Western Transportation Institute
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/35
The Western Transportation Institute is the country's largest National University Transportation Center focused on rural transportation issues.
Because we live and work in rural communities, we understand the critical roles rural transportation plays in the lives of people, in the environment and in the economy.
We draw from our eight integrated research groups to create solutions that work for our clients, sponsors and rural transportation research partners. WTI focuses on rural issues, but some of our program areas also address the concerns of the urban environment. Whatever the objective, we bring innovation and expertise to each WTI transportation research project.
WTI's main facility with its state-of-the-art labs is adjacent to the Montana State University campus in Bozeman, Montana. We have additional offices in Alberta, Canada, and central Washington, and a large testing facility in rural Montana near Lewistown. Contact us to find out how to address your rural transportation research needs.
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Item Exploring Apex Predator Effects on Wildlife-Vehicle Collisions: A Case Study on Wolf Reintroductions in Yellowstone(Western Transportation Institute, 2024-09) Bell, Matthew; Huijser, Marcel P.; Kack, DavidThis study investigates the impact of wolf reintroduction on wildlife-vehicle collisions (WVCs) along a segment of US-191 bordering Yellowstone National Park. Wolves were reintroduced in 1995–1996, and subsequent wolf pack establishment may have influenced the behavior and population dynamics of prey species, potentially altering WVC patterns. Using carcass data collected from 1989 to 2021, the analysis was divided into two primary phases: before wolves (1989–1996) and after wolves (1997–2021). A series of linear mixed-effects models were developed to assess changes in WVCs across these time periods. Predictor variables included average annual daily traffic (AADT), elk population estimates, and wolf counts. Results showed that WVCs significantly declined in the post-wolf period, suggesting that the presence of wolves may reduce WVCs directly by modifying prey behavior and movement patterns, or indirectly by reducing prey population densities. Further analysis revealed that while elk populations were a significant predictor of WVCs before wolves were reintroduced, this relationship weakened post-reintroduction. Traffic volume did not significantly influence WVC patterns in either period, nor did it interact significantly with wolf presence. The inclusion of wolf counts as a continuous variable showed a negative relationship with WVCs, indicating that higher wolf densities may contribute to a further reduction in collisions over time. These findings suggest that apex predators can play a role in mitigating human-wildlife conflicts, such as WVCs, by influencing prey species’ behavior and distribution. The study provides valuable insights for wildlife managers and transportation planners, highlighting the potential benefits of predator conservation for road safety and ecosystem health.Item Patterns of Domestic Animal-Vehicle Collisions on Tribal Lands in Montana, U.S.(Western Transportation Institute, 2024-09) Bell, Matthew; Huijser, Marcel P.; Kack, DavidAnimal-vehicle collisions (AVCs) are a significant concern for motorist safety and pose a risk to both wildlife and domestic animals. This report analyzes spatial patterns of wildlife-vehicle collisions (WVCs) and domestic animal-vehicle collisions (DAVCs) on Montana’s tribal lands to identify high-risk areas and inform mitigation strategies. Data from the Montana Department of Transportation (MDT) for large mammal carcasses (2008–2022) and reported crashes (2008–2020) were used to perform Kernel Density Estimation (KDE) and Getis-Ord Gi* (GOG) hotspot analyses for three tribal reservations with sufficient data: Blackfeet, Crow, and Flathead. The KDE results show distinct spatial patterns for DAVCs and WVCs on each reservation, with DAVC hotspots concentrated near agricultural and grazing areas, while WVC hotspots were associated with natural habitats and wildlife corridors. The GOG analysis further revealed that DAVC hotspots tend to be more temporally stable, suggesting that collisions with domestic animals are influenced by consistent factors such as livestock access points and grazing practices. In contrast, WVC hotspots were more variable, likely driven by changes in wildlife movement patterns and seasonal behavior. Overall, the findings indicate that the elevated rates of DAVCs on tribal lands, compared to non-tribal lands, are likely due to unique factors such as open range grazing practices and road infrastructure adjacent to grazing lands. This report emphasizes the need for targeted mitigation strategies on tribal roads, such as enhanced livestock fencing, road signage, and livestock underpasses in high-risk areas, to reduce collisions and improve safety for both motorists and animals. Understanding the distinct spatial and temporal patterns of DAVCs and WVCs is crucial for developing