A Data-Driven Process for Prioritizing Highway Safety Investments in Idaho

Transcription

1 0 0 A Data-Driven Process for Prioritizing Highway Safety Investments in Idaho Submission Date: November, 0 Number of Words in Text: Number of Figures, Tables & Photographs: Total Word Count: William R. Loudon (Corresponding Author) DKS Associates 0 Broadway Suite 0 Oakland, CA Ph: -- Fax: -- wrl@dksassociates.com Robert J. Schulte DKS Associates 0 SW Washington, Suite 00 Portland, OR 0 Ph: 0-- Fax: 0-- rjs@dksassociates.com Joshua Pilachowski DKS Associates 0 Broadway Suite 0 Oakland, CA Ph: --0 Fax: -- josh@dksassociates.com Brent Jennings Idaho Transportation Department PO Box Boise, Idaho 0- Ph: (0) - Fax (0) -0 brent.jennings@itd.idaho.gov

2 Loudon, Schulte, Pilachowski, Jennings ABSTRACT The Idaho Transportation Department (ITD) developed a data-driven method for identifying the highest priority locations for safety improvements and for evaluating the potential safety benefits of candidate projects. DKS developed criteria defining the Safety Health Score for each segment of the state highway system and used the score to identify the highest priority locations for safety analysis in each ITD district. Visualization of the highest priority locations was aided by Geographic Information System (GIS) mapping of the Safety Health Score. Five years of crash data for the high-priority locations was used to identify potential improvement projects. The Human Factors Guidelines were used to help identify crash causality and appropriate countermeasures for consideration. The Highway Safety Manual procedures provided the basic methodology for estimating the potential reduction in the number and severity of crashes from each of the potential improvements. In the application of the corridor safety analysis, a new approach was used to develop the data needed for the Highway Safety Manual procedures. The project team used the digital video logs that are collected for all state highway mileage each year to prepare estimates of values for numerous roadway characteristics including lane widths, shoulder widths, and locations of driveways and other private access points. The team developed a method for prioritizing safety projects based on an assessment of potential crash cost savings and the benefit-cost ratio of each potential project.

3 Loudon, Schulte, Pilachowski, Jennings OVERVIEW Improving the safety of the state s transportation system has been identified as one of the top priorities of the Idaho Transportation Department (ITD) (). In an effort to improve its costeffectiveness in funding safety projects, ITD undertook an innovative, data-driven program for prioritizing safety investment on roadways throughout the state. The technical work on the program was led by DKS Associates and builds upon work done previously in a pilot program examining rural roadways in District of ITD. The program demonstrated the usefulness of a new method for identifying the highest-priority locations for safety improvements and for evaluating and prioritizing potential improvements. The recently developed Highway Safety Manual (HSM) () provided the basic methodology for estimating the potential annual reductions in the number and severity of crashes for segments and intersections on the state highway system in Idaho where higher-than-average crash rates have been reported. Factors that are accounted for in the estimation of crash-reduction potential include physical characteristics of the roadway, speed controls and enforcement practices, and the traffic control used at intersections. The Highway Safety Corridor Analysis (HSCA) project was implemented as one part of a new initiative of ITD called the Investment Corridor Analysis Planning System (ICAPS). ICAPS is a single transportation investment analysis framework designed to identify potential new investments that can create a positive economic environment in the state. ICAPS is designed to enhance accountability, transparency, and system performance; be outcome based; and provide data for cost-effective decision making. The HSCA project was the first statewide project in which a data-driven process was used to identify statewide priorities for needs and investments in one of ITD s funding areas. The state highway system is spread across six maintenance districts and is organized by the ICAPS categories of importance: Interstate, Statewide, Regional, and District. The HSCA project followed a multi-step process to identify the highest priority locations in need of safety improvements across all districts and ICAPS categories. The process began with a high level identification of high priority segments. This step involved the assessment of five years of crash records and traffic volumes across maintenance districts and ICAPS roadway categories to narrow down over,000 center-line miles statewide to roughly,0 miles that were worse than average from a safety perspective. The second step was to identify the specific locations within the priority segments where the density and severity of crashes were the greatest. This process resulted in the identification of priority locations where more in-depth analysis was undertaken. These priority locations were distributed through all six districts and across all ICAPS categories. The final list of priority locations included less than twenty-five miles of state highway. For each of these locations, the detailed crash records were examined for information about the causes of the crashes. This information along with a more detailed examination of the roadway characteristics led to the development of diagnoses of the causes of the predominant crashes. These diagnoses led to the identification of countermeasures that could potentially reduce the crash frequency or severity. The HSM procedures were also used to identify potential countermeasures and to estimate the annual crash reduction and monetary savings from the reductions. Annualized costs for each countermeasure were estimated based on recent construction history and the experiences of the District staff. With annualized savings and costs, a benefit-cost (B/C) ratio was estimated for each potential countermeasure. The B-C ratio was then used to identify which types of safety improvements and which specific locations appeared to be most cost effective. This information was used to prioritize safety projects for funding.

4 Loudon, Schulte, Pilachowski, Jennings Prior Work on Statewide Need and Funding Prioritization The work in the HSCA project benefited from a review of work by numerous other state departments of transportation on need and funding prioritization and the use of GIS visualization methods to support the process. At the Washington State Department of Transportation (WSDOT), the prioritization of their highway projects is based on a mathematical ranking algorithm that uses as inputs cost-efficiency, public support, environmental affects, mode integration, and compatibility to land-use plans. They found that adding factors other than just cost-efficiency to the algorithm has improved their ranking of the projects (). WSDOT has also developed a model for prioritizing with respect to the State Transportation Policy Plan. This prioritization model is unique because it can accommodate future changes in state policy (). The Georgia Department of Transportation (GDOT) has a Multi-modal Transportation Planning Tool (MTPT) that displays its results using GIS technology. The tool provides planning guidance in rural areas, detects implementation problems in the planning phase, and prioritizes projects (). The New Jersey Department of Transportation also uses GIS to display results from their bicycle and pedestrian prioritized project list (). The Florida Department of Transportation (FDOT) has developed a decision-based GIS computer model to choose the most appropriate projects to fund with respect to preserving wildlife and their natural routes (). In Wisconsin, GIS was used to rank watersheds to prioritize the areas that could most improve water quality (). In Maryland, GIS was used to prioritize needs for the maintenance of the state s stormwater best management practices facilities (). IDENTIFICATION OF HIGH PRIORITY SAFETY LOCATIONS The determination of high-priority segments consisted of a two-step process. The first step was to use a safety score to identify segments with a worse-than-average five-year crash history compared to other state highway segments within the same ICAPS category. A relative comparison was used rather than a comparison to an absolute standard because the goal of the process was to rank potential safety improvement locations relative to all other locations in the state. For each segment, five years of crash records were aggregated by crash severity and year. The levels of crash severity are as follows: Fatal Serious Injury (Injury A) Visible Injury (Injury B) Possible Injury (Injury C) Property Damage Only (PDO) The crash severity of a crash event is determined by the worst injury that occurred. Using a fiveyear average annual daily traffic (AADT), crash rates per ten million vehicle miles travelled were calculated for fatal crashes, injury and fatal crashes, and total crashes. Each segment was then compared to segments within the same ICAPS category to determine the percent of statewide vehicle miles travelled (VMT) with a lower fatal, injury, and total crash rate. The Safety Health Score was originally developed as part of the system for defining and visualizing corridor health in ITD District (), and was confirmed for use in this project by the Technical Advisory Committee. It is calculated using the following formula: Safety Health Score= 0.*(% of State VMT with Fatal and Serious Injury Crash Rate greater than or equal to the Segment)

5 Loudon, Schulte, Pilachowski, Jennings + 0.*(% of State VMT with Injury Crash Rate greater than or equal to the Segment) + 0.0*(% of State VMT with Total Crash Rate greater than or equal to the Segment) () The score combines the three crash rate types fatality, injury and total and gives higher priority to those crashes involving a fatality or injury. The overall rating is based on weights of % for the fatality and serious injury rate, % for the injury rate and 0% for the total crash rate. An overall rating of 0% or above is considered better-than-average, a rating of 0% to 0% is considered average, and a rating of less than 0% is considered worse-than-average. All segments with worse-than-average score were considered high-priority segments. This step resulted in a reduction from the statewide total of,000 miles of roadway to,0 miles of high-priority segments. Visualization of the highest priority locations was aided by GIS mapping of the Safety Health Score. A map of the Safety Health Score for the entire state is provided in Figure.

6 Loudon, Schulte, Pilachowski, Jennings FIGURE Existing Safety Health Score of State Routes inn Idaho The second step was to identify the specificc locations within the high-priority segments where the density and severity of crashes had been the highest. To do this, the high priority segments were reexamined by one-tenth mile segments. The following formula shows the calculation for the Needs Score, which is similar to the Safety Health Score (Equation ) that was already derived. The focus of this level of analysis was to determine locations with high crash rates. Unlike the previous step, a distinction between crashes with fatalities and crashes with injuries was avoided becausee of the highly stochasticc nature of fatal crashes. Needs Score= =0.0*(Injury Crash Rate/Average Injury Crash Rate for ICAPS category) + 0.0*(Total Crash Rate/Average Total Crash Rate for ICAPS category) ()

7 Loudon, Schulte, Pilachowski, Jennings This score provides an indication of the need for safety improvements, relative to the need of similar facilities in the same ICAPS category. Unlike thee Safety Health Score, the Needs Score is unbounded, and can be quite highh for locations with a high crash rate. Once the Need Scores were calculated, the segments with the highest scores were manually inspected using plots of the scores like thatt in Figure. The plots were used to determine where there were isolated locations with high scores (intersections) and clusters of locations with high scores (segments). 0 FIGURE Sample Plot of Needs Score for Identification of Priority Safety Improvement Areas. The analysis resulted in four distinct categories of Priority Safety Improvement Areas: Segments with high crash rates but no concentration of crashes. Segments with high crash rates throughout, and a concentration of crashes at a single intersectionn Segments with multiple intersections, all with high crash rates Isolated intersections with high crash rates This step resulted in distinct Priority Safety Improvement Areas spanning the six districts and the ICAPS categories. They covered miles of roadway, comprising 0.% of the original length of state roadway centerline miles. CRASH RATE FORECASTING METHODOL LOGY The recently developed HSM provided the basic methodology for estimating the potential reduction in the number, severity, and type of crashes for segments and intersections. The HSM predictive method provides a procedure to estimate the expected average crash frequency by total crashes, crash severity, and crash type for a roadway network, facility, or site. With this method, the roadway is divided into individual sites that are homogenous roadway segments and intersections. A facility comprises a contiguous set of individual intersections and roadway

8 Loudon, Schulte, Pilachowski, Jennings 0 0 segments, referred to as sites. Different facility types are determined by surrounding land use characteristics, the roadway cross-section, and the degree of access. For each facility type, a number of different site types may exist, such as divided and undivided roadway segments and unsignalized and signalized intersections. A roadway network comprises a number of contiguous facilities. The predictive method is used to estimate the expected average crash frequency for an individual site, with the cumulative sum of crash frequencies for all sites serving as the estimate for an entire facility or network. The predictive models used within the HSM method are of the general form shown below. N predicted = N spf x * (CMF x * CMF x * *CMF yx ) *C x () where: N predicted = predicted average crash frequency for a specific year for site type x N spf x = predicted average crash frequency determined for the base conditions of the Safety Performance Function (SPF) for site type x CMF yx = Crash Modification Factors (CMFs) specific to site type x (r=rural and u=urban) and geometric and traffic control features y C x = calibration factor to adjust the SPF for the local conditions for site type x (r=rural and u=urban) The Crash Modification Factors (CMFs) shown in the equation are used to adjust the estimate of average crash frequency for the effect of the specific geometric design and traffic control features of a local site. These factors are the characteristics that determine what a future crash frequency would be, but also represent the types of safety improvements for which a crashreduction potential can be estimated. The CMFs in the HSM have been determined by a national review panel to have sufficient historical data on effectiveness to be considered statistically reliable. A partial list of CMFs with ranges of values is shown in Table. For many of these countermeasures, the CMF varies by the level of crash severity. In addition to the CMFs in the HSM, others are available from various sources. The Federal Highway Administration maintains a web site with a clearinghouse of all CMFs included in the HSM as well as additional CMFs that have been estimated by safety practitioners from available data. The clearinghouse is a dynamic database and is continually being updated with new studies and CMFs. The CMFs in the clearinghouse are rated based on their statistical reliability and sample size. The web site can be found at the following web address: The research team used CMFs from this site, when available, for analysis of potential countermeasures not covered by the CMFs in HSM. Available CMFs are often specific to a certain roadway environment or crash severity.

9 Loudon, Schulte, Pilachowski, Jennings 0 TABLE Range of Values of Highway Safety Manual Crash Modification Factors CMF Description Range of CMFs Roadway Segments Lane width.00. Shoulder width and type 0.. Horizontal curves length, radius, spiral transitions.00 unb. Sideslopes.00. Horizontal curves/superelevation.00. Grades.00. Driveway density.00 unb. Centerline rumble strips (Y/N?) Passing lanes (Y/N?) Two way center turn lanes (Y/N?) Roadside design 0.. Lighting (Y/N?) Automated speed enforcement (Y/N?) Intersections Skew angle.00. Left turn lanes (major approaches only) Right turn lanes (major approaches only) Lighting DEVELOPMENT OF INPUT DATA Roadway data describing the physical features of all of the Priority Safety Improvement Areas were required for calculation of base year CMFs. For roadways segments, standard ITD databases were investigated, but it was found that some of the data that were needed to calculate the CMFs for small portions of the segments rated Worse than Average was not available in the ITD statewide data system. The additional data items that were needed to apply the HSM procedures included the following: Access Density Turn Lane Locations Passing Lane Locations Intersection and Segment Lighting Centerline Rumble Strips Two-Way Left-Turn Lanes Roadside Hazard Rating Shoulder Type Presence of Short -Lane Sections Number of Intersection Legs Utility and Signage Locations This information needed to be collected for the roughly miles of Priority Safety Improvement Areas. To address the additional data needs, the research team relied on the ITD Digital Videologs and Google Earth to collect the additional data. Figure provides an image from the videologs used for measurement. Shoulder widths were sampled at 0. mile intervals. Information on horizontal curves such as curve length and radius was developed based on a text report that can be

10 Loudon, Schulte, Pilachowski, Jennings generated using the video log software. A similar reportt for verticall curvature was used to develop information on grades. Driveway densities were calculated by directly observing the number of driveways along the roadway from the videologs. Information needed for the Roadside Design CMF includes clear zone characteristics such as width, surface roughness, slope, and the presence/ /absence of fixed objects. These features were observed directly from the videologs. Automated speed enforcement was assumed not to exist along any of the highways. Information for the intersection CMFs was developed using a combination of aerial photos and the video logs. Intersection skew angle was estimated from aerial photos. The presence/ /absence of turn lanes on the major road approaches of the intersections was determined from the video logs, as well as the existence of intersection lighting. Intersection data was collected only for those locations where traffic counts were available for the minor road, because without this information the HSM methodology could not be applied. 0 0 FIGURE Example Measurement from ITD Digital Videolog In addition to the roadway data, traffic volume and crash data weree needed for the calculation of existing crash rates for roadway segments and intersections. Base year traffic volumes were obtained from ITD s traffic count database. Crash data were obtained from the ITD Webcars database. Individual crashes were assigned to roadways segments and intersections according to the guidelines contained in the HSM. DEVELOPMENT OF DIAGNOSES AND COUNTERMEASURES For each of the Priority Safety Improvement Areas a detailed review of crash data for the previous five years was conducted. For intersections, this included the preparation of collision diagrams that summarized the frequency of crashes by direction of travel and crash type. In addition to the crash data, information on site characteristics was gathered from ITD video logs and aerial imagery. This information included geometric features, roadway delineation and signing, roadside hazards, access, and other characteristics. Once assembled, this information was summarized in tabular form and analyzed in an attempt to establish crash patterns and potential contributing causes. For segments, crashes were summarized by various characteristics such as direction of travel and crash type. This analysis allowed for the identification of crash patterns including direction of travel (opposing, same direction, and angle), and crash event (rear end, sideswipe, opposing sideswipe, angle). By grouping crash types, the cause off each type can be more easily identified. The information on site characteristics gathered from ITD video logs and aerial imagery, including roadway delineation and signing, roadside hazards, and access, was used to identify physical characteristics of the roadway that mayy have had an effect on the crash record for

11 Loudon, Schulte, Pilachowski, Jennings each location. From the crash record, information on the operating environment during a crash event was gathered. This included time of crash, lightingg conditions, weather conditions, and road surface environment. Using the Human Factors Guidelines (HFG) (), the physical features and operating environment are analyzed from the standpointt of driver capabilities and limitations. An understanding of how the driver interprets and reacts to specificc features of the roadway and operating environments is essential in accurately identifying crash causality and the appropriate countermeasures. An example application of the HFG is for a -mph curve located at the bottom of a long downgrade along a rural -mph highway illustrated in figure. The highway is relatively straight to the north and south of the curve. There have been a total of crashes over the past three years on the curve. 0 0 FIGURE Example Location for Application of Human Factors Guidelines The HFG indicates that driver expectations about a curve are an important factor in drivers over the past three years on the curve. Expectations, inn turn, are significantly influenced by the driver s experience with previous curves and tangents. Based on this information, the diagnosis for this location was that there was inadequate driver awareness of the curve and curve characteristics and that this was likely influenced by the lack similar curves on both sides of this section (previous driver experience). The drivers misjudgment of speed on the curve could also be affected by downgrade prior to curve. Once a diagnosis was developed, one or more countermeasures that might be effective in reducing the crash rate or severity were identified. Thee investigation of countermeasures focused on those improvements for which a CMF was available, becausee the objective of the research effort was to assess the effectiveness of the HSM methodologies in estimating the crash-reduction potential of safety projects. However, if other improvementss were identified thatt could significantly affect the crash rate, these were also noted. Additional factors consideredd in the identification of countermeasures were feasibility and cost. In certain cases no countermeasures were recommended if, based on the diagnosis, it appeared that no improvements could be made to the roadway environment that would reduce the crash rate. ESTIMATION OF CRASH REDUCTION POTENTIAL FROM COUNTERMEASURES The crash reduction potential of the countermeasures was estimated using the HSM procedure previously described, but only the countermeasures for which there is a CMF could be analyzed. The research team used the CMFs in the HSM plus those in the FHWA inventory of CMFs. When possible, the effect of the countermeasuree on both frequency and severity of crashes was estimated. For some of the countermeasures, the CMF provides this capability. The application procedure was an adaptation of the standard HSM methodology in whichh crash

12 Loudon, Schulte, Pilachowski, Jennings frequencies are estimated based on the safety performance function, local calibration factor, and CMFs. With the modified procedure, observed crash rates were substituted for the safety performance function and calibration factor. This was done because of the significant level of effort required to develop a local calibration factor that would have exceeded the scope of the study. To allow for comparison across countermeasures, the crash reduction benefits were translated to a dollar value based on the estimated dollar cost of a crash for each severity level. The most recent values developed by ITD at the time of the analysis were used (). They were as follows: Fatalities $,, Serious Injuries $0, Visible Injuries $, Possible Injuries $, Property Damage Only $,0 The HSM procedures did not allow the research team to differentiate between the three types of injury crashes in estimating the crash reduction potential so reduction in injury crashes were assumed to be proportional to the observed distribution in the five-year history for the location. The potential savings from each countermeasure for each priority location was calculated. This step was done by first determining the expected reduction in crash rates from a given countermeasure. The expected crash rates were then used to develop an expected annual cost from crashes. The annual savings was then calculated to be the difference between the observed annual cost and the expected annual cost given the proposed countermeasure. The estimated savings ranged from $,00 for widening and paving the shoulder of a segment on a rural two-lane highway to $,0,00 for installing changeable speed warning signs for icy conditions for a major US highway. The large difference in potential savings between these two examples is heavily driven by the potential for reducing fatalities and injuries. Countermeasures with CMFs that specifically reduce fatalities or injuries have significantly greater estimated savings than those that do not if the five-year crash history for the location includes fatalities or injuries. The location on the rural highway had no fatalities or serious injuries and the CMF provided only a % reduction in crashes. In contrast, the location on the US highway had three fatalities and two serious injury crashes, and the CMF resulted in almost a 0% potential reduction in crashes. ESTIMATION OF COST OF COUNTERMEASURES The annualized costs of the countermeasures were estimated using planning-level unit cost rates agreed upon by the districts. The rates were developed based on the recent experience of District staff. The unit cost rates were designed to be comprehensive incremental costs associated with the countermeasure over its life cycle. This would include any design and engineering costs, construction costs and maintenance costs. Once the unit cost rates were multiplied by the units associated with the countermeasure (lane miles, number of signs, etc.) to yield a life-cycle cost, an annual cost was calculated by dividing the life-cycle cost by the number of years assumed in the life cycle. A cost horizon was given for each countermeasure based on input from district staff and the expected lifespan. Long-term improvements such as paving and intersection modification were given a maximum cost horizon of 0 years. Signage was given a cost horizon of - years depending on the environment and countermeasures with ongoing costs had a cost horizon of one year. The expected cost of a countermeasure divided by the life cycle length provided an annualized cost.

13 Loudon, Schulte, Pilachowski, Jennings PRIORITIZATION OF COUNTERMEASURES USING BENEFIT COST RATIO The prioritization of safety countermeasures in this research effort was based primarily on Benefit-Cost ratio, but only crash reduction benefits in terms of reduced fatalities, injuries or property damage were considered. Other benefits would be produced by the countermeasures and should ultimately be considered before making funding decisions about the safety projects. Many of the countermeasures analyzed would result in reduced delay either because of the reduced crash frequency or because the countermeasure increases the throughput of the highway. Crashes often result when there is conflict caused by congestion and so many countermeasures address both safety and congestion. Table provides an assessment of countermeasure effectiveness by ICAPS category of the countermeasures for which a benefit could be estimated. They are listed by category in order of Benefit-Cost ratio. Average Percent Savings in Crash Costs and Average Crash Cost Savings are also provided as ranking measures. Of the countermeasures analyzed, had Benefit-Cost ratios in excess of.00. The countermeasures with the highest Benefit-Cost ratios tended to be those that did not require high construction costs. Intersection improvements such as protected left-turn signal phasing, left-turn lanes, and right-turn lanes had high Benefit-Cost ratios as did signage such as dynamic speed warning signs and chevron sign for curves. Improvements that required a high level of additional labor such as reduced speed limits with additional enforcement and additional winter maintenance had the lowest Benefit-Cost ratios. Many of the countermeasures in the middle of the range required more significant construction cost which tended to lower the Benefit-Cost ratio. For some including consolidating driveways, construction of roundabouts to replace intersections, and adding passing lanes, there would also be other potential benefits particularly in adding capacity. In the case of widening shoulders, the improvement also provides opportunities for use by bicyclists, snowmobilers, operators of agricultural equipment or as bus pull-out bays. A Benefit/Cost ratio could not be developed for all countermeasures. For some, the benefit could not be estimated because there was no CMF available and one countermeasure cost could not be estimated from unit cost rates. If no CMF was available, there was no attempt to estimate a cost. Many of these countermeasures may be valuable and cost-effective improvements and should be given consideration along with the others when plans for improvements are being made for a specific location. It may be possible for some to be added to a work package along with other improvements at little extra cost. While the Benefit-Cost ratio proved a useful measure for prioritization of safety funds, other measures were also found to be useful for decision making about what project to program at the district level. Because many benefits with safety-improvement potential are considered for other reasons such as capacity improvement, the cost of the improvement may be justified on other grounds. In these situations, a measure that excludes the cost of the improvement may be appropriate. For the projects evaluated in the Idaho work, the average savings in crash costs was also calculated.

14 Loudon, Schulte, Pilachowski, Jennings TABLE Countermeasures for ICAPS Category Interstate Countermeasure Summary Average B/C Average % Crash Cost Savings Average Crash Cost Savings Baseline Crash Costs Number ICAPS: Interstate Install changeable speed warning signs for icy conditions % $ 0,0 $,0,0 Increased pavement friction % $, $, Extend off ramp acceleration lane. % $, $ 0, Add lighting. % $, $,0 Improved winter maintenance 0. % $, $, Lower posted speed by mph (increase enforcement) 0.0 % $, $,0 ICAPS: Statewide Add chevrons in curve section 0 % $,0 $,0,0 Install changeable speed warning signs for icy conditions 0 % $,0, $,0,0 Advance curve warning sign with advisory speed 0 % $, $,0,0 Add yellow backplate to signal heads 0 % $,0 $,0 Add rumble strips % $ 0, $, Advance street name signs % $, $, Install flashing beacons as advance warning % $, $, Install Raised Center Median % $,, $,0, Widen and pave shoulders. % $, $,00,0 Add right turn lanes. % $,0 $, Add left turn lanes. % $, $, Modify horizontal curve.0 % $, $,00,0 Convert to roundabout. % $, $,0 Add lighting. % $ 0, $, Install overhead flashing beacon. % $, $, Improved winter maintenance 0. % $, $,00, ICAPS: Regional/District Convert intersection to four way stop 00 % $,, $,, Add rumble strips % $, $, Add chevrons in curve section 0 % $,0 $,00 Advance curve warning sign with advisory speed % $, $, Prohibit on street parking % $, $, Install Raised Center Median 0 % $, $,,0 Install a TWLTL 0% $, $,, Install new fluorescent curve signs. % $, $, Add lighting. % $, $ 0, Increased pavement friction. % $,0 $, Add right turn lanes. % $, $, Add left turn lanes. % $, $, Install overhead flashing beacon. % $,0 $,,00 Widen and pave shoulders. % $,0 $,0 Reduce skew angle.0 % $ 0, $, Signalize intersection 0. % $,0 $,0 Convert to two "T" intersections 0. % $,0 $,

15 Loudon, Schulte, Pilachowski, Jennings 0 0 The majority of the countermeasures with the highest percent savings are capital improvements involving a change in intersection type, curve or addition of turn lanes. Although there were many countermeasures that were ranked high in all three of the ICAP categories, many of the lower cost improvements were appropriate for the lower volume roads in the Regional and District categories. This is reflected in Table where performance measures are compared across ICAP category and area type (urban and rural). In each case the Benefit-Cost ratio for the countermeasures for the more rural and lower volume roads are considerably higher than the urban and higher volume roads. The results for Regional routes show both the highest number of proposed countermeasures and the highest average benefit-cost ratio, however, the Statewide routes have the highest average percent savings and total savings. Table shows that while there were many more rural locations analyzed and they had a much higher benefit-cost ratio, urban locations had, on average, a higher percent and total crash cost savings. This is probably due to the higher volume of traffic on urban roads resulting in higher total costs (and therefore higher total savings). TABLE Summary of Results by ICAP Category and Area Type Countermeasure Summary Average % Average Average Crash Cost Crash Cost B/C Savings Savings Number ICAPS Category Interstate % $,000 Statewide % $,,000 Regional 0.0 % $ 0,000 District 0.0 % $ 0,000 Area Type Urban % $,000 Rural 0 % $,000 RECOMMENDATIONS FOR FURTHER RESEARCH OR DEVELOPMENT The work in this research effort has provided a major step toward a data-driven method for prioritizing safety projects in Idaho. The work has also helped to identify two areas in which additional research or development could support continued progress. The HSM recommends that a calibrated model of crash frequency and severity be used to estimate the average annual crash frequency for locations where safety improvements are being considered. Lacking a locally calibrated model, the analysis for this project was conducted using the previous five-year average crash rate as an estimator of the future crash rate. Crashes have a stochastic nature and a safety problem area identified based on the five-year crash history can be the result of random variation, particularly with respect to the specific location where the crash(es) occurred. Having a calibrated HSM procedure would allow more assessment of the need for safety improvements based on roadway and traffic operations characteristics and less on the specific five-year history of crashes. The prioritization of potential safety countermeasures in the project included consideration of only crash reduction benefits in the Benefit-Cost ratio calculations. Many of the countermeasures analyzed could also have benefits such as congestion reduction (increased capacity) and improved conditions for other modes (e.g., wider shoulders would improve the safety of shoulders for bicycling, snowmobiling or other slower moving vehicles). For many

16 Loudon, Schulte, Pilachowski, Jennings locations, the reduction in crashes would have congestion reduction benefits because traffic would not be delayed by crashes, and many of the improvements would reduce congestion under normal, non-crash conditions as well. Procedures could be developed to incorporate more types of benefits into the calculation of B/C ratios. CONCLUDING OBSERVATIONS The HSCA process represents an accepted, defensible, and repeatable process. The primary analysis tool used within the HSCA process, the HSM, is a nationally recognized, state-of-the-art resource for the analysis of highway safety needs and identification of countermeasures. Another important tool used within the process is the HFG, which is the most comprehensive source of information on the relationship of the driver to the roadway and traffic operating environment currently available. Use of these resources makes the process defensible, in addition to the objective, system-wide approach that is followed in the identification of safety needs and countermeasures. The step-by-step nature of the process, with standard procedures for the measurement of safety needs and the potential effectiveness of countermeasures, allow it to be repeatable for future application. The HSCA process screens the locations of interest to identify the highest priority locations to reduce the length of roadway over which additional data must be collected for application for the HSM predictive procedure. Even without the need for fieldwork, using the ITD video logs or Google Earth to collect the additional roadway characteristics for the application of the HSM methodology can require significant labor. By using the readily available data in the ITD data systems to identify the highest priority locations, the number of centerline miles of state highway for which the supplemental data were needed was reduced from 000 total miles in the state system to miles identified as Priority Safety Improvement Areas. This represented a reduction of. percent. The HSCA process places ITD in-line with FHWA directives for data-driven, performance-based safety decision-making processes. MAP- clearly indicates that safety is the US DOT s top priority within all of its programs and that a key strategy for achieving this goal for highways is for states to have a data-driven, performance-based safety system. In its Summary of Highway Provisions for MAP-, the FHWA states that: The Highway Safety Improvement Program (HSIP) emphasizes a data-driven, strategic approach to improving highway safety on all public roads that focuses on performance. The foundation for this approach is a safety data system, which each State is required to have to identify key safety problems, establish their relative severity, and then adopt strategic and performance-based goals to maximize safety. By systematically identifying Priority Safety Improvement Areas and safety countermeasures statewide, the HSCA directly addresses this requirement. REFERENCES.) Idaho s Strategic Highway Safety Plan. Idaho Transportation Department, July, 0..) Highway Safety Manual: First Edition, 0. American Association of State Highway and Transportation Officials, Washington, D.C., 0..) Niemeier, D., Reed, T., Rutherford, G., and Morin, P. (). Statewide Programming: Implementing Transportation-Policy Objectives. Journal of Infrastructure. Systems, (), 0.

17 Loudon, Schulte, Pilachowski, Jennings 0.) Reed, T. L., Niemeier, D. A., & Rutherford, G. C. (). Prioritization of capacity improvements..) Dixon, K., Sarasua, W., Daniel, J., and Mazur, G. (00). Tool for Rural and Statewide Multimodal Transportation Planning. Journal of. Computers and Civil Engineering., (),..) Swords, A., Goldman, L., Feldman, W., Ehrlich, T., Bird, W. (00). Analytical Framework for Prioritizing Bicycle and Pedestrian Investments: New Jersey s Statewide Master Plan Update, Phase. Journal of the Transportation Research Board. Volume /00, -..) Smith, D. J., Harris, L. D., & Mazzotti, F. J. (). Highway-wildlife relationships (No. WPI 0)..) Richardson, M., Gatti, R., (). Prioritizing wetland restoration activity within a Wisconsin watershed using GIS modeling. Journal of Soil and Water Conservation. Volume, No., -..) Sanghavi, S., & Mattejat, P. (00). Stormwater GIS: Managing State-wide Stormwater BMPs. Public Works, ()..) Shaw, William, William R. Loudon, Robert J. Schulte and Joanna Liu. Visualizing Long- Range Health of Rural Recreational and Agricultural Corridors. Transportation Research Record No., 00, pp. -..) Human Factors Guidelines for Road Systems, Second Edition, NCHRP Report 00, Transportation Research Board of the National Academies, Washington, D.C., 0..) Idaho Traffic Crashes 0. Idaho Office of Highway Safety, Idaho Transportation Department, 0.