43 management techniques that change traffic volumes, street widths, add pedestrian refuges, or a combination of these (TRB, 2016).  Truck level of service. This method evaluates the effect of changes in average truck speeds on overall truck level of service (Dowling et al., 2014).  Crash modification factors (CMFs). These factors estimate the change in crash rate that would occur as a result of implementing a particular access management technique. CMFs are straightforward to apply; the appendix provides guidance on selecting appropriate CMFs that may be developed following the publication of the Guide.  Vehicle crash models. These models (Bowman and Vecellio, 1994; Bowman et al., 1994; Potts et al., 2006; Potts et al., 2011; Carter et al., 2006) estimate the crash rate or total number of crashes that would occur given a particular set of conditions. They are applicable to a small number of access management techniques that affect factors included in one of these models. Individual Guide sections often present additional quantitative techniques that are not discussed in the appendix. These quantitative techniques are considered straightforward to apply without additional explanation. Table 19 lists the analysis methods described in the Guide’s appendix. For each group of access management techniques described in the Guide, the table indicates whether an analysis method (a) can be used to calculate relative changes in operations or safety performance without calculating absolute performance values first, (b) can only calculate relative changes when an absolute value is calculated first, or (c) is not applicable to any technique in the group. Note that a given method will typically be applicable to only one or a few of the multiple access management techniques included in a given group. Table 19. Quantitative method applicability by access management technique group. Access Management Technique Group HCM Vehicle Delay HCM Arterial Speed HCM Queues HCM MMLOS HCM Ped & Bike Delay Truck LOS CMFs Bowman Crash Rate Models Potts Ped Crash Model Carter Inter- section Safety Restrict left-turn movements                     Non-traversable medians                     Two-way left-turn lanes                     Frontage/service roads                     Unsignalized median openings                     Traffic signal spacing                     Number and spacing of access points                     Interchange areas                     Left-turn lanes                     Right-turn lanes                     Driveway channelization                     Alternative intersections and interchanges                     Parking and stopping restrictions                     Roundabouts                     Driveway sight distance                     One-way driveways                     Driveway width                    

44 Driveway vertical geometry                     Driveway throat length                       Notes:  HCM = Highway Capacity Manual, LOS = level of service, MMLOS = multimodal LOS, CMF = crash modification factor.     = Relative change in performance can be estimated without calculating absolute performance values. Absolute          values can also be calculated.     = Relative change in performance can be estimated by calculating absolute values first.     = Method not applicable to this group of access management techniques.  References Alluri, P., A. Gan, K. Haleem, S. Miranda, E. Echezabal, A. Diaz, and S. Ding. (2012). Before-and-After Study of Roadways Where New Medians have been Added. Florida Department of Transportation. Tallahassee, Florida. Bowman, B.L., and R.L. Vecellio. (1994). “Effect of Urban and Suburban Median Types on Both Vehicular and Pedestrian Safety.” Transportation Research Record. 1445. TRB, National Research Council, Washington, D.C. Bowman, B., R. Vecellio, and J. Miao. (1994). “Vehicle and Pedestrian Accident Models for Median Locations.” Journal of Transportation Engineering, Vol. 121, No. 6, American Society of Civil Engineers, Washington, D.C., November- December. Carter, D., W. Hunter, C. Zegeer, J. Stewart, and H. Huang. (2006). Pedestrian and Bicyclist Intersection Safety Indices: Final Report. Report No. FHWA-HRT-06-125. Federal Highway Administration, Washington, D.C. Dowling, R., G. List, B. Yang, E. Witzke, and A. Flannery. (2014). NCFRP Report 31: Incorporating Truck Analysis into the Highway Capacity Manual. Transportation Research Board of the National Academies, Washington, D.C. Highway Capacity Manual: A Guide for Multimodal Mobility Analysis, 6th ed. (2016). Transportation Research Board, Washington, D.C. Potts, I., D. Harwood, D. Torbic, S. Hennum, C. Tiesler, J. Zegeer, J. Ringert, D. Harkey, and J. Barlow. (2006). Synthesis on Channelized Right Turns on Urban and Suburban Arterials. Midwest Research Institute, Kansas City, Missouri. Potts, I., D. Harwood, K. Bauer, D. Gilmore, J. Hutton, D. Torbic, J. Ringert, A. Daleiden, and J. Barlow. (2011). NCHRP Web-Only Document 208: Design Guidance for Channelized Right-Turn Lanes. Transportation Research Board of the National Academies, Washington, D.C.

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TRB’s National Cooperative Highway Research Program (NCHRP) Web-Only Document 256: Assessing Interactions Between Access Management Treatments and Multimodal Users describes operational and safety relationships between access management techniques and the automobile, pedestrian, bicycle, public transit, and truck modes. This contractor's report may help assist in the selection of alternative access management techniques based on the safety and operation performance of each affected travel mode.The roadway system must accommodate many types of users—bicyclists, passenger cars, pedestrians, transit, and trucks. This report examines the interactions between multimodal operations and access management techniques and treatments, and the trade-off decisions that are necessary.

NCHRP Research Report 900: Guide for the Analysis of Multimodal Corridor Access Management accompanies this report.

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