Bio-Based Renewable Additives for Sustainable Roadway Snow and Ice Control Operations

The objective of this project is to develop innovative anti-icing and pre-wettting formulations for snow and ice control on roadways, using locally-sourced agricultural wastes, fruit by-products and other bio-based additives for freezingpoint suppression, performance enhancement, and infrastructure preservation

The Perception of Autonomous Driving in Rural Communities

69A3551747129Autonomous, or self-driving, vehicles have the capability to either fully or partially replace a human driver in the navigation to a destination. To better understand how receptive society will be to these types of vehicles, this study focused on the perceived level of trust in autonomous vehicles (AVs) by rural drivers and passengers. An online survey that examined the behavioral and value-based perspectives of drivers was developed and distributed to respondents across the United States, and a total of 1,247 valid responses were collected and analyzed. Based on the results, rural (and non-rural) respondents had similar levels of trust when comparing self-driving vehicles with human-driven vehicles, though older people and those with less education tended to have less trust in self-driving vehicles. The outcomes from this study can be used to support targeted outreach efforts for those drivers who remain skeptical about the overall safety benefits of this evolving transportation technology area

Evaluating Management Options to Increase Roadside Carbon Sequestration

DTRT13-G-UTC49We estimated the amount of carbon sequestered along Montana Department of Transportation (MDT) roads and tested 3 different highway right-of-way (ROW) management techniques to increase carbon stocks. Using Geographic Information System techniques, the total ROW acreage owned by MDT was found to sequester 75,292 metric tons of carbon per year and to consist mostly of grasslands (70%). From 2016-2018 we tested 3 ROW management techniques to increase carbon stocks- increase mowing height, plant woody shrubs, or add legumes to reclamation seed mixes of disturbed soils - at 3 sites (Three Forks [3F], Bear Canyon [BC], and Bozeman Pass [BP]) along Interstate 90 in southwestern Montana. Soil samples generally averaged 0.75\u20131.5% soil organic carbon (SOC) at the 3F site, 2.5\u20134% SOC at the BC site, and 1.5\u20132.5% SOC at the BP site. Average SOC levels were always lower in 2018 than in 2016. Soil respiration rates were generally highest in June or July at the BC site, averaging ~4 \u3bcmol CO2 m-2 second-1. Soil respiration rates were lower at the BC site in November 2016, at the BP site in June 2018, and at the 3F site in July 2018 (all ~2\u20133 \u3bcmol CO2 m-2 s-1). Aboveground biomass carbon estimates generally mirrored belowground SOC estimates. Taken together, our findings suggest that of the three treatments implemented (raised mowing height, shrub planting, and disturbance), minimizing disturbance to soils likely makes the greatest contribution to the medium- and long-term carbon-storage potential of these roadside soils

Sizing Hydraulic Structures in Cold Regions to Balance Fish Passage, Stream Function, and Operation and Maintenance Cost

The purpose of this research was to evaluate how characteristics of hydraulic structures, such as slope or size, used at crossings over waterways relate to operation and maintenance (O&M) effort, fish passage, and stream function. Data on O&M concerns, fish passage concerns, and crossing characteristics were collected from 45 road-stream crossings in Prudhoe Bay, Alaska, during lower and higher water periods in both 2014 and 2015 (four events total). Logistic regression and generalized mixed models were used to examine relationships between O&M effort (response) and five explanatory variables. For all data from all years combined, there were no observable associations among O&M and culvert type or constriction ratio. However, lower constriction ratios were observed for sites with O&M needs in the June 2014 data set. The proportion of sites with both fish passage and O&M concerns was 0.52; comparatively, the proportion of sites with no fish passage concern but with O&M concern was 0.35

Near-Roadway Air Pollution: Evaluation of Fine Particulate Matter (PM2.5) and Ultrafine Particulate Matter (PM0.1) in Interior Alaska

This report presents a study of fine (PM2.5) and ultrafine (PM0.1) particles in the Fairbanks North Star Borough (FNSB) in Interior Alaska, with specific emphasis on the relationship of ultrafine particles (UFPs) to vehicular traffic. Chapter 1 provides a summary of published literature on particulates in air from vehicular emissions. Chapter 2 provides a novel and robust GIS-based data analysis approach to PM2.5 data collected by the FNSB. This analysis approach is convenient for identifying hotspots, as well as locations where PM2.5 changes either abruptly or continuously or does not change at all. The results reveal that average on-roadway PM2.5 concentrations are higher in North Pole than in Fairbanks, and mean levels are higher in stationary background monitoring data than in mobile monitoring on-roadway data. Not surprisingly, significant negative correlations were found between temperature and PM2.5. Chapter 3 presents the results from the data collection campaign to measure UFPs at roadside locations in Fairbanks and North Pole and investigate the relationship of UFPs with traffic and meteorological parameters. Multilinear predictive models were developed for estimation of UFPs and PM2.5 based on weather and traffic parameters. Overall, this study improves our understanding of on- and near-roadway particulates in a cold-climate region

A Targeted Approach to High-Volume Fly Ash Concrete Pavement (Phase I Report)

Unlike the conventional method of admixing nanomaterials directly in fresh concrete mixture, a more targeted approach was explored. Specifically, nanomaterials were used to improve the interface between coarse aggregate and cement paste, by coating the coarse aggregate with cement paste that contained graphene oxide or nanosilica. Using coated coarse aggregates, the mechanical and transport properties of high-volume fly ash (HVFA) concrete were tested to evaluate the effect of nanomaterial coating on the interface transition zone of concrete. The compressive and splitting strengths of HVFA concrete at 3, 7, 14, and 28 days and the water sorptivity and chloride migration coefficient at 28 days were studied. Results show that nanomaterial-coated coarse aggregate can improve the transport properties of HVFA concrete by reducing permeability. However, no improvement was seen in the compressive and splitting strengths when incorporating coated coarse aggregate, compared with direct mixing of nanomaterials in fresh concrete. Resistance to freezing/thawing cycles and scanning electron microscope/energy dispersive X-ray spectroscopy of concrete samples were also investigated to obtain a more comprehensive and mechanistic understanding of nanomaterial coating

Assessing the Transportation Adaptation Options to Sea Level Rise for Safety Enhancement in RITI Communities through a Structured Decision-Making Framework

Through a structured decision-making framework, this study aims to better understand the key factors influencing transportation adaptation planning in practice. Qualitative, semi-structured, in-depth interviews with various stakeholders were conducted to identify the main concerns, challenges, objectives, tradeoffs, and evaluation variables in transportation adaptation planning. Stakeholders were identified through preliminary interviews with transportation planning experts from the metropolitan planning organization using typical case and snowball sampling methods. Key aspects related to the major concerns, objectives, priorities, adaptation plan evaluations, implementation challenges, and potential conflicts and tradeoffs are identified. Major barriers to adaptation plan development and implementation include lack of resources, competing with more urgent needs, conflicts with other planning objectives, lack of holistic view, working in silos, mismatched and outdated information, uncertainty in future scenarios, and action inertia. To overcome these challenges, we propose 1) more efforts to understand community values, develop strategic goals, and identify their priorities in order to balance the tradeoffs 2) collaboration with other sectors to develop a holistic view of resilience and strategic plans that achieve multiple planning goals 3) collaborate with diverse stakeholders to reduce spatial and temporal information mismatches and to create adaptive plans that can accommodate multiple scenarios with uncertainty 4) conduct community outreach and stakeholder engagement from the beginning to build support, consolidate resources, and eliminate social inertia for plan implementation

Drone-Based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways