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35 Table 10. Total WVC and deer carcass removals by roadway characteristic (20012003). Number and Number and Number and Percentage of Percentage of Percentage of WildlifeVehicle Deer Carcass Roadway Milesa Collisions Removals Roadway System 1,020.46 1,892 6,382 Interstate (0.9%) (8.2%) (25.3%) 3,635.25 6,042 10,205 U.S. Highway (3.2%) (26.2%) (40.4%) 5,039.19 5,722 8,075 Iowa State Route (4.4%) (24.8%) (32.0%) 30,843.84 6,826 119 Farm to Market Route (27.3%) (29.6%) (0.4%) Area Type 97,885.5 20,222 22,155 Rural (86.6%) (87.6%) (87.7%) 15,172.75 2,872 3,103 Urban (13.4%) (12.4%) (12.3%) Number of Lanesb 109,471.10 16,429 13,393 Two (96.8%) (71.1%) (53.0%) 2,033.43 4,898 9,650 Four (1.8%) (21.2%) (38.2%) a Roadway mileage changes each year. Number and percentage of roadway miles in table represents average annual mileage that existed from 2001 to 2003. b Number includes through, turn, and two-way left-turn lanes. ferent spatial accuracies of the data and the plots on the re- The length of the segments evaluated and modeled was pri- sults of this work are noted where appropriate. Table 10 marily defined by the changes in roadway cross section design shows the number and percentage of Iowa roadway mileage, (e.g., number of lanes). Only those rural roadway segments with reported WVCs, and deer carcass removals along roadways a length of < _ 0.1 mi were used in the development of the model. with varying characteristics. The traffic volume and cross section attribute data collected Findings and Results were also used with the WVC and deer carcass removal data Aspect 1: Application of Reported WildlifeVehicle to develop prediction models. Descriptive statistics for the Collision Data 2001 to 2003 roadway length, AADT, WVC, and deer carcass removal data used in the model development are summarized Tables 12 through 14 provide details of the SPFs. For each of in Table 11. the four states, three levels of SPFs were developed with varying Table 11. Modeling database summary (rural segments > 0.1 mi). Roadway Two-Lane Rural Roadway Multilane Rural Roadway Category Total Mean Min Max Total Mean Min Max Length (Miles) 6,529 0.49 0.10 1.78 1,317 0.35 0.10 1.39 Average Annual Daily Traffic NAa 2,433 103 13,000 NA1 12,659 180 77,433 (AADT) WildlifeVehicle Collisions/ 6,721 0.39 0.00 16.32 3,438 0.87 0.00 14.23 Mile-Year Carcass Removals/ 11,640 0.64 0.00 75.85 8,288 1.97 0.00 93.33 Mile-Year a NA = Not Applicable

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36 Table 12. SPFs for rural two-lane roadways. Model Form: Total wildlifevehicle collisions/mile-year State/ = AADT 1 exp 2 SURFWID 3 HI 4 SPEED 5 LANEWID Terrain Model ln Dispersion 1 2 3 4 5 (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) parameter -7.8290 0.6123 CA 1 All 1.6098 (0.1868) (0.225) -8.7034 Flat (0.2005) -8.1810 0.6636 CA 2 Rolling 1.4831 (0.1930) (0.0228) -8.0343 Mountainous (0.1989) -8.5357 Flat Design (0.2046) 55 -7.9275 0.6518 CA 3 Rolling -0.3310 1.4493 (0.1968) (0.0230) (0.0449) -7.7157 Else = 0 Mountainous (0.2029) -4.5625 0.3743 NC 1 All 0.9222 (0.0576) (0.0078) Flat -4.3984 Rolling (0.0745) 0.3637 NC 2 0.8142 -5.5363 (0.0077) Mountainous (0.0653) Flat -4.3805 Posted Rolling (0.0773) < 55 0.4447 -0.0122 NC 3 -0.7165 0.7353 -5.7195 (0.0087) (0.0022) Mountainous (0.0248) (0.0685) Else = 0 -9.1135 1.0237 UT 1 All 1.7610 (0.1423) (0.0205) -9.3123 Flat (0.3385) -9.0528 1.0092 UT 2 Rolling 1.6123 (0.3393) (0.0410) -8.7728 Mountainous (0.3006) -12.987 Flat Posted (0.9608) 55 -12.803 0.8073 0.4751 UT 3 Rolling -0.6646 1.3985 (0.9613) (0.0455) (0.0838) (0.1344) -12.408 Else = 0 Mountainous (0.9485) -8.6850 0.7802 WA 1 All 1.3825 (0.3020) (0.0367) -8.5319 0.8034 -0.0584 WA 2 All 1.0237 (0.3552) (0.0426) (0.0117) Posted 55 -8.5161 0.7622 -0.0696 WA 3 All 0.4358 0.9528 (0.3493) (0.0426) (0.0124) (0.0964) Else = 0 data requirements. The first level required only the length and research. These applications are illustrated in the "Interpreta- AADT of a segment. The second level included the requirement tion, Appraisals, and Applications" section. that segments be classified as flat, rolling, or mountainous ter- In general, the calibrated SPFs make good intuitive sense in rain. The third level of SPFs added additional