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Successful navigation through our environment depends on selecting and controlling the appropriate course of action. According to ecological psychology, decisions on when and how we act is achieved through the detection of information specifying affordances; the fit between an actor’s action possibilities and their environment. As a result, errors in decision-making may derive from an unanticipated actualization of what the environment affords. While recent research has aimed to understand how changes in physical limitations of individuals influence their perception of what the environment affords them, little research has investigated how changes during life span influence sensitivity to specifying information, and how these may impact upon action decisions. In a series of interlinking studies using virtual traffic environments, different age groups were investigated in their ability to: tune into action-relevant information in the optical array, use such information to guide both the choice of behaviour and the continuous guidance of action, and re-educate attention to specifying or non-specifying action information. Findings support the general assumption that ageing impacts the ability to tune into information which specifies whether a gap affords crossing or not and this extends beyond action selection to influence how an affordance is actualized. Together, these studies provide evidence of age deterioration in the use of perceptual-based information that guides decisions about when and how to act. This suggests that to help prevent unintended injury, intervention strategies for older adults should focus on re-learning how to tune into and use information that specifies affordances in hazardous environments.

Roads and their traffic are known to affect bird species at both individual and population levels. Collisions with vehicles can cause direct mortality, and noise, light and chemical pollution can have sub-lethal impacts. Additionally, roads can cause habitat degradation, fragmentation and edge effects. However, thus far, the published literature on this topic comprises only relatively small-scale studies and our understanding of the impacts of roads on bird populations at landscape or national scales is limited. I use bird count and road data from across Great Britain to assess the spatial associations between the density and traffic volumes of roads, and bird populations in the surrounding areas. In Chapter 1, I provide background detail on the impacts of roads on birds and introduce the premise and necessity of my thesis. In Chapter 2, I quantify changes in the detectability of birds in field surveys in relation to road exposure. I find that, while some species are significantly harder to detect in areas of higher road exposure, others are easier. I therefore suggest that, in analyses of bird populations around roads, where possible, variation in detectability with exposure to roads should be accounted for, to avoid under- or over-estimation of road impacts on birds. In Chapter 3, I incorporate my detectability models into a spatial analysis of bird populations and roads across Britain, for 51 common and widespread species. This methodology allows independent assessment of the associations between roads and bird abundance, accounting for the potentially confounding impacts of roads on detectability. I find the abundances of 30 species to vary significantly with exposure to roads, some negatively and others positively. Across the interquartile range of road exposure, the mean decrease in bird abundance (for species with significant negative associations) was -19% and the mean increase (for species with significant positive associations) was +47%. In Chapter 4, in order to explore interspecific variation in these associations, I analyse a further 24 rarer species, and then test my results for all 75 species in relation to five characteristics. In this analysis I find 58 species to vary significantly in abundance with exposure to roads and the mean changes in bird abundance across the interquartile range of road exposure to increase to -39% and +48%. I also find that, with increasing road exposure, species with higher national populations have relatively higher abundance, while nationally rarer species have relatively lower abundance. Smaller-bodied and migratory species are also more negatively associated with road exposure. The distances over which negative associations between road exposure and bird abundance can be detected reach a mean of 700 m from a road, an area covering over 70% of Britain and 41% of the total area of terrestrial protected sites. I suggest that roads may, like some other forms of human disturbance, create conditions that benefit generally common species at the expense of others, thus having the potential to contribute to large-scale simplification of bird communities. Finally, in Chapter 5, I give an overall discussion of my thesis and highlight the importance of further work to understand in more detail the impacts of roads on birds in Britain and elsewhere, and to mitigate them effectively.

