Improving Survey Methodology to Monitor Rare Grassland Birds in South Dakota

Breeding Bird Survey (BBS) data for grassland bird species has shown the most rapid population decline of any other bird group. Current roadside survey techniques, however, may fall short of providing accurate numbers of rare grassland bird species such as chestnut-collared longspur (Calcarius ornatus), lark bunting (Calamospiza melanocorys), Sprague’s pipit (Anthus spragueii), and Baird’s sparrow (Ammodramus bairdii). Trends resulting from roadside data for grassland birds are oftentimes determined to be statistically insignificant because many grassland bird species occur on too few routes, occur in low numbers per route, and show high annual fluctuations in number. It is possible roadside surveys may be providing inaccurate assessments of population trends. The objectives of this study were to 1) determine if increasing the number of routes will increase detection, 2) determine if detection of grassland birds is different on paved versus gravel roads, 3) determine if grassland birds are more likely to occur along routes with more grassland cover, 4) determine if there is an interaction between grassland cover and road type on the occurrence of grassland birds, 5) and to determine if the occurrence of grassland birds is greater away from roads. We utilized BBS methodology to conduct roadside surveys and paired on and off-road surveys along new and existing BBS routes over three seasons (2013-15) in western South Dakota. We used analysis of covariance to determine whether grassland birds were significantly affected by road type or percent grassland or differed between on and off-road surveys. The amount of grassland within point counts was the most significant variable effecting the abundance of grassland bird species. Higher percentages of grassland within a point count negated road effects for some species and resulted in an increase in abundance of focal bird species. Further, the inclusion of off-road point counts 800 m from roads increased abundance of two Species of Conservation Concern. Our findings suggest that increasing routes in areas with intact grassland habitat on gravel roads and incorporating off-road surveys at 800 m will allow for increased detection of focal grassland bird species which in turn can better advise conservation programs within South Dakota and beyond

Driver Drowsiness Detection System: An Approach By Machine Learning Application

The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents

Review of current study methods for VRU safety : Appendix 4 –Systematic literature review: Naturalistic driving studies

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: •Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). •Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. •Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village.The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people

Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection

In the domain of traffic safety and road maintenance, precise detection of road damage is crucial for ensuring safe driving and prolonging road durability. However, current methods often fall short due to limited data. Prior attempts have used Generative Adversarial Networks to generate damage with diverse shapes and manually integrate it into appropriate positions. However, the problem has not been well explored and is faced with two challenges. First, they only enrich the location and shape of damage while neglect the diversity of severity levels, and the realism still needs further improvement. Second, they require a significant amount of manual effort. To address these challenges, we propose an innovative approach. In addition to using GAN to generate damage with various shapes, we further employ texture synthesis techniques to extract road textures. These two elements are then mixed with different weights, allowing us to control the severity of the synthesized damage, which are then embedded back into the original images via Poisson blending. Our method ensures both richness of damage severity and a better alignment with the background. To save labor costs, we leverage structural similarity for automated sample selection during embedding. Each augmented data of an original image contains versions with varying severity levels. We implement a straightforward screening strategy to mitigate distribution drift. Experiments are conducted on a public road damage dataset. The proposed method not only eliminates the need for manual labor but also achieves remarkable enhancements, improving the mAP by 4.1% and the F1-score by 4.5%.Comment: 10 pages, 13 figures, 2 Table

Not all surveillance data are created equal—A multi‐method dynamic occupancy approach to determine rabies elimination from wildlife

1. A necessary component of elimination programmes for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programmes. 2. We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modelling of 10 years of raccoon rabies virus (RABV) surveillance data (2006–2015) collected from three states in the eastern United States. We estimated detection probability of RABV cases for each surveillance method (e.g. strange acting reports, roadkill, surveillance‐trapped animals, nuisance animals and public health samples) used by the USDA National Rabies Management Program. 3. Strange acting, found dead and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in fall– winter compared to spring–summer. Found dead animals in fall–winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). 4. Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. 5. Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence