Scholarly Work - Western Transportation Institute
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/9749
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Item Setting up ROaDS Partners with Customized Surveys(Western Transportation Institute, 2024-09) Bell, Matthew; Kack, DavidThe Roadkill Observation and Data System (ROaDS) project, developed through a partnership between the U.S. Fish and Wildlife Service, National Park Service, and the Western Transportation Institute at Montana State University, provides a user-friendly data collection system to monitor wildlife-vehicle collisions (WVCs) and identify safe crossing locations on roads managed by federal land management agencies (FLMAs). This report outlines recent outreach efforts and successful implementation of the ROaDS system with external partners, including the Nevada Department of Transportation (NDOT) and the Indiana Department of Natural Resources (IDNR). Custom surveys were developed for these agencies to address specific data collection and conservation goals, resulting in improved capacity to monitor WVCs and identify high-risk areas for targeted mitigation. The project has garnered interest from several other state transportation agencies, showcasing the adaptability of the ROaDS system for diverse road and wildlife management applications. The successful deployment in Nevada and Indiana demonstrates the system’s potential to support data-driven decision-making and enhance wildlife connectivity across the country.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 10 Steps to Implementing Health in All Policies in Rural Communities(Western Transportation Institute, 2024-08) Comey, Danika; Madsen, MatthewThis toolkit serves as a guiding document for frontier, rural, and micro-urban communities to implement a Health in All Policies (HiAP) framework in rural America. Too often, rural America is overlooked when it comes to public health and policy work. This tool will guide public health practitioners, community planners, elected officials, healthcare providers, and those who are interested in improving community and public health by analyzing and improving local policy in rural communities. Barriers to accessing healthcare services are well documented in rural communities. Rural populations often face greater challenges accessing healthcare services compared to their urban peers such as long distances to primary care, lower insurance coverage rates, higher health needs, and higher rates of poverty [1–4]. Incorporating a HiAP framework in rural areas is an effective way to decrease health inequities and disparities between urban and rural communities.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 Risk mapping of wildlife–vehicle collisions across the state of Montana, USA: a machine-learning approach for imbalanced data along rural roads(Oxford University Press, 2024-05) Bell, Matthew; Wang, Yiyi; Ament, RobWildlife–vehicle collisions (WVCs) with large animals are estimated to cost the USA over 8 billion USD in property damage, tens of thousands of human injuries and nearly 200 human fatalities each year. Most WVCs occur on rural roads and are not collected evenly among road segments, leading to imbalanced data. There are a disproportionate number of analysis units that have zero WVC cases when investigating large geographic areas for collision risk. Analysis units with zero WVCs can reduce prediction accuracy and weaken the coefficient estimates of statistical learning models. This study demonstrates that the use of the synthetic minority over-sampling technique (SMOTE) to handle imbalanced WVC data in combination with statistical and machine-learning models improves the ability to determine seasonal WVC risk across the rural highway network in Montana, USA. An array of regularized variables describing landscape, road and traffic were used to develop negative binomial and random forest models to infer WVC rates per 100 million vehicle miles travelled. The random forest model is found to work particularly well with SMOTE-augmented data to improve the prediction accuracy of seasonal WVC risk. SMOTE-augmented data are found to improve accuracy when predicting crash risk across fine-grained grids while retaining the characteristics of the original dataset. The analyses suggest that SMOTE augmentation mitigates data imbalance that is encountered in seasonally divided WVC data. This research provides the basis for future risk-mapping models and can potentially be used to address the low rates of WVCs and other crash types along rural roads.Item The effectiveness of electrified barriers to keep large mammals out of a fenced road corridor and a campground(Parks Canada Agency, 2024-06) Huijser, Marcel P.For this project the researchers investigated the effectiveness of electrified barriers designed to keep large mammals out of a fenced road corridor (Trans-Canada Highway through Banff and Yoho National Park) and a campground (Lake Louise Campground, Banff National Park). The barriers were designed for large ungulates (e.g. white-tailed deer, mule deer, elk, moose) and large mammal species with paws (e.g. black bear, grizzly bear). The barriers consisted of steel pipes that were partially electrified. None of the white-tailed deer, mule deer, elk, moose, black bears, grizzly bears, red foxes, and coyotes that were observed on the habitat side of the barriers crossed the electrified barriers into the fenced road corridor or the campground. A black bear attempting to exit the fenced road corridor failed to cross to the habitat side of the electrified barrier. Two red foxes and one wolverine did appear to exit the fenced road corridor to the habitat side of the electrified barrier, but these three crossings were all in winter when the voltage was likely compromised because of snow and road salt. In addition, crossings to the habitat side can be considered acceptable as they improve human safety on the main highway and keep the animals from being hit by vehicles. We conclude that, although sample sizes were limited, the electrified barriers (when voltage was adequate and when not filled with snow) were 100% effective in keeping both large ungulates and large species with paws out of a fenced road corridor and a campground.Item Statewide GNSS-RTN Systems: Current Practices(Scientific Research Publishing, Inc., 2023-01) Raza, Sajid; Al-Kaisy, AhmedThe applications of geospatial technologies and positioning data embrace every sphere of modern-day science and industry. With technological advancement, the demands for highly accurate positioning services in real-time led to the development of the Global Navigation Satellite System—Real-Time Network (GNSS-RTN). While there is numerous published information on the technical aspects of the GNSS-RTN technology, information on the best practices or guidelines in building, operating, and managing the GNSS-RTN networks is lacking in practice. To better understand the current practice in establishing and operating the GNSS-RTN systems, an online questionnaire survey was sent to the GNSS-RTN system owners/operators across the U.S. Additionally, a thorough review of available literature on business models and interviews with representatives of two major manufacturers/vendors of GNSS-RTN products and services were conducted. Study results revealed a great deal of inconsistency in current practices among states in the way the GNSS-RTN systems are built, operated, and managed. Aspects of the diversity in state practices involved the business models for the GNSS-RTN systems besides the technical attributes of the network and system products. The information gathered in this study is important in helping state agencies make informed decisions as they build, expand or manage their own GNSS-RTN systems.Item The Role of GNSS-RTN in Transportation Applications(MDPI AG, 2022-07) Raza, Sajid; Al-Kaisy, Ahmed; Teixeira, Rafael; Meyer, BenjaminThe Global Navigation Satellite System—Real-Time Network (GNSS-RTN) is a satellite-based positioning system using a network of ground receivers (also called continuously operating reference stations (CORSs)) and a central processing center that provides highly accurate location services to the users in real-time over a broader geographic region. Such systems can provide geospatial location data with centimeter-level accuracy anywhere within the network. Geospatial location services are not only used in measuring ground distances and mapping topography; they have also become vital in many other fields such as aerospace, aviation, natural disaster management, and agriculture, to name but a few. The innovative and multi-disciplinary applications of geospatial data drive technological advancement towards precise and accurate location services available in real-time. Although GNSS-RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land surveying, the implications of precise real-time location services would be far-reaching and more critical to many advanced transportation applications. The GNSS-RTN technology is promising in meeting the needs of automation in most advanced transportation applications. This article presents an overview of the GNSS-RTN technology, its current applications in transportation-related fields, and a perspective on the future use of this technology in advanced transportation applications.Item Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals(Western Transportation Institute, 2021-08) Videa, Aldo; Wang, YiyiPassenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning System (GPS) data. The research was conducted as a case study of the Streamline Bus System in Bozeman, Montana. To evaluate the reliability of the data collected with the Raspberry Pi computers, the study conducted technology-based estimation of ridership, OD flows, wait time, and travel time for a comparison with ground truth data (passenger surveys, manual data counts, and bus travel times). This study introduced the use of a wireless Wi-Fi scanning device for transit data collection, called a Smart Station. It combines an innovative set of hardware and software to create a non-intrusive and passive data collection mechanism. Through the field testing and comparison evaluation with ground truth data, the Smart Station produced accurate estimates of ridership, origin-destination characteristics, wait times, and travel times. Ridership data has traditionally been collected through a combination of manual surveys and Automatic Passenger Counter (APC) systems, which can be time-consuming and expensive, with limited capabilities to produce real-time data. The Smart Station shows promise as an accurate and cost-effective alternative. The advantages of using Smart Station over traditional data collection methods include the following: (1) Wireless, automated data collection and retrieval, (2) Real-time observation of passenger behavior, (3) Negligible maintenance after programming and installing the hardware, (4) Low costs of hardware, software, and installation, and (5) Simple and short programming and installation time. If further validated through additional research and development, the device could help transit systems facilitate data collection for route optimization, trip planning tools, and traveler information systems.