This thesis addresses the detrimental effects of road traffic congestion in the Smart City environment. Urban congestion is a recognisable problem that affects much of the world's population through delays and pollution although the delays are not an entirely modern phenomena. The progressive increase in urbanisation and the numbers of powered road vehicles have led to an increasing need to control traffic in order to maintain flows and avoid gridlock situations. Signalised methods typically control flows through reduction, frequently increasing delays, holding traffic within the urban area and increasing local pollution. The current levels of vehicular congestion may relate to an increase in traffic volumes of 300% over 50 years while traffic control methods based on delaying moving traffic have changed very little. Mobility and Socio-economics indicate that the number of active road vehicles will increase or at least remain at the same levels in the foreseeable future and as a result congestion will continue to be a problem. The Smart City concept is intended to improve the urban environment through the application of advanced technology. Within the context of road transportation, the urban area consists of a wide variety of low to moderate speed transportation systems ranging from pedestrians to heavy goods vehicles. Urban roadways have a large number of junctions where the transport systems and flows interact presenting additional and more complex challenges as compared to high speed dual carriageways and motorways. Congestion is a function of population density while car ownership is an indicator of affluence; road congestion can therefore be seen as an indicator of local economic and social prosperity. Congestion cannot be resolved while there is a social benefit to urbanisation, high density living and a materialistic population. Recognising that congestion cannot be resolved, this research proposes a method to reduce the undesirable consequences and side effects of traffic congestion such as transit delays, inefficient fuel use and chemical pollution without adversely affecting the social and economic benefits. Existing traffic signal systems manage traffic flows based on traffic arrivals, prediction and traffic census models. Flow modification is accomplished by introducing delays through signal transition in order to prioritise a conflicting direction. It is incorrectly assumed that traffic will always be able to move and therefore signal changes will always have an effect. Signal transitions result in lost time at the junction. Existing Urban Traffic Control systems have limited capability as they are unable to adapt immediately to unexpected conditions, have a finite response, cannot modify stationary flow and may introduce needless losses through inefficient transition. This research proposes and develops Available Forward Road Capacity (AFRC), an algorithm with the ability to detect the onset of congestion, actively promote clearance, prevent unnecessary losses due to ineffective transitions and can influence other AFRC equipped junctions to ensure the most efficient use of unoccupied road capacity. AFRC is an additional function that can be applied to existing traffic controllers, becoming active only during congestion conditions; as a result it cannot increase congestion above current levels. By reducing the duration of congestion periods, AFRC reduces delays, improves the efficiency of fuel use and reduces pollution. AFRC is a scalable, multi-junction generalised solution which is able to manage traffic from multiple directions without prior tuning; it can detect and actively resolve problems with stationary traffic. AFRC is evaluated using a commercial traffic simulation system and is shown to resolve inbound and outbound congestion in less time than Vehicle Actuated and Fully Timed systems when simulating both morning and evening rush-hours.

Traffic-related air pollution harms health, so whilst it would be advantageous to improve air quality, the socioeconomic impact of air pollution mitigation in Scotland is not fully understood. Evidence from research literature, current regulatory and policy directives and a socioeconomic analysis are required to assess the true health impact. This thesis presents the first health and socioeconomic analysis of traffic-related air pollution and health for Scotland. A critique of the literature was undertaken to determine the evidence base and the strength of evidence in terms of association and causation, between air pollution and ill health. The evidence was subsequently applied in epidemiological studies of Scottish residents, to assess the actual impact on health in Scotland. The perception of barriers and incentives for change were investigated to understand behavioural influences. Recent policy development in Scotland was reviewed, and a socioeconomic analysis of a proposed air pollution strategy in Scotland, was undertaken. The evidence from 30 cohort studies and nine literature reviews demonstrated a link between poor air quality, mortality and respiratory ill health, but the results for other health conditions were inconsistent. The links were associative rather than causal and therefore might be attributable to other factors other than air pollution. Furthermore, epidemiological studies on Scottish populations did not show health effects from traffic-related air pollution. The socioeconomic analysis suggested that an initial investment of between £27m and £44m to introduce Low Emission Zones (LEZ), and an effective active travel programme, might result in a saving of £38m in terms of Years of Life Lost (YLL) and reduction in sickness absence. It is unlikely that the Clean Air For Scotland Strategy will deliver improved air quality and health without substantial investment, better alignment of planning, and a greater public engagement to support public and active transport options.

This thesis examines traffic congestion in twentieth-century London. Ideas about movement, mobility and circulation in the capital's streets intersect with a broad range of discourses and practices, from planning to policing, and from engineering to economics. Within these discourses, characterizations of congestion are political acts, and proposed solutions therefore represent political as well as geographical and technological world views. The thesis opens with an examination of the experience of congestion in twentieth-century London, setting up the 'traffic problem', before surveying the ways traffic congestion has been considered in the history of urban planning, the dominant discourse in which this topic is explored. Common themes have emerged from this close reading of the planning literature, including geographies of governance and the relationship between London-wide bodies and local authorities; technologies of congestion and notions of grids, surveillance, verticality and technological progress; segregation and control of different classes of people and vehicles; and the politics and financing of roads and mobility. The subsequent four chapters therefore examine 'solutions' of the traffic problem from a set of disciplinary points of view suggested by these themes, namely police officers, highway engineers, systems analysts, and political economists (although the boundaries between the groups are highly permeable). In each instance, detailed case studies of geographical places and technological structures are used to ground the discussions of disciplinary discourses and point to their limits. A fifth chapter then returns to the professional planning discourse and draws together these themes, examining how plans to reduce congestion in London have been negotiated into reality. The concluding chapter looks at the situation in London today and in the near future. It includes accounts from current practitioners in each of the disciplinary fields studied, and gives a sense of the long-term continuity of these wider concerns - the fact that characterizations of congestion are themselves historical artefacts which travel through time.

Traffic population in the UK has grown by 27% in 2002 and predicted to continue to an estimated 38% in 2016 and up to 60% by 2031. This means vehicular emissions from road transport may account for higher proportion of total emissions of pollutants resulting in air pollution with its attendant consequences. Although poor air quality concerns has often been linked to urban areas, many rural areas apparently have locations where air quality objectives may be threatened especially in the wake of increasing vehicular population. Thus, this elicits the necessity to investigate the relationship between vehicular emissions and air quality. This study investigated the influence of vehicular traffic on a major trunk road on rural air quality through continuous measurements of nitrogen dioxide, sulphur dioxide and hydrocarbon between June 2008 and April 2010 along a major trunk road in a catalogued rural environment in the UK. Collection and analysis of pollutants was by Dräger short-term tubes and Dräger passive diffusion tube techniques. Throughout the sampling period, concentrations of sulphur dioxide were not detected using the short-term tube technique but were detected by the passive diffusion tubes. The study found that variations in mean concentrations of the pollutants were synonymous with traffic frequency and were influenced by meteorological conditions especially wind speed, temperature and relative humidity. Results observed concentration decline trend with increasing distance and showed maximum concentrations during winter, mainly in areas of close proximity to anthropogenic source, and minimum in summer. Values between winter year 1 and winter year 2 monitoring campaigns showed significant difference (P<0.05 and R=0.91) as was in summer year 1 and year 2 (P<0.05 and R=0.94), spring year 1 and year 2 (P<0.05 and R=0.84) and autumn year 1 and year 2 (P<0.05 and R=0.79). When compared with the guidance limits, NO2 Page ii showed exceededance at roadside and 50 m, and at some sample sites, up to 100 m from the road. Conversely, SO2 did not show any exceedance but statistical analyses was mostly significant between concentrations and distance at p≤0.05, suggesting the variability of pollutants, as well as the influence of distance on their temporal and spatial distribution. Results also show that pollutants correlated very well with daily traffic population with strong positive r2 and R-values. Similarly, the study considered the application of hazel leave (Corylus avellana) and ryegrass (Lolium perenne) vegetation samples in monitoring rural air quality. Both samples were collected in different seasons and distances (5m, 50m, and 100m) from the A49 trunk road at four rural sites characterised with diverse traffic densities and anthropogenic activities. The aim was to determine the elemental content and trends within the samples and to investigate the influence of distance from the road, height from ground level, and sampling season on the elemental levels. The levels of Al, As, Ba, Ca, Si, Mg, S, Cd, Cr, Na, Ni, Sb, Se, Sn, Mo, Mn, C, K, P, Cl, Ti, Fe, Zn, and Pb were determined using X-ray fluorescence (XRF), inductively coupled plasma-mass spectrometer (ICP-MS), and Scanning Electron Microscopy-Energy Dispersive X-ray spectrometer (SEM-EDX). Results show that despite the traffic differentials between the sampling sites, the pollution level of heavy metals were generally low in all sampling site and concentrations of Cr, Cu, Ni, Pb, and Ti exhibited inverse relationship with distance, decreasing in levels with increasing distance from the trunk road. Although root uptake from the soil is a potential source of heavy metals, geochemistry research of the study area did not show any evidence that proves any major heavy metals deposit concerns in the soil. It is therefore possible that heavy metal emissions were deposited in a form that was not readily available for root uptake, thereby narrowing the presence of heavy metal pollutants to other potential Page iii sources. However, this study found high level of heavy metals at the roadside measurements in the order of Zn (0.703 ppm) > Ti (0.346 ppm) > Cr (0.111 ppm) > Cu (0.106 ppm) > Pb (0.026 ppm) > Ni (0.025 ppm). They were found in different magnitudes higher than their respective levels at 50 and 100 m from the trunk road and therefore tend to support traffic origin. Findings from this study show that heavy metals exhibited different degree of correlation between individual elements, ranging from very strong positive to weak, as well as negative correlations. Statistical analyses show that the elements predominantly exhibited statistically significant differences between elements and between distances from the road. Overall, findings from this study demonstrate that both vegetation species prove to be successfully useful in determining the pollution status and trends of traffic-related heavy metals.

This thesis looked at traffic crashes in the emirate of Dubai in the United Arab Emirates (UAE) to establish the current situation in road safety and ways of improving it. A global overview of road safety literature revealed that standards of road safety vary widely by region. Key indicators like fatality rate and risk (Jacobs et al, 2000) were found to be higher in most neighbouring Gulf Cooperative Council (GCC) countries (10-25 fatalities/100,000 pop., 3-5 fatalities/10,000 motor vehicles) than in the best-performing Western countries (6 fatalities/100,000 pop., 1 fatality/10,000 motor vehicles). Interventions and countermeasures to tackle specific road safety issues were reviewed from international studies. Countermeasures were chosen with consideration for the local situation in Dubai within the categories of Human, Environmental and Vehicle factors. Examples of selected measures include offending driver punishment (Human), Electronic Stability Control (Vehicle) and central barriers (Environment). These measures were mostly studied in different environments to those in Dubai so the aspect of knowledge transfer between areas of different cultural and environmental conditions was discussed. Data from real world injury crashes (as collected by Dubai Police and the Roads & Transport Authority) over twelve years (1995 2006) were subject to macroanalysis in SPSS to identify the main issues over the past decade. 18,142 crashes involving 30,942 casualties and 48,960 vehicles were analysed at the outset. The following issues were among the main concerns: - High proportion of fatal crashes out of all injury crashes (13.5% compared to 1.4% in the UK); - Most fatal crashes involved a single vehicle hitting a pedestrian; - Most injury crashes involved a single vehicle; - Inconsiderate driving was the most common crash cause cited by the police. Countermeasures found in the literature to counteract these problems were then suggested for application and the estimated savings from applying them were calculated. Savings were quantified as either reductions in casualties or injury crashes. Furthermore, cost savings for the calculated reductions were estimated using existing UK crash costs due to the scarcity of UAE crash cost estimates. Calculation of the estimated improvement in safety if these countermeasures were applied retrospectively meant a reduction of 4,634 injury crashes and 1,555 casualties over the 12-year period with an estimated cost saving of approximately £368 million or 2.7 billion Dirhams. To refine this method more detailed data on crashes were required and collected from the dedicated crash investigation team files in Dubai Police for 2006 and part of 2007. This new dataset (300 crashes) was put into a purpose-built database with over 140 fields and subject to microanalysis to more accurately match the problems and interventions. Six interventions were matched to individual cases in the database where they would have positively altered the outcome. This process was verified by independent crash experts and investigators. The benefits from these six countermeasures were then weighted to calculate the benefits for the whole crash population over a year. Examples of specific interventions included guardrails along the roadside; grade-separated crossing facilities for pedestrians; Electronic Stability Control and speed cameras. The estimated total reduction in crashes was 2,412 annually with calculated savings of £40 million or 280 million Dirhams. This was the first time this geographical area was studied in such depth and detail to allow the calculation of benefits from interventions matched to known road safety issues. Various limitations were encountered such as the unavailability of GIS basemaps and the continuously changing infrastructure and population of Dubai. Numerous areas of further work were identified. Such work areas include hospital studies for collecting injury data to compare with police data; changing vehicle standards so that they are better suited to local crash types; the calculation of crash and injury costs based on local figures; vehicle fleet analysis for comparing different vehicle segments and exposure; