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As human populations continue to grow and encroach into wildlife habitats, instances of human-wildlife conflict are on the rise. Increasing numbers of reported wildlife-vehicle collisions (WVCs) provide tangible evidence of anthropogenic impacts on wildlife as well as increasing threats to human health and safety. Increasing WVCs are of particular concern, especially those involving large-bodied ungulates such as moose (Alces spp.), because of the increased risk of property damage, personal injuries, and human fatalities. Motorists directly involved in a WVC are at risk of injury or mortality, but other motorists are also put at risk due to road obstructions and traffic congestion associated with WVCs. Mitigating these impacts on motorists and wildlife requires investigation into the temporal and spatial factors leading to WVCs. In Alaska, most WVCs involve moose (Alces alces), a large bodied ungulate capable of threatening human life when involved in a collision. Each moose-vehicle collision (MVC) in Alaska is estimated to cost $33,000 in damages. With this analysis, I analyzed the plethora of factors contributing to moose and motorist occurrence on the road system and motorist detection based on a historical dataset of MVC reports throughout Alaska from 2000 to 2012 and a dataset of field-derived measurements at MVC locations within the Matanuska-Susitna Borough from 2016 to 2018. My first analysis focused on the daily and annual trends in MVC rates as compared to expected moose and human behavioral patterns with a focus on guiding mitigation strategies. Fifty percent of the MVCs reported between 2000 and 2012 occurred where the commuter rush hours overlapped with dusk and dawn in winter, and the artificial lighting differences between boroughs suggest a link between artificial lighting and reduced MVCs. To focus more specifically on roadside features contributing to MVC risk, I collected and analyzed local and regional scale land cover and road geometry data at reported MVC sites in an area with a rapidly growing human population. I compared these data to similar data collected at random locations near documented MVC sites and at locations where moose that were fitted with global-positioning system (GPS) transmitters crossed highways. I used generalized additive mixed models to delineate which of the variables impacted the risk of both moose road crossings and MVCs. Moose road crossings were influenced by approximations of spatial, seasonal, and daily moose density as well as the proportion of deciduous-coniferous and coniferous forest in the area and the number of possible corridor or land cover types surrounding the site. The best MVC risk model was described by expected seasonal and daily changes in moose density and local scale measurements, including the sinuosity of the road, the height of vegetation near the road, and the angle between the road surface and the roadside. Together this information should guide transportation and urban planners in the Matanuska-Susitna Borough to use roadside vegetation removal, seasonal speed reduction, improved lighting strategies, dynamic signage, or partnerships with mobile mapping services to reactively reduce MVCs and to focus future road planning in areas with lower moose abundance and build roads that increase visibility and detection distances in areas where moose are common.

This study was conducted to investigate and model the high traffic collision frequencies in the City of Edmonton, Canada. Consistent collision spikes were observed on Fridays compared to the other days of the week. The first Negative Binomial model was formulated to establish a relation between the collision frequency and the independent variables. The second Multinomial logistic regression model was formulated to examine the probability of age categories and gender involved in collision for each day of week considering collision has happened. The proposed collision prediction models were found good. They could provide a realistic estimate of expected collision frequency and properties of collision for a particular day as a function of number of hours of daylight, number of hours of snowfall, visibility, age and gender. It is hoped that predicted collision frequency will help the decision maker to quantify traffic safety of Edmonton and improve the scenario. / Transportation Engineering

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