roadway variables that the sign, and to some extent the magnitude, of the esti- such as average lane width. All variables were from state HSIS mated coefficients and exponents accord with expectations. data. Segments were defined as sections of roads, generally be- Surprisingly, the exponent of the AADT term, although rea- tween significant intersections and having essentially common sonably consistent for the three levels of models in a state, varied geometric characteristics. Illustration of the application of the considerably across states. This exponent varied significantly SPFs developed is a key component of this aspect of the safety across facility types, reflecting differences in traffic operating

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37 Table 13. SPFs for rural multilane roadways. Model Form: Total wildlifevehicle collisions/mile-year = AADT 1 exp 2 MEDWID 3 HI 4 SPEED State/Model Terrain ln 1 2 3 (s.e.) (s.e.) (s.e.) (s.e.) -5.2576 0.3290 CA 1 All (0.4397) (0.0470) -6.4592 Flat (0.4523) -5.7615 0.3926 CA 2 Rolling (0.4398) (0.0464) -5.5220 Mountainous (0.4498) -6.4885 Flat (0.4485) -5.8372 0.4145 -0.0057 CA 3 Rolling (0.4360) (0.0464) (0.0015) -5.6577 Mountainous (0.4462) -3.3660 0.2501 NC 1 All (0.6314) (0.0684) Flat -2.5310 Rolling (0.6063) 0.1736 NC 2 -4.1844 (0.0641) Mountainous (0.5934) Flat -2.4303 Rolling (0.5871) 0.1858 NC 3 -4.0785 (0.0621) Mountainous (0.5741) -4.1217 0.4414 UT 1 All (0.6231) (0.0742) -4.4878 Flat (1.5295) 0.3900 UT 2 Rolling -3.4508 (0.1754) Mountainous (1.5013) -12.7417 1.2066 WA 1 All (1.9219) (0.2028) -12.9945 Flat (1.9091) 1.1398 WA 2 Rolling -11.8326 (0.1987) Mountainous (1.8894) -14.1608 Flat (2.1029) 1.2721 0.1244 WA 3 Rolling -13.2591 (0.2153) (0.0775) Mountainous (2.0800) conditions. The variables found to be significant at the 10% For application in another state, or even for application level varied by state were: in the same four states for different years from those in the calibration data, the models should be recalibrated to re- AADT: Annual average daily traffic flect differences across time and space in factors such as SURFWID: Total surface width (feet) collision reporting practices, weather, driver demograph- LANEWID: Average lane width (feet) ics, and wildlife movements. In essence, recalibration HI: Average degree of curvature involves using a multiplier, which is estimated to reflect SPEED: Posted speed in North Carolina & these differences by first using the models to predict the design speed in California (mph) number of collisions for a sample of sites for the new state MEDWID: Median width (feet) or time period. The sum of the collisions for those sites is MEDTYPE: Positive barrier or unprotected divided by the sum of the model predictions to derive the

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38 Table 14. SPFs for rural freeways. Model Form: Total wildlifevehicle collisions/mile-year = AADT 1 exp 2 MEDWID 3 HI 4 SURFWID 5 MEDTYPE State/Model Terrain ln 1 2 3 4 5 Dispersion (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) parameter -6.2814 Flat (0.7166) 0.2810 CA 1 1.5885 Rolling -4.7526 (0.0726) Mountainous (0.7098) -5.6746 Flat (0.6925) 0.3050 -0.0126 CA 2 1.3543 Rolling -4.3198 (0.0700) (0.0014) Mountainous (0.6857) -4.3930 0.4356 UT 1 All 1.9966 (1.4121) (0.1550) -7.8707 Flat (1.4831) -6.9760 0.7272 UT 2 Rolling 1.5641 (1.4811) (0.1632) -6.0374 Mountainous (1.4516) 8.0592 Median Flat (1.4808) Type -7.1234 Rolling (1.4773) Positive 0.7472 barrier UT 3 1.5277 (0.1630) -1.0633 -6.0651 (0.4623) Mountainous (1.4465) Unprotected 0.0000 -15.5153 1.3969 WA 1 All 0.8816 (1.7866) (0.1809) -16.8612 Flat (1.7977) -15.8572 1.4355 WA 2 Rolling 0.7807 (1.7634) (0.1784) -15.4443 Mountainous (1.7846) -9.9014 Flat (3.9034) -8.8909 1.4507 -0.1483 WA 3 Rolling 0.7867 (3.8877) (0.1793) (0.0765) -8.4610 Mountainous (3.8975) multiplier. Further details of this procedure are provided in removal data on a roadway network within a GIS platform. Appendix B. This information is useful because it helps define where the In deciding which among available competing models is WVC and deer carcass removal data were reported or best to adopt for another state for which a similar model may collected, and whether the occurrence of either is actually over- not be available, goodness-of-fit tests must be conducted. or under-represented along roadways with particular charac- Choosing the most appropriate model is especially important teristics. In addition, the results of visual and quantitative because the exponents for AADT, by far the most dominant WVCs, and deer carcass removal comparisons (statewide, variable, differ so much between states. A discussion of these example corridor, and model content) are described. tests is provided in a recent FHWA report.241 A summary is In general, the amount of two-lane roadway mileage used presented as part of Appendix B. in the modeling was almost 5 times greater than the multilane roadway mileage (See Table 11). Two-lane roadways with medians were not included. The multilane database included Aspect 2: Comparison of WildlifeVehicle Collision all State Routes, U.S. Highways, or Interstate highways with and Carcass Removal Data more than two through lanes. Overall, despite the propor- The findings from this aspect of the safety analysis focused tions of roadway mileage in the database, approximately two on the challenges related to combining WVC and deer carcass WVCs were reported along the two-lane roadways for every

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39 WVC reported along the multilane roadways. Similarly, the modeling activities in this research are noted below. The number of deer carcasses removed from two-lane roadways statistics in Table 10 might also be used for gross compari- was about 1.4 times that removed from the multilane road- son purposes to roadway segments of interest with similar ways. The mean number of WVCs and carcass removals per characteristics. A review of the percentages by roadway sys- mile-year, however, along the multilane roadways in the tem reveals that the deer carcass removal data are primarily database are much greater than those along the two-lane from the interstates, U.S. Highways, and State Routes. This roadways. Additionally, the AADT along the multilane rural trend is not surprising because the data provided was from roadways was also greater than the two-lane roadways. the IaDOT. About 80% of the WVC reported, on the other hand, occurred on U.S. Highways, State Routes, and farm to WVC and deer carcass removal GIS activities. There are market roadways. The percentage of WVCs and carcasses a number of advantages when information is incorporated removed along interstates, U.S. Highways, and State Routes into a GIS platform, including an increased ability to organ- are much greater than their statewide roadway mileage ize and integrate spatial data, the relatively easy presentation would suggest. For every reported WVC along the interstate, of the data, and the capability to quickly analyze and/or com- there were more than three carcasses collected. Table 10 pare one or more datasets. Visual patterns are also easier to shows that the percentage of urban and rural roadway discern, and data can be assembled from multiple sources and mileage is essentially the same as the percentage of WVCs formats to produce broader and more rigorous evaluation ac- and deer carcass removals in these areas. From a roadway tivities. The GIS process in a safety data project is typically mileage point of view, the number of WVCs and deer carcass composed of three steps: (1) data acquisition and importa- removals also appears to be over-represented along four- tion, (2) data management, and (3) spatial analysis. The first lane roadways. More than 90% of the WVCs and deer carcass steps are often the most difficult. removals from 2001 to 2003 occurred along two- and four- The general objective of the GIS activities in this aspect of the lane roadways. safety data analysis was to combine and document spatial rep- resentations of the WVC and deer carcass removal locations. Statewide and sample corridor comparisons. The avail- Deer carcass removal data and locations are not normally avail- ability of WVC and deer carcass removal data in Iowa within a able in any consistent manner across jurisdictions. In this study, GIS platform that contains information about the Iowa roadway the carcass reports included route and milepost to reference network allowed a relatively easy comparison and calculation of locations of deer carcasses to the road network. To geo-code various safety measures related to each dataset. Statewide WVC these records, the research team obtained the location of the and deer carcass removal frequencies and rates are shown in mileposts from the Iowa State University Center for Trans- Table 15 for the 3-year analysis time period as are the combined portation and Education (CTRE). This information was devel- number of deer carcasses removed by the IaDOT and those sal- oped from different DOT data sources and combined with a vaged through the Iowa Department of National Resources GIS data set. The WVC data were relatively easy to incorporate (IaDNR). About 34% of roadside deer carcasses are salvaged into the GIS platform because latitude and longitude coordinate under permit from the state. Sixty-six percent of the roadside positions for each incident were available. The spatial accuracy deer carcasses are removed by IaDOT and their location noted of the carcass removal locations was different; they were esti- (these are the removals plotted in Figures 4 and 5). According to mated to the nearest 0.1 milepost. In addition, the individual the IaDNR, the roadway locations for the deer carcasses it whole milepost locations (e.g., 1.0, 2.0, etc.) on the Iowa road- permits for salvage are not consistently collected and should way GIS map were the only spatial data connection that would therefore not be used for analysis. allow the plotting of the deer carcass removal locations. For The numbers in Table 15 are general statewide measures schedule and budget reasons, therefore, the estimated locations and when recalculated for individual roadway segments are of the deer carcass removals were rounded to the nearest often different (Table 16). The data in Table 15 illustrate three milepost, summed, and plotted. statewide databases that provide different values for the WVC The total number of deer carcass removals in 2002 is plot- data in Iowa. The number of deer carcasses removed by ted in Figure 4 at each milepost (with scaled and shaded IaDOT, for example, is approximately 1.09 times greater than circles to represent the different number at each location). the number of WVCs reported to the police. The number of This spatial modification was considered appropriate given salvaged and unsalvaged deer carcasses, on the other hand, is the accuracy of the datasets provided, the objective of this approximately 1.66 times greater. The other safety measures work (i.e., a comparison of data as they might be available show a similar trend. However, only the WVCs and deer car- to a decision maker), and the WVC and carcass removal data cass removals in Table 15 are related to roadway location in likely to be available within other states. The impact of Iowa, and typically the location of the latter is not known. The this spatial alteration on the results of the comparisons and plots in Figures 4 and 5 show that the spatial patterns of the

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40 Table 15. Statewide wildlifevehicle collision and deer carcass removal metrics (2001 to 2003). Salvaged and Carcass Metricsa WVC Unsalvaged Deer Removalsb Carcassesc Total 23,094 25,258 38,283 Rate per Year 7,698 8,419 12,761 Rate per Roadway Mile 0.20 0.22 0.34 Rate per Hundred Million 25.3 27.6 41.9 Vehicle-Miles-of-Travel a Statewide roadway mileage and vehicle-miles-of-travel used in all calculations. b Deer carcass removals are those recorded and summarized by the Iowa DOT by location. c Salvaged and unsalvaged deer carcasses are summarized by the Iowa Department of National Resources. The Department of Transportation deer carcass removals are a portion of this total, but they are the only removals for which roadway location is known. WVC and deer carcass removal data are also different. It is show that reported WVCs and deer carcass removal data (as not likely that this conclusion will change if the data were available) likely have different spatial patterns. This lack of plotted differently. The use of different databases could lead similarity could lead to the implementation of countermea- to different statewide policy and corridor-level decisions sures along different roadway segments. Table 16 summarizes related to WVCs. In addition, the choice of the database used the WVC and deer carcass removal data from 2001 to 2003 could lead to different conclusions. for the roadway segments shown in Figure 5. The differences Figure 5 shows the reported WVCs and deer carcass in the magnitude of the WVCs and deer carcass removals that removals for sample roadway segments along Interstate 80 occur along these roadway segments are clear. Overall, the and U.S. Highway 18 in Iowa. Note that no WVCs were number of carcasses removed along the Interstate 80 segment reported along this segment of U.S. Highway 18 in 2002. was 8.6 times greater than the number of WVCs reported. A more detailed summary of the WVCs and deer carcass The number of carcasses collected along U.S. Highway 18, on removals along these two segments is shown in Table 16. the other hand, was 3.8 times greater than the number of re- These measures could be compared to the statewide results in ported WVCs. Table 15 and/or those calculated for roadways with similar More than 90% of the Interstate 80 segment length sum- characteristics (See Table 10). marized in Table 14 (and shown in Figure 5) was classified as a The results of this type of general comparison can be used four-lane rural freeway. The frequencies and rates in Table 16 as a filter to determine whether a particular roadway segment are all generally greater than the statewide measures for a needs more detailed consideration. Figures 4 and 5 generally roadway with these characteristics. Only the use of a WVC rate Figure 5. Deer carcass removal and WVC locations along segments of Interstate 80 and U.S. Highway 18 (2002).

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41 Table 16. Comparison of roadway segment WVC and deer carcass removal measures (2001 to 2003). I-80 I-80 U.S. Hwy 18 U.S. Hwy 18 Wildlife Deer Wildlife Deer Carcass Variable Rates Vehicle Carcass Vehicle Removals Collisions Removals Collisions (9.9 Mi) (8.4 Mi) (8.4 Mi) (9.9 Mi) Total Number 19.0 163.0 5.0 19.0 Rate / Year 6.3 54.3 1.7 6.3 Rate / Roadway Mile 2.3 19.3 0.51 1.9 Rate / Hundred Million 10.4 89.6 17.2 65.4 Vehicle-Miles-of-Travel Note: See Figure 5 for plots of 2002 wildlifevehicle collisions and deer carcass removals along these segments in Iowa. might lead to the conclusion that this segment has a typical WVCs or deer carcass removals per mile-year. Details of the WVC data level. The U.S. Highway 18 segment in Figure 5 is rural two-lane and multilane models are shown in Tables 17 primarily a two-lane rural roadway. Mixed conclusions result and 18. Prediction (not causal) models with only AADT are when the WVC and deer carcass removal measures for this provided later in this section. Volume-only models were roadway (See Table 16) are compared to relevant statewide developed for comparison and application purposes. The vari- measures. The WVCs and deer carcasses removals per mile ables considered for use in each of the models were selected along the segment are larger than the statewide measures, but from the Iowa roadway cross section database (which included the rates (based on volume) are both smaller than those cal- more than 90 factors). The following variables, which came culated for the entire state. Clearly, the choice of the data from the IaDOT database, were considered: (WVCs or deer carcass removals) and the measures (e.g., per AADT: Annual average daily traffic on roadway mile or rate) that are used impacts whether a particular road- (vehicles per day in both directions) way segment might be identified for closer consideration. The AVGSHLD: Average of left- and right-shoulder widths comparisons described above consider average values, but on two-lane roadways more critical WVC frequency or rate data could be used as an CRASHES: Number of police-reported animal-vehicle initial step to identify hotspot roadway segments. collisions (used in one model for deer carcass removal prediction) WVC and deer carcass removal model development and LANES: Total number of through lanes present comparison. Prediction models using WVC, deer carcass re- LSHDWID: Width of the left side or inside shoulder moval, and roadway cross section data from Iowa were (nearest foot) developed to assist in the identification of potential hotspot MEDTYPE: Classified as zero (0) if unprotected or 1 if a roadway segments and are described next. They can be applied positive barrier in a manner similar to those described previously in this report. MEDWID: Width of the median between the edges of This section of the safety analysis report focuses on the differ- traffic lanes (nearest foot) RSHDWID: Width of the right side or outside shoulder ences between the models developed with the WVC and deer (nearest foot) carcass removal data and the potential impact of those differ- SPEED: Posted speed in miles per hour ences. A site visit to each potential "high" collision or carcass SURFWID: Surface width of roadway measured from segment is necessary for confirmation purposes and the iden- edge of pavement to edge of pavement tification of specific countermeasure installation locations. (feet) The combination of WVC, deer carcass removal, and road- way location data in a GIS platform allowed the production of The form and content of the WVC and deer carcass re- prediction models to describe the relationships between the oc- moval prediction models developed for rural two-lane road- currence of a WVC or carcass removal and several roadway ways in Iowa are shown in Table 17. Two models were devel- cross section characteristics typically available through DOT oped for both WVCs and deer carcass removals with different databases. These analyses applied to rural paved two-lane and sets of independent variables. Both models are provided be- multilane roadways in Iowa with a State Route, U.S. Highway, cause they produce similar results, but have different input or Interstate designation. They can be applied within an variables, which may make them useful to different practi- empirical Bayesian approach. The negative binomial models or tioners. The variables in the models include AADT, SPEED, SPFs were created from 2001, 2002, and 2003 data to predict and AVGSHLD; for one deer carcass removal model, the num-

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42 Table 17. Models for rural two-lane roadways (segments > 0.1 mi) in Iowa. Model Form: Total WVCs or deer carcass removals per mile-year = Model AADT 1 exp AVGSHLD SPEED CRASHES 2 3 4 Dependent a Variable ln( ) 1 2 3 4 Dispersion (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) Parameter WVCs/ -5.9203 0.6164 0.0193 1.0179 Mile-Year (0.2088) (0.0283) (0.0067) WVCs/ -6.4968 0.6429 0.0095 1.0196 Mile-Year (0.3807) (0.0268) (0.0059) Deer Carcass -5.4332 0.5784 0.0677 5.2702 Removals/ (0.2957) (0.0403) (0.0096) Mile-Year Deer Carcass -4.9635 0.4890 0.0701 0.2714 5.0062 Removals/ (0.2954) (0.0405) (0.0096) (0.0225) Mile-Year a These symbols represent the parameters estimated in the modeling process and that measure the impact of each independent variable on the expected crash frequency. ber of reported WVCs was included. The model coefficients for countermeasure implementation. Of course, some of the dif- all models are shown in Table 17 along with their standard ferences shown in Table 17 are due to the differences in the spa- error and the model dispersion parameter. The impact of the tial accuracy of the information provided for the two databases variables in each model is somewhat different, and the ex- and ultimately plotted in the GIS platform. These accuracies, planatory value of the WVC model appears to be greater than however, are typical. the deer carcass removal model. The large dispersion parame- Similar WVC and deer carcass removal prediction models ter of the deer carcass removal model is high, which should be were also developed for rural multilane roadways in Iowa. The considered if it is applied. Given that most jurisdictions do not model coefficients for these models are shown in Table 18 as have deer carcass removal data by location, it is encouraging are their standard errors and the model dispersion parame- that the CRASHES data may be used as a predictor of carcasses. ters. There are more differences in these models than those Thus, if carcass data could be collected even for a subset of the produced for the two-lane rural roadways. The models in roadways in a jurisdiction, a model that included reported col- Table 18 contain different variables. The models include lisions to predict carcasses could be recalibrated and applied. one or more of the AADT, AVGSHLD, MEDTYPE, and The differences in these models further support the conclusion MEDWID predictor variables. As with the two-lane models, that the use of WVC or deer carcass removal data can result in the number of WVCs could also prove to be a useful predic- the identification of different roadway segments for potential tor of deer carcass removal frequency. The results of this Table 18. Models for rural multilane roadways (segments > _ 0.1 mi) in Iowa. Model Form: Total WVCs or deer carcass removals per mile-year = Model AADT 1 exp AVGSHLD MEDWID MEDTYPE CRASHES 2 3 4 5 Dependent Variable ln( ) 1 2 3 4 5 Dispersion (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) (s.e.) Parameter With Median Barrier: -0.2471 WVCs/ -0.9021 0.0527 0.0390 Mile-Year (0.0851) 0.6360 (0.3905) (0.0391) (0.0205) Unprotected: 0.0000 Deer Carcass -4.6677 0.5616 0.0017 Removals/ (0.5972) (0.0660) (0.0011) 7.8601 Mile-Year Deer Carcass -4.3118 0.4871 0.3314 Removals/ 7.2680 (0.5851) (0.0637) (0.0385) Mile-Year

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43 Table 19. Volume-only models (segments > 0.1 mi) in Iowa. Model Form: Total wildlifevehicle collisions or deer carcass removals Model per mile-year = AADT 1 Dependent Variable )ln( 1 Dispersion (s.e.) (s.e.) Parameter Rural Two-Lane Roadway WVCs/ -5.9894 0.6439 1.0204 Mile-Year (0.2077) (0.0268) Deer Carcass -5.5973 0.6662 Removals/ 5.3432 (0.2952) (0.0384) Mile-Year Rural Multilane Roadways WVCs/ -1.2494 0.1199 0.6381 Mile-Year (0.2985) (0.0321) Deer Carcass -4.8520 0.5919 Removals/ 7.8791 (0.5923) (0.0640) Mile-Year model development activity further support the importance the two datasets. The high dispersion parameters of the deer of choosing the appropriate database to evaluate collision carcass removal models in Table 19 should be noted. problem locations. The dispersion parameter of the deer car- Figures 6 and 7 plot the AADT (volume-only) deer car- cass removal model is high, which should be considered in the cass removal and WVC models in Table 19 for two-lane application of this model. and multilane rural roadways, respectively. Because AADT Finally, WVC and deer carcass removal models, with is the only independent variable, a simple comparison AADT as the only input variable, were also developed. These shows that the models diverge as AADT increases, dramat- models are shown in Table 19. They were created for applica- ically so for multilane roadways. These plots illustrate that tion if the data for the roadway cross section variables in the the deer carcass removal and WVC frequencies predicted previous models were not available. In addition, the volume- are different and not strictly linearly correlated. The avail- only models were compared to the other models to investi- ability of WVC data throughout the United States led the gate the additional explanatory value offered by the addition research team to ask whether the volume-only WVC mod- of more road cross section variables. A comparison of the dis- els might be recalibrated to predict deer carcass removals. persion parameters with those in Tables 17 and 18 reveals that To do so, the volume-only WVC models were applied to the inclusion of other roadway cross section variables in the the deer carcass removal database. The sum of the observed models adds little to the predictive strength of the WVC and deer carcass removals was then divided by the sum of the deer carcass removal models. In other words, the AADT predictions from the WVC model. This factor was applied measure contains most of the explanatory value of these as a multiplier to the WVC volume-only model and the models. Overall, the explanatory value of the WVC models deer carcass removal predictions were recalculated and with only AADT is still better than those developed with deer compared (See Figure 6 and Figure 7). This comparison carcass removal data. Some of this difference, as previously was completed separately for the two-lane and multilane stated, is due to the inconsistency in the location accuracy of rural roadway data. 2.50 Removals Per Mile-Year WVCs or Deer Carcass 2.00 1.50 WVCs 1.00 Deer Carcass Removals 0.50 0.00 0 2000 4000 6000 8000 10000 12000 14000 Average Annual Daily Traffic Figure 6. Two-lane rural roadway volume-only model results.