Western Transportation Institute

The 100th anniversary of the first scientific account of roadkill recognizes the growing awareness of roads’ ecological impacts. Since the first scientific record of roadkill in 1925, global road networks have expanded to over 21.6 million km. Roads result in the mortality of millions of animals annually, threatening biodiversity by reducing populations, fragmenting habitats, and raising extinction risks. Although it was not until the early 2000s that road ecology became formally established as a discipline, research has since advanced substantially. A recent global roadkill dataset spanning 2283 species in 54 countries is an example of this advancement, and it highlights the urgency of this issue. As road expansion accelerates, especially into intact habitats, it is essential to align infrastructure development with both ecological sustainability and economic objectives. We need to build better roads that meet human needs while safeguarding biodiversity, promoting conservation, and preventing another century of major ecological harm.

Abstract Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one‐dimensional (1D) residual denoising network (R1DNet) to achieve the noise removal of 3D pavement texture data. R1DNet is proposed as a 1D architectural encoder–decoder that considers the unique characteristics of 3D texture data from 3D laser imaging technology. The encoder extracts diverse profile features of input noisy texture data through two favorably developed 1D modular structures: a cascade deep convolutional module and a parallel multi‐scale attention module. The decoder gradually parses the extracted profile features and estimates noise, with which the clean texture data are obtained based on a simple subtraction operation. The architecture of R1DNet is determined to be optimal in both accuracy and efficiency, using a customized performance‐balancing evaluation function. For model development in a supervised manner, a systematic labeling method is specifically developed, which can build the baseline clean texture data from real 0.1 mm noisy 3D texture data. The experimental results show that the proposed R1DNet can effectively eliminate noise and produce clean texture data closely matching the baseline, presenting significant improvements in accuracy and consistency, compared to the traditional denoising methods.

The US Highway 191 (US-191)/Montana Highway 64 (MT-64) Wildlife & Transportation Assessment (the “Assessment”) improves understanding of the issues affecting driver safety, wildlife mortality, and wildlife movement along the major routes that connect Yellowstone National Park, the Custer Gallatin National Forest, and other public lands to the growing population centers of Bozeman, Big Sky, and nearby communities in Southwest Montana. By engaging personnel from multiple federal, state, and local agencies along with key stakeholders to examine problems and possibilities through the lens of spatial ecology, the US-191/MT-64 Wildlife & Transportation Assessment brings new insight into the impact of two major roads that unite local communities yet divide the landscape and natural habitats. The information included in this report should inform and support area communities and agency decision-makers to select and pursue wildlife accommodation options. With the passage of the Infrastructure Investment and Jobs Act of 2021, significant funds for wildlife accommodation measures are available nationwide on a competitive basis. The US-191/MT-64 Wildlife & Transportation Assessment better equips part of Southwest Montana’s gateway to Yellowstone National Park to take advantage of new funding opportunities.

The goal for this manual is to provide practical information for the implementation of mitigation measures that aim to: 1. Improve human safety through reducing collisions with large animals, including large wild mammal species, select free roaming large feral species, and select free roaming large livestock species, and 2. Improve or maintain habitat connectivity for terrestrial wildlife species and selected feral species through safe crossing opportunities. This manual does not include all possible measures that can or may reduce animal-vehicle collisions and maintain or improve habitat connectivity for wildlife. The measures included in this manual are: Barriers (fences) in combination with crossing structures (for large wild mammals and for small wild animal species), roadside animal detection system, Barriers (fences), Barriers (fences) in combination with crossing structures (for free roaming livestock), and culling, relocation, anti-fertility treatment, roadside animal detection systems, barriers (fences), and barriers (fences) in combination with crossing structures (for large feral mammal species such as feral horses and burros).

Geospatial location service is not only used in measuring ground distances and mapping topography, but has 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 toward precise and accurate location services available in real-time. Although the RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land survey, the implications of precise real-time location services would be far-reaching and critical to many advanced transportation applications. The GNSS real-time network (RTN) technology, introduced in the mid-1990s, is promising in meeting the needs of automation in most of the 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.

Abstract Roads obstruct wildlife movements, and wildlife‐vehicle collisions are a hazard to both animals and humans. Wildlife and transportation managers often consider reducing the speed limit to reduce wildlife‐vehicle collisions, but there is little empirical data to support or refute this measure. We experimentally reduced the nighttime speed limit from 70 to 55 mph on six stretches of highway that cross mule deer (Odocoileus hemionus) winter range or migration paths. Drivers consistently reduced their speeds, but only by 3–5 mi/h. Reduced speed limit did not make it any easier for deer to cross the road, indicating no benefit for habitat connectivity. At winter sites, the number of deer‐vehicle collisions was not affected by the reduced speed limit whereas at migration sites, collisions were modestly lower under the reduced speed limit. Given the small reduction in vehicle speeds, it is not surprising that there was little benefit of reduced speed limit for deer or people. We conclude that reduced nighttime speed limit is not an effective way to reduce wildlife‐vehicle collisions or make roads more permeable to wildlife due to poor compliance from motorists.

The negative effects of roads on wildlife populations are a growing concern. Movement corridors and road-kill data are typically used to prioritize road segments for mitigation measures. Some research suggests that locations where animals move across roads following corridors coincide with locations where they are often killed by vehicles. Other research indicates that corridors and road-kill rarely occur in the same locations. We compared movement corridor and road mortality models as means of prioritizing road segments for mitigation for five species of felids in Brazil: tiger cats (Leopardus tigrinus and Leopardus guttulus were analyzed together), ocelot (Leopardus pardalis), jaguarundi (Herpailurus yagouaroundi), and puma (Puma concolor). We used occurrence data for each species and applied circuit theory to identify potential movement corridors crossed by roads. We used road-kill records for each species and applied maximum entropy to determine where mortality was most likely to occur on roads. Our findings suggest that movement corridors and high road mortality are not spatially associated. We suggest that differences in the behavioral state of the individuals in the species occurrence and road-kill data may explain these results. We recommend that the road segments for which the results from the two methods agree (~5300 km for all studied species combined at 95th percentile) should be high-priority candidates for mitigation together with road segments identified by at least one method in areas where felids occur in low population densities or are threatened by isolation effects.