Matching Items (4)
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- All Subjects: Hydrologic sciences
- Creators: Chester, Mikhail
Description
Bicyclist and pedestrian safety is a growing concern in San Francisco, CA,
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
ContributorsMcIntyre, Andrew (Author) / Salon, Deborah (Thesis advisor) / Kuby, Mike (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cities are, at once, a habitat for humans, a center of economic production, a direct consumer of natural resources in the local environment, and an indirect consumer of natural resources at regional, national, and global scales. These processes do not take place in isolation: rather they are nested within complex coupled natural-human (CNH) systems that have nearby and distant teleconnections. Infrastructure systems—roads, electrical grids, pipelines, damns, and aqueducts, to name a few—have been built to convey and store these resources from their point of origin to their point of consumption. Traditional hard infrastructure systems are complemented by soft infrastructure, such as governance, legal, economic, and social systems, which rely upon the conveyance of information and currency rather than a physical commodity, creating teleconnections that link multiple CNH systems. The underlying structure of these systems allows for the creation of novel network methodologies to study the interdependencies, feedbacks, and timescales between direct and indirect resource consumers and producers; to identify potential vulnerabilities within the system; and to model the configuration of ideal system states. Direct and indirect water consumption provides an ideal indicator for such study because water risk is highly location-based in terms of geography, climate, economics, and cultural norms and is manifest at multiple geographic scales. Taken together, the CNH formed by economic trade and indirect water exchange networks create hydro-economic networks. Given the importance of hydro-economic networks for human well-being and economic production, this dissertation answers the overarching research question: What information do we gain from analyzing virtual water trade at the systems level rather than the component city level? Three studies are presented with case studies pertaining to the State of Arizona. The first derives a robust methodology to disaggregate indirect water flows to subcounty geographies. The second creates city-level metrics of hydro-economic vulnerability and functional diversity. The third analyzes the physical, legal, and economic allocation of a shared river basin to identify vulnerable nodes in river basin hydro-economic networks. This dissertation contributes to the literature through the creation of novel metrics to measure hydro-economic network properties and to generate insight into potential US hydro-economic shocks.
ContributorsRushforth, Richard Ray (Author) / Ruddell, Benajmin L (Thesis advisor) / Allenby, Braden (Committee member) / Chester, Mikhail (Committee member) / Seager, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Weather radars provide quantitative precipitation estimates (QPEs) with seamless spatial coverage that can complement limitations of sparse rain gage measurements, including those affecting intensity-duration-frequency (IDF) relations used for infrastructure design. The goal of this M.S. thesis is to assess the ability of 4-km, 1-h QPEs from the Stage IV analysis of the Next-Generation Radar (NEXRAD) network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA, using a dense network of 257 rain gages as reference. The generalized extreme value (GEV) distribution is used to model the frequency of annual P maximum series observed at gages and radar pixels for durations, d, from 1 to 24 h. Estimates of P quantiles from radar QPEs are negatively biased (-20% – -30%) for d = 1 h. The bias tends to 0 and errors are small for d ≥ 6 h, independently of the return period. The presence of scaling for the GEV location and scale parameters, needed to apply IDF scaling models, was found for both radar and gage products. Regional frequency analysis methods combined with bias correction of the GEV shape parameter allow reducing the statistical uncertainty and providing seamless spatial distribution of P quantiles at daily and subdaily durations that address limitations of current IDF relations in southwestern U.S. based on NOAA Atlas 14.
ContributorsSrivastava, Nehal Ansh (Author) / Mascaro, Giuseppe (Thesis advisor) / Chester, Mikhail (Committee member) / Garcia, Margaret (Committee member) / Papalexiou, Simon Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Water-Energy Nexus (WEN) is a concept that recognizes the interdependence of water and energy systems. The Phoenix metropolitan region (PMA) in Arizona has significant and potentially vulnerable WEN interactions. Future projections indicate that the population will increase and, with it, energy needs, while changes in future water demand are more uncertain. Climate change will also likely cause a reduction in surface water supply sources. Under these constraints, the expansion of renewable energy technology has the potential to benefit both water and energy systems and increase environmental sustainability by meeting future energy demands while lowering water use and CO2 emissions. However, the WEN synergies generated by renewables have not yet been thoroughly quantified, nor have the related costs been studied and compared to alternative options.Quantifying WEN intercations using numerical models is key to assessing renewable energy synergy. Despite recent advances, WEN models are still in their infancy, and research is needed to improve their accuracy and identify their limitations. Here, I highlight three research needs. First, most modeling efforts have been conducted for large-scale domains (e.g., states), while smaller scales, like metropolitan regions, have received less attention. Second, impacts of adopting different temporal (e.g., monthly, annual) and spatial (network granularity) resolutions on simulation accuracy have not been quantified. Third, the importance of simulating feedbacks between water and energy components has not been analyzed.
This dissertation fills these major research gaps by focusing on long-term water allocations and energy dispatch in the metropolitan region of Phoenix. An energy model is developed using the Low Emissions Analysis Platform (LEAP) platform and is subsequently coupled with a water management model based on the Water Evaluation and Planning (WEAP) platform. Analyses are conducted to quantify (1) the value of adopting coupled models instead of single models that are externally coupled, and (2) the accuracy of simulations based on different temporal resolutions of supply and demand and spatial granularity of the water and energy networks. The WEAP-LEAP integrated model is then employed under future climate scenarios to quantify the potential of renewable energy technologies to develop synergies between the PMA's water and energy systems.
ContributorsMounir, Adil (Author) / Mascaro, Giuseppe (Thesis advisor) / White, Dave (Committee member) / Garcia, Margaret (Committee member) / Xu, Tianfang (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2022