Safety analysis of off-highway vehicles use within public rights-of-way in Alaska
KeywordAll terrain vehicle driving
Off-road vehicle trails
All terrain vehicle trails
All terrain vehicles
MetadataShow full item record
AbstractMany Alaskans own and operate off-highway vehicles (OHVs) for recreational purposes or for use as primary and secondary modes of transportation. However, the reported crash rate shows that 80 on-road crashes, resulting in five fatalities, occur each year statewide. As a result, the use of OHVs has been identified as a safety concern in the Alaska State Highway Safety Improvement Plan. Minimal research dedicated to understanding OHV use in Alaska exists which limits the potential for improvements that address safety concerns in an equitable fashion. The research presented here contributes to better understanding of on-road OHV use through observational and retrospective analysis. Field-based observations were conducted within highway rights-of-way in 14 strategic locations across Alaska to quantify OHV use and the risk-taking behaviors of riding without helmets, passengers riding without a designated seat, and riding unlawfully on the road. Additional risk factors from the field observations and Alaska Department of Motor Vehicles (DMV) crash data for the period from 2000 through 2016 were identified using the Chi-Square test for independence. Spatial analysis of the Alaska DMV crash data for the period from 2009 through 2016 tested for complete spatial randomness of crashes and identified clusters of crashes by frequency and severity using neighborhood point density statistics. Frequent OHV use was observed with daily traffic volumes exceeding 40 vehicles per day in three field study locations. Several risk-tolerant behaviors were observed including users riding without helmets and vehicles carrying passengers without a designated seat an average of 70 and 20 percent of the time, respectively. Additionally, over half the OHV users were observed to be riding unlawfully using the road. Risk-tolerant behaviors were most frequently observed in communities where on-road use is legal and happened to be coincident with the highest on-road use rates. Overrepresented risk factors for high crash severity incidents included riding at night, in summer, on unpaved roads, on local roads or collectors, in rural areas, for single-vehicle crashes with the occupant not using safety equipment and riding under the influence of alcohol. Crashes were observed to be clustered around towns with the highest frequencies occurring near town centers. The prevalence of risk-tolerant riding behaviors and unlawful on-road riding indicates the need for improvements to existing laws and the education and enforcement thereof. Changes must address the unique needs of users while also considering local jurisdiction such that safety can be improved while also maintain transport equity for residents of rural and isolated communities in Alaska.
DescriptionThesis (M.S.) University of Alaska Fairbanks, 2020
Showing items related by title, author, creator and subject.
UAF's light-duty vehicle fleet lifecyle, maintenance costs and composition: ordinary least square regression and panel data analysisHix, Edward R.; Wright, Christopher; Baek, Jungho; Little, Joe; Goering, Greg; Platt, Nathan (2020-08)The University of Alaska Fairbanks maintains a vehicle fleet for use by its staff, faculty, and students. Given the multifaceted needs of the campus and the impact that the harsh subarctic climate can have on vehicles, management of the fleet to meet the needs of its users is a complex task. One method UAF uses to manage the cost of the fleet is to extract the depreciation expense from each fleet vehicle into a non-interest bearing recharge account to eventually purchase its replacement. While several reviews have been conducted regarding the management of this fleet, a gap in research involves analysis of cost of this fleet over its lifecycle. This study examined the effects of fleet vehicle lifecycle extension beyond the predetermined 10-year useful life at UAF. Three novel datasets were created from UAF Facilities Services' archival maintenance work order data: a vehicle dataset, work order dataset, and a panel dataset. Ordinary least squares regression methods were used to examine the impact of model year on a vehicle's nominal purchase price and the impact of vehicle specification on real purchase price. Fixed and random effects panel methods were used to examine the impact of vehicle specification and vehicle age on maintenance costs. The effects of extending the fleet lifecycle from ten to twenty-years on maintenance and operational cost were estimated. Population dynamics models estimated the impact of the ten year lifecycle extension on the replacement fund. The results of this study suggested increasing vehicle lifecycles by ten years increased operating, maintenance, and replacement costs and effectively reduced the replacement fund purchasing power. The extension of vehicle lifecycles resulted in continually increasing rental rates and ultimately to the insolvency of the replacement fund.