comprehensive mitigation approaches that enhance safety and connectivity on Montana’s tribal lands.Item Identification and prioritization of road sections with a relatively high concentration of large wild mammal-vehicle collisions in Gallatin County, Montana, USA(2024-09) Huijser, Marcel P.; Bell, Matthew A.The primary objective of this project is to identify and prioritize the road sections in Gallatin County that have a relatively high concentration of collisions involving large wild mammals. These road sections may then later be evaluated for potential future mitigation measures aimed at 1. Reducing collisions with large wild mammals, and 2. Providing safe passage across roads for large wild mammals, as well as other wildlife species in the area. We acquired the 3 datasets related to large wild mammal-vehicle collisions in Gallatin County: 1. Wildlife-vehicle crash data collected by law enforcement personnel, 2. Carcass removal data collected by road maintenance personnel; and 3. Grizzly bear road mortality data by the U.S. Geological Survey. The carcass removal data and grizzly bear road mortality data were merged into one carcass database. We conducted separate analyses for the crash data and the carcass data. We conducted two different types of analyses to identify and prioritize road sections with the highest number of wildlife-vehicle crashes and carcasses: 1. Kernel Density Estimation (KDE) analysis that identifies road sections with the highest concentration of collisions, and 2. Getis-Ord Gi* analysis identifies road sections that have statistically significant spatial clusters of collisions. There was great similarity between the hotspots identified through the Kernel Density Estimation analyses for 2008-2022 and 2018-2022 for both the crash and carcass removal data. The same was true for the Getis-Ord Gi* analyses. Especially sections of I-90 and US Hwy 191 between I-90 through Four Corners to the mouth of Gallatin Canyon had the highest concentration of wild animal crashes and large wild animal carcasses. Based on the Getis-Ord Gi* analyses, these road sections generally had concentrations of crashes and carcasses that were significantly higher than expected should the crashes and carcasses have been randomly distributed. In other words, these road sections do not only have the highest concentration of crashes and carcasses, but the identification of these road sections is not based on coincidence. These road sections have a concentration of crashes and carcasses that is beyond random.Item Implementing wildlife fences along highways at the appropriate spatial scale: A case study of reducing road mortality of Florida Key deer(Pensoft Publishers, 2022-03) Huijser, Marcel P.; Begley, James S.Florida Key deer mortality data (1966–2017) showed that about 75% of all reported deer mortalities were related to collisions with vehicles. In 2001–2002, the eastern section of US Hwy 1 on Big Pine Key (Florida, USA) was mitigated with a wildlife fence, 2 underpasses, and 4 deer guards. After mitigation, the number of reported Key deer road mortalities reduced substantially in the mitigated section, but this was negated by an increase in collisions along the unmitigated section of US Hwy 1 on Big Pine Key, both in absolute numbers and expressed as a percentage of the total deer population size. The data also showed that the increase in Key deer collisions along the unmitigated highway section on the island could not be explained through an increase in Key deer population size, or by a potential increase in traffic volume. The overall Key deer road mortality along US Hwy 1 was not reduced but was moved from the mitigated section to the nearby unmitigated section. Thus, there was no net benefit of the fence in reducing collisions. After mitigation, a significant hotspot of Key deer-vehicle collisions appeared at the western fence-end, and additional hotspots occurred further west along the unmitigated highway. Exploratory spatial analyses led us to reject the unmitigated highway section on Big Pine Key as a suitable control for a Before-After-Control-Impact (BACI) analysis into the effectiveness of the mitigation measures in reducing deer-vehicle collisions. Instead, we selected highway sections west and east of Big Pine Key as a control. The BACI analysis showed that the wildlife fence and associated mitigation measures were highly effective (95%) in reducing deer-vehicle collisions along the mitigated highway section. Nonetheless, in order to reduce the overall number of deer-vehicle collisions along US Hwy 1, the entire highway section on Big Pine Key would need to be mitigated. However, further mitigation is complicated because of the many buildings and access roads for businesses and residences. This case study illustrates that while fences and associated measures can be very effective in reducing collisions, wildlife fences that are too short may result in an increase in collisions in nearby unmitigated road sections, especially near fence-ends. Therefore it is important to carefully consider the appropriate spatial scale over which highway mitigation measures are implemented and evaluated.