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Safety Data and Analysis in Developing Emphasis Area Plans (2008)

Chapter: Section VI - Special Road User Populations

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Suggested Citation:"Section VI - Special Road User Populations." National Academies of Sciences, Engineering, and Medicine. 2008. Safety Data and Analysis in Developing Emphasis Area Plans. Washington, DC: The National Academies Press. doi: 10.17226/14170.
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Suggested Citation:"Section VI - Special Road User Populations." National Academies of Sciences, Engineering, and Medicine. 2008. Safety Data and Analysis in Developing Emphasis Area Plans. Washington, DC: The National Academies Press. doi: 10.17226/14170.
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Suggested Citation:"Section VI - Special Road User Populations." National Academies of Sciences, Engineering, and Medicine. 2008. Safety Data and Analysis in Developing Emphasis Area Plans. Washington, DC: The National Academies Press. doi: 10.17226/14170.
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Page 56
Page 57
Suggested Citation:"Section VI - Special Road User Populations." National Academies of Sciences, Engineering, and Medicine. 2008. Safety Data and Analysis in Developing Emphasis Area Plans. Washington, DC: The National Academies Press. doi: 10.17226/14170.
×
Page 57
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Suggested Citation:"Section VI - Special Road User Populations." National Academies of Sciences, Engineering, and Medicine. 2008. Safety Data and Analysis in Developing Emphasis Area Plans. Washington, DC: The National Academies Press. doi: 10.17226/14170.
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54 Planning Programs Related To Reducing Crashes Involving Older Drivers, Younger Drivers, Pedestrians And Bicyclists This section of the guide provides the details of choosing treatment strategies for older drivers, younger drivers, pedes- trians or bicyclists, and targeting those treatments to sub- groups of these populations or to locations where their crashes occur. As indicated earlier, it is assumed at this point that the analyst has chosen his/her other emphasis area or areas (e.g., older drivers and/or pedestrians) and has established a stretch goal. In implementing driver-oriented programs, the estima- tion of program costs is often challenging. In addition to the direct cost of the program, one-time start-up costs and indirect/administrative costs may be substantial, but are not always addressed in the cost estimation process. Planning of pedestrian and bicycle safety programs is often challenging because of limited crash data. In planning pedestrian safety improvements, opportunities to improve accessibility under the requirements of the Americans with Disabilities Act (ADA) should be addressed. Four procedures for choosing and targeting treatment strategies were described in the Stage 3 text in Section III. Three of those procedures require that the effectiveness (CRF or AMF) of at least part of the potential treatment strategies be known. However, almost none of the strategies in the guides related to these special road-user populations have known effectiveness. For that reason, only the details of Procedure 3 will be covered in this section. If AMFs are de- veloped for treatments for these populations, or if the analyst is only interested in examining the few treatments with known AMFs, then the economic-based Procedures 1 or 2 can be used. If AMFs exist for some of the treatments of potential interest but not for all (which will likely be the case in the near future), Procedure 4 can be used. While the crash types will differ, details of the use of all three of these “known-effectiveness procedures” are provided in Section IV on “Roadway Segment Programs.” Thus, the basic steps in Procedure 3 presented below will be appropriate for all four of the road user populations covered in this section. The data (e.g., variable values used to define older driver crashes and crash types for older drivers vs. pedestrians) will differ, but the basic procedure will remain the same. The analyst is strongly urged to carefully review the material in each of the pertinent guides before beginning this planning process. These user-population-oriented guides are found within NCHRP Report 500: Guidance for Implementation of the AASHTO Strategic Highway Safety Plan. The specific volumes pertinent to this section are: • Volume 9: A Guide for Reducing Collisions Involving Older Drivers (9) • Volume 10: A Guide for Reducing Collisions Involving Pedes- trians (10) • Volume 18: A Guide for Reducing Collisions Involving Bi- cycles (2008) • Volume 19: A Guide for Reducing Collisions Involving Young Drivers (2007) A link to these downloadable guides can be found at http:// safety.transportation.org/guides.aspx. Procedure 3 – Choosing Roadway User Treatments and Target Subgroups When Treatment Effectiveness in Terms of Crash/Injury Reduction Is Not Known Again, the assumption here is that there is no known level of effectiveness for the treatment strategies of interest – no de- fined CRFs or AMFs. Thus, economic analyses like those that are the basis for Procedures 1, 2A and 2B, and 4 are not possi- ble for these treatments. This Procedure 3 is aimed at helping S E C T I O N V I Special Road User Populations

the analyst make an educated choice of which treatments will be most effective in their jurisdiction, and to help the analyst develop a targeting strategy for the treatment in cases where it is not to be applied jurisdiction-wide (e.g., where specific user subpopulations or roadway locations are to be targeted). In general, within each user group, the choice between alterna- tive treatments will be based on the specific nature of the pop- ulation’s crash problem, and the choice of target subgroups will be based on the determination of where the crash/injury problem of interest is found. A discussion of this more general procedure was included above, and the reader should review that section. Data Needs The only required data for Procedure 3 are crash data that will allow the analyst to (1) isolate crashes involving the spe- cific user population of interest (e.g., older drivers) and (2) define crash types for this user population which would suggest strategies and target subgroups. DMV records, and particularly DMV driver history files, may also be useful in planning driver-oriented programs. To isolate crashes involving the population of interest, the analyst will need to examine the data formats/coding in his/her crash file to identify variables that can be used in determining whether or not a given crash is a “target-population crash.” Crash databases often categorize data for a given crash into up to three subfiles – (1) general accident/crash variables (“crash”), (2) variables for each vehicle in the crash (“vehicle”), and (3) variables for each occupant/person in the crash (“person” or “occupant”). The variables needed to determine whether a crash is a “target-population crash” are usually found in the oc- cupant/person subfile, but could also be found in the general crash subfile (e.g., a “flag” for all pedestrian crashes) or “vehicle” subfile (e.g., driver information included with each vehicle record). Pedestrians or bicyclists are sometimes classed as a “vehicle type” in the vehicle file, and sometimes as a “person type” in the occupant/person file. (If the jurisdiction’s data are compliant with the Model Minimum Uniform Crash Criteria – MMUCC – these data variables will be in the “Person” subfile.) In short, crash files differ from jurisdiction to jurisdiction. While certainly not always the case, the variables (or similar variables) listed in Exhibit VI-1 will be used in this identifica- tion of target-population crashes. Defining crashes that will guide the choice of treatment strat- egy and the targeting of these strategies will require crash data that include specific variables and codes on such items as location of crash (intersection vs. non-intersection), condi- tion of driver or pedestrian, driver/bicyclist/pedestrian action prior to crash, light condition, etc. Again, the names of variables and the specific codes needed to conduct these analy- ses will vary from jurisdiction to jurisdiction. While not all crash types for all treatment strategies related to all four populations are included here, Exhibit VI-2 provides some guidance concerning where example variables related to some treatment strategies might be found. Note that in MMUCC- compliant databases, the term “non-motorist” will be used for both pedestrians and bicyclists. Procedure As described in Section III, Procedure 3 has two basic steps. First, choose the best treatments for the user population of interest (e.g., the older-driver treatments most likely to be applicable in a given jurisdiction) from among the set of all treatments presented in the applicable NCHRP Report 500 guides. Second, choose the subgroups of users or highway locations to which the selected treatments should be applied. As described earlier in more detail, the choice of the “best treat- ments” from the listing of many potential user-population treatments can be based on the following factors: a) The potential treatment judged to be the most effective, even given that effectiveness is unknown b) The relative magnitude of the crash types and severity levels that the treatment will affect c) The cost of the potential treatments (either jurisdiction- wide, per-mile or per-location) d) Other technical or policy considerations These factors must be combined in some fashion to decide which treatment to choose. While there are multiple ways of 55 Population Type Variable Crash Database Subfile Older Drivers or Younger Drivers Person Type Person/Occupant Driver Age Person/Occupant or Vehicle Driver Date of Birth Person/Occupant or Vehicle Pedestrian or Bicyclist Person Type Person/Occupant Vehicle Type Vehicle Crash Type (First Harmful Event) Crash Exhibit VI-1. Crash variables and subfile location by population type.

making this choice, the following represents one such pro- cedure. 1. Prioritize the specific user-population problem(s) to be addressed. This is related to Factor b in the above list. Here, the initial issue is whether to treat older driver, younger driver, pedestrian or bicyclist crashes. This prioritization will be based on the frequency and severity of the specific types of user-population crashes occurring in an analyst’s jurisdiction. Crashes specific to a given user population were defined in the table above. For each user population, the analyst could begin the process by analyzing 3 to 5 years of crash data to determine the frequency of each population. However, since some crashes for some pop- ulations are more severe than others, total crash frequency alone does not provide the complete answer. While an alternative is to restrict the analysis to only fatal and serious-injury crashes, this will severely limit the crash sample, and will also omit a large component of the crash problem – non-serious injury and no-injury crashes. A better solution is to weight each crash for a given user population by an economic cost based on its severity, and then accumulate the total crash cost for each population. Information on economic cost per crash severity level can be found in Crash Cost Estimates by Maximum Police-Reported Injury Severity Within Selected Crash Geometries (22). Here, instead of using severity cost by crash type as is done in roadway-program analyses covered in earlier sections, the analyst will use the basic crash cost by police-reported severity level (i.e., K,A,B,C,O). Exhibit VI-3 below presents those costs per crash. Costs for combinations of crash severity levels (e.g., K+A crashes) are presented in that report (22). This 56 Crash Type/Issue Variable Crash Database Subfile Intersection vs. Non-intersection Relation to Junction Type of Intersection Traffic Control Device Type Crash Crash Crash or Vehicle Nighttime/Reduced Visibility Light Condition Weather Roadway Lighting Crash Crash Crash Lane Departure (Potentially Related to Pavement Markings) Accident/Crash Type Manner of Collision Sequence of Events First Harmful Event Most Harmful Event Crash Location (Off-road) Crash Crash Vehicle Crash Vehicle Crash Crashes Associated with Medical Conditions Driver Condition Person Occupant Restraint Use Occupant Protection System Use Person Work Zone Work Zone Related Roadway Condition Crash Crash Pedestrian “Walking along Roadway” Crashes Pedestrian (or Non-Motorist) Action Prior to Crash Person Speed-related Crashes Driver Action Prior to Crash Violation Indicated Contributing Circumstances Person (or Vehicle) Person (or Vehicle) Person (or Vehicle) Crash Location (for Targeting Treatments) County City Route Milepost Longitude/Latitude Block Address Crash Crash Crash Crash Crash Crash Speed Limit (for Use in Developing Cost per Crash) Speed Limit Crash Exhibit VI-2. Crash variables and subfile location by crash type/issue. Crash Severity Speed Limit Category Comprehensive Cost/Crash* < 45 mph $3,622,200 Fatal (K) > 50 mph $4,107,600 < 45 mph $195,700 Serious Injury (A) > 50 mph $222,300 < 45 mph $62,200 Moderate injury (B) > 50 mph $91,600 < 45 mph $40,100 Minor Injury (C) > 50 mph $49,500 < 45 mph $7,000 No Injury (O) > 50 mph $7,800 *Crash cost in 2001 dollars Exhibit VI-3. Crash cost by crash severity and posted speed limit (22).

analysis of total crash cost will provide the analyst with overall information on which user population is most im- portant in his/her jurisdiction. For the chosen user population, the analyst could then conduct additional analyses of “critical crash types” for that population by producing crash-type distributions and weighting each crash type by the cost per crash. This could be done either by using the costs for the 22 crash types presented in the above report, or by developing severity distributions within each crash type and weighting the individual severity-level frequencies by the cost estimates above. This analysis will then produce a listing of potentially treatable crash types for the chosen user population that can be sorted by crash frequency or total crash cost, thus pro- viding a ranked listing. For the higher-ranked crash types, the analyst can then conduct additional analyses to deter- mine more of the specifics of the crash circumstances (e.g., nighttime vs. daytime distributions of total crash cost). These additional “drill-down” analyses should be designed to provide additional information that could lead to the choice of one treatment over another (e.g., intersection lighting will affect nighttime older-driver crashes at inter- sections, and traffic calming measures on road sections are more likely to affect locations with higher speeds, as defined by either speed limit or speeding as a contributing factor). 2. Identify possible treatments for use for each high-priority crash type. The analyst will then review the pertinent NCHRP Re- port 500 guides and list treatments that would be most ap- propriate for each of the high-priority crash types identi- fied in the above step. The choice should be limited to those treatment strategies that are classified as proven or tried in the guides. If not already conducted in the “drill- down” analysis in the preceding step, more specific infor- mation on the total crash cost related to each potential treatment strategy could be developed by specifying the crash types that are most likely to be affected by each strategy (e.g., pedestrian-crossing crashes at higher-speed intersections as targets for intersection traffic calming treatments), producing crash frequencies for each speci- fied crash type, and multiplying the frequencies by cost per crash. For some strategies, the NCHRP Report 500 se- ries presents information concerning which crash types are most likely affected by that treatment strategy. How- ever, for other user-population strategies, it will not be possible to define one or more specific crash types for a given potential strategy (e.g., education programs for drivers and pedestrians, resource centers to promote safe mobility choices for older drivers). In these cases, the an- alyst will have to make some judgment concerning the relative size of the crash problem that could potentially be affected by these strategies. 3. Rate the possible treatments based on estimated effec- tiveness. Since this procedure deals with treatment strategies with unknown effectiveness, this appears to be impossible. However, for a given set of possible treatments for a par- ticular user group, it may be possible to make a judgment concerning which treatment strategy would be expected to be most effective. For example, strategies related to chang- ing the roadway may be more effective, in general, than strategies related to education (but, of course, will affect only those users at the treated locations). At times, this will clearly be a very difficult judgment to make. 4. Choose “best” treatment(s) by considering estimated effectiveness, cost, and other technical and policy con- siderations. The analyst will then combine the output of the steps above with at least two other factors in making a final decision on which treatment(s) to implement – the cost of the treatment and other technical and policy consid- erations. Unfortunately, there are no good guidelines for how to “weight” the different factors. While problem size (total crash cost) and assumed treatment effective- ness are key factors, there may be technical, policy, and cost considerations that will remove certain treatments from consideration even if they are felt to be effective. The analyst will have to choose the final treatments based on best judgment. The procedure outlined above will at least ensure that the major factors in the decision are clearly defined. The output of this step will be one or more chosen treatments, with the nature of the treat- ment defining the specific crash types more likely to be affected. 5. Target the chosen treatments to the user populations where the problem is found. In some cases, treatment strategies related to user pop- ulations will be implemented jurisdiction-wide. In other cases, it may be desirable to target the treatment to either a subgroup of the user population or to specific locations (e.g., specific counties, route sections, or intersections). If a given strategy can be linked to a specific crash type or types, choosing high-priority subgroups for targeting can be done using similar procedures noted above for choos- ing treatments. Here, the user-population crashes within each crash type would be divided among all potential user subgroups (e.g., pedestrian crashes would be divided into age groups), and crash frequency or total crash cost would be calculated for each subgroup, producing a ranking based on problem size. If treatment-cost estimates can be made for each subgroup, the total crash cost and treat- ment cost can be combined to provide an indication of which subgroup might produce the largest payoff per treatment dollar spent. 57

If targeting is to be done by location, the treatment could be targeted to counties, city areas, or routes/streets showing the highest total crash cost or frequency, coupled with the analyst’s judgment of potential differences in cost between locations and technical and political issues. If the crash data are mileposted, the analyst could (1) link crashes to routes and search for the locations of “clusters” of target crashes for possible treatment or (2) use a net- work screening program similar to that described under Procedure 2A to identify 1-mile sections with the highest crash frequency or total crash cost. The windows identi- fied by the network screening program could then be ranked by crash frequency or total crash cost to identify priority locations. The analyst would then correct for “treatment gaps” using the same logic provided in Proce- dure 2A (see Section IV). If the crashes are not mileposted, but there is information available on jurisdiction and route, the analyst could link crashes to routes within the jurisdiction and calculate the total crash cost or number of target crashes per mile by dividing the sum of the crash cost or the sum of target crashes on that route by route length. The analyst could then rank the potential routes for treatment based on this rate per mile, and choose the routes to be treated based on the highest rankings plus other technical and policy factors. 6. Decide what to do with multiple treatments for the same subgroup or on the same segments/routes. The above steps could possibly produce subgroups, ge- ographic areas, roadway locations or routes within a ju- risdiction that could be treated with multiple treatments. If the potential treatment strategies still under considera- tion are characterized by different target crash types (e.g., left-turn intersection crashes vs. angle intersection crashes for older drivers), and if the crash data are mile- posted or include route information, the analyst could use the outputs of Step 5 above in making the treatment choice. Step 5 would produce the total crash cost or crash frequency of each potential target subgroup or location. For each subgroup or location where multiple treatments are possible, the analyst could compare the crash frequency or total crash cost for each of the different possible strategies. Total crash cost would be a much su- perior criterion if the target crash types being compared differ with respect to crash severity (e.g., turning crashes vs. head-on crashes). If total crash cost or frequency for one treatment strategy clearly exceeds total crash cost or frequency for the other, the first would be a logical treat- ment choice. If the total crash cost or frequency for the different strategies is essentially the same, the analyst will need to make the decision based on best judgment, such as applying the same treatment used with other user pop- ulations. 7. Add new treatments, new targets or new approaches (e.g., inclusion of improved signing and marking in normal maintenance efforts) until the available funding is used. Without effectiveness measures for the treatments, it is not possible to verify whether or not a specific set of treat- ment types and treatments will meet the established goal. Therefore, the best that can be done is to proceed in select- ing treatment types and treatments until the available budget for safety improvement has been fully committed. The total benefit of the selected program will not be fore- castable, but the success of the program can be determined by evaluations conducted after its implementation. Closure – Good Data Produce Better Results The assumption in this section has been that crash data are available, but not necessarily other data such as roadway in- ventories. As is obvious in the procedures above, the availabil- ity of mileposted crash data will result in improved treatment targeting, and the availability of linkable (and thus mileposted) inventory data would further increase the analyst’s ability to both choose treatment strategies and to target them. For ex- ample, inventory data could provide detailed data not found in crash data files on such items as signal timing, intersection layout, and street width, all of which are related to treatment strategies listed in the guides. In like fashion, more detailed data on crash types would greatly increase the analyst’s ability to choose treatments, par- ticularly for pedestrian and bicycle crashes. Such enhanced data can be developed by a state or local jurisdiction using a tool known as Pedestrian and Bicycle Crash Analysis Tool (PBCAT). For more information on this tool, go to http:// www.walkinginfo.org/pc/pbcat.htm. Finally, many of the special user-population strategies cov- ered in this section will be applied in local jurisdictions as well as at the state level. Many local jurisdictions have or are consider- ing officially or unofficially increasing the threshold for crash reporting which means they will be reporting fewer non-injury crashes. It should be noted that such a policy will likely greatly reduce the crash sample available for analysis in local jurisdic- tions, particularly for older and younger driver programs, since many of the crashes for these two groups will be non-injury crashes. While pedestrian and bicycle crashes may be less affected, their numbers are usually so small in a local jurisdic- tion that any decrease is problematic. Safety analysts are urged to consider such proposed threshold changes carefully and to bring the expected negative effects to the attention of decision makers. 58

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TRB's National Cooperative Highway Research Program (NCHRP) Report 500, Vol. 21: Guidance for Implementation of the AASHTO Strategic Highway Safety Plan: Safety Data and Analysis in Developing Emphasis Area Plans provides guidance on data sources and analysis techniques that may be employed to assist agencies in allocating safety funds.

In 1998, the American Association of State Highway and Transportation Officials (AASHTO) approved its Strategic Highway Safety Plan, which was developed by the AASHTO Standing Committee for Highway Traffic Safety with the assistance of the Federal Highway Administration, the National Highway Traffic Safety Administration, and the Transportation Research Board Committee on Transportation Safety Management. The plan includes strategies in 22 key emphasis areas that affect highway safety. The plan's goal is to reduce the annual number of highway deaths by 5,000 to 7,000. Each of the 22 emphasis areas includes strategies and an outline of what is needed to implement each strategy.

Over the next few years the National Cooperative Highway Research Program (NCHRP) will be developing a series of guides, several of which are already available, to assist state and local agencies in reducing injuries and fatalities in targeted areas. The guides correspond to the emphasis areas outlined in the AASHTO Strategic Highway Safety Plan. Each guide includes a brief introduction, a general description of the problem, the strategies/countermeasures to address the problem, and a model implementation process.

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