#psucivilandenvironmentalengineering

Climate extremes, in particular droughts, are significant driving forces towards riverine ecosystem disturbance. Drought impacts on stream ecosystems include losses that can either be direct (e.g. destruction of habitat for aquatic species) or indirect (e.g. deterioration of water quality, soil quality, and increased chance of wildfires). This paper combines hydrologic drought and water quality changes during droughts and represents a multi‐stage framework to detect and characterize hydrological droughts while considering water quality parameters. This method is applied to 52 streamflow stations in the state of California, USA over the study period of 1950‐2010. The framework is assessed and validated based on two drought events declared by the state in 2002 and 2008. Results show there are two opposite drought propagation patterns in northern and southern California. In general, northern California indicates more frequent droughts with shorter time to recover. Chronology of drought shows that stations located in southern California have not followed a specific pattern but they experienced longer drought episodes with prolonged drought recovery. When considering water quality, results show that droughts either deteriorate or enhance water systems, depending on the parameter of interest. Undesirable changes (e.g., increased temperature and decreased dissolved oxygen) are observed during droughts. In contrast, decreased turbidity is detected in rivers during drought episodes, which is desirable in water systems. Nevertheless, water quality deteriorates during drought recovery, even after drought termination. Depending on climatic and streamflow characteristics of the watersheds, it was found that it would take nearly 2 months on average for water quality to recover after drought termination.

Past research efforts have shown that cyclists’ safety, stress, and comfort levels greatly affect the routes chosen by cyclists and cycling frequency. Some researchers have tried to categorize cyclists’ levels of traffic stress utilizing data that can be directly measured in the field, such as the number of motorized travel lanes, motorized vehicle travel speeds, and type of bicycle infrastructure. This research effort presents a novel approach: real-world, on-road measurements of physiological stress as cyclists travel across different types of bicycle facilities at peak and off-peak traffic times. By matching videos with stressful events, it was possible to observe the circumstances of those stressful events. The stress data was normalized, and the method was carefully validated by a detailed analysis of the stress measurements. Novel statistical results from a multi-subject study quantifies the impact of traffic conditions, intersections, and bicycle facilities on average stress levels.

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from the abstracts of selected patents. After applying three classes of RA (variable-based analysis without and with loops and state-based analysis), nine specific IV states of a predicting model are extracted. These states involve the four keywords of “chamber”, “hous”, “main”, and “return”. Lastly, the abstracts of the patents are examined to identify the technical subjects relevant to smart building technologies for which these keywords are proxies.

Across the United States, jurisdictions are investing more in bicycle and pedestrian infrastructure, which can benefit from nonmotorized traffic volume data. The design of nonmotorized counting programs varies. Whereas some agencies use automated counters to collect continuous and short duration counts, the most common type of bicycle and pedestrian counting is manual counting either in the field or from video. The objective of this research is to identify the optimal times of day to conduct manual counts for the purposes of accurately estimating annual average daily nonmotorized traffic (AADNT). This study used continuous bicycle and pedestrian counts from six U.S. cities to estimate AADNT and analyze estimation errors for multiple short duration count scenarios. Using two permanent counters per factor group reduces error substantially (> 50%); afternoon counts seem to be best for reducing error (2:00 to 6:00 p.m.). Error on Sunday is often as good as, if not better than, Saturday, contrary to what others have found. Arlington has the lowest AADNT estimation error (mean absolute percentage error), probably because of better data quality and higher nonmotorized traffic volumes, and Mount Vernon, Washington has the highest. Average AADNT estimation errors for the studied short duration count scenarios range from 30% to 50%. Error is lower for the commute factor group, bicycle-only counts, scenarios in which more peak hours are counted, and when more than one permanent counter is available to estimate adjustment factors.

The recent availability of high-frequency transit (HFT) GPS data for buses has allowed the estimation of detailed bus-travel speed profiles between bus stops. HFT data are defined as data comprising GPS vehicle trajectory recorded at or less than 5 s intervals. With HFT data it is now possible to measure changes in bus speed at specific locations of interest, such as intersections, ramps, crosswalks, and so on. Previous research efforts have not compared GPS-based bus speeds with general traffic speeds at specific locations between stops. This research fills this knowledge gap, utilizing accurate stationary radar speed data as a baseline or ground truth data to estimate GPS-based bus speed accuracy. A thorough data analysis of the bus and traffic speed data indicates that HFT speed estimations between stops are accurate and highly correlated with traffic speeds. Time–space speed profiles and regression analysis are utilized to quantify factors that affect HFT speed estimation accuracy. The relative advantages and limitations of the HFT data are presented and discussed. This study concludes that large HFT datasets can be utilized to accurately monitor speeds between transit stops. HFT datasets are suitable to cost-effectively monitor recurrent arterial speed performance for both passenger and transit vehicles.

Pile foundations support structures built on a layer of weak surface soil or those with very heavy, concentrated loads. Using simulated earthquake data generated by Oregon State University researchers at UC Davis’s Center for Geotechnical Modeling and computer modeling, Khosravifar will assess the effects of the combined force structural loads exert and the lateral spreading associated with soil liquefaction on pile foundations. Khosravifar’s research will fill critical knowledge gaps in our understanding of how piles react to prolonged, high-intensity seismic events like those the Pacific Northwest could experience during a Cascadia Subduction Zone earthquake. Results from the research may also inform policymakers tasked with updating design and building codes for ports, wharves, bridges, and other structures.

This research models urban freight distribution services lifecycle CO2e emissions. A lifecycle emissions minimization model for the fleet size and composition problem is presented and applied to a real-world case study. The model explicitly incorporates parking and idling emissions which are significant in multi-stop urban distribution routes. Lifecycle emission elasticities as well as the impact of logistics constraints such as route duration and vehicle cargo capacity are estimated and analyzed. Policy implications and tradeoffs between electric tricycles and conventional diesel vans are discussed.

Abstract: This research aimed to develop an understanding of the safety and operational implications of using the flashing yellow arrow (FYA) in permitted and protected/permitted right turn (PPRT) operations to maximize safety and efficiency. This report includes a review of more than 50 scientific and technical articles on the selection of right turn phasing alternatives and a review of Oregon crash data at intersections with exclusive right-turning lanes from 2011-2013. The study had three phases: 1) a web-based survey, 2) a microsimulation model, and 3) and a driving simulator study. The survey measured Oregon driver’s comprehension of right-turn signal display. Analysis revealed a general misunderstanding of the required driver response for the steady red arrow signal indication, but comprehension of the FYA for right turns was high. The microsimulation model of several PPRT phasing alternatives indicated that the pedestrian volumes had the greatest effects on delays. The driving simulator experiment indicated that driver responses were relatively consistent with those observed in the web-based survey. The results suggest that Oregon transportation agencies might improve driver-yielding behavior and pedestrian safety at signalized intersections with high volumes of permissive right turns from exclusive right-turn lanes by using the FYA display in lieu of a steady circular green display.