Property Crime Reported in Alaska, 1986–2015Parker, Khristy (Alaska Justice Statistical Analysis Center, Justice Center, University of Alaska Anchorage, 2017-02-06)This fact sheet presents data on property crime in Alaska from 1986 to 2015 as reported in the Alaska Department of Public Safety publication Crime in Alaska. "Property crime" is an aggregate category that includes burglary, larceny-theft, and motor vehicle theft crimes. From 1986 to 2015 the property crime rate in Alaska decreased as the overall crime rate decreased. On average, property crime accounted for two-thirds of all crime in Alaska over the thirty-year period.
Near-roadway air pollution: evaluation of fine particulate matter (PM₂.₅) and ultrafine particulate matter (PM ₀.₁) in Interior AlaskaKadir, Abdul; Aggarwal, Srijan; Belz, Nathan; Barnes, David; Mao, Jingqiu (2019-05)Particulate air pollution in the form of fine (PM₂.₅) and ultrafine (PM₀.₁) particles has become a global concern, especially in urban areas with high population and vehicular traffic. Considerable research has been carried out to understand the underlying processes that impact particulate pollution, but most studies have been conducted in warmer and urban regions such as in California. The Fairbanks North Star Borough (FNSB), in Interior Alaska, provides an interesting example of a relatively small- to mid-sized northern locality (population ~100,000) with persistent air quality issues and extremely cold climatic conditions for a major part of the year. Since December 2009, the FNSB has been designated a nonattainment region by the U.S. Environmental Protection Agency for the federal PM₂.₅ standard. As part of their remediation efforts, the borough and state have undertaken increased monitoring by using an on-roadway monitoring vehicle (sniffer vehicle) and stationary near-roadway sites for air quality measurements, beyond what is required for regulatory compliance. One of the goals of this project was to develop a novel data investigation and analyses methodology for the geospatial air quality data collected by the borough's mobile monitoring vehicle (years 2012-15), to shed light on the PM₂.₅ issues faced by the FNSB. In addition, this research also undertook measurements of ultrafine particle (UFP) concentration levels at four road weather information system (RWIS) sites in the FNSB region. UFPs, though unregulated, are considered to have significant human health impacts and no known studies have investigated UFPs in FNSB. In addition to UFPs, other parameters such as PM₂.₅, traffic, and weather data were measured at the same locations to investigate any underlying trends/correlations with UFPs. In the first part of the research with mobile monitoring, data were categorized in nine different groups based on their mean and standard deviation values to determine the spatiotemporal distribution of PM₂.₅. This novel way of grouping data allows identification of locations with consistently poor and consistently better air quality, by going beyond the simple analyses of means and accounting for variability and standard deviation in the data. In addition to hotspot identification, analyses found that average on-roadway PM₂.₅ concentrations were higher in North Pole (27.2 μg/m³) than in Fairbanks (12.9 μg/m³), and that average concentrations were higher in the background stationary monitoring data (29.4 μg/m³) than in the mobile monitoring data (20.0 μg/m³) for the study period. Not surprisingly, significant negative correlations (R² = 0.49 for Fairbanks, and R² = 0.31 for North Pole) were found between temperature and PM₂.₅. Temporal distribution of the data suggests that PM₂.₅ levels increase gradually in the months of October and November, peak during the months of December, January, and February, and quickly plummet beginning March. In the latter part of the study, data on UFP measurements were collected at four RWIS sites in the FNSB for four days between March 1 and 18, 2017, for five continuous hours each day. Among other parameters, PM₂.₅ concentrations, temperature, relative humidity, wind speed, and traffic volume data were collected. Data were analyzed to develop correlations between UFPs and other parameters, to compare data from this study with other studies, and to determine current roadside UFP concentration levels in interior Alaska. Fairbanks roadside locations showed higher mean UFP counts (41,700 particles/cm³) than the North Pole (22,100 particles/cm³) locations. Similarly, for the period of study, Fairbanks roadside locations showed higher PM₂.₅ concentrations and traffic counts (6.3 μg/m³; 15 vehicles/min) than the North Pole (4.6 μg/m³; 10 vehicles/min) locations, both being well below the on-roadway and background PM₂.₅ concentrations estimated in the first part of this report. Multilinear predictive models were developed for estimation of UFPs and PM₂.₅ based on weather and traffic parameters. This first study of UFPs in Alaska improves our understanding of near-roadway UFPs in cold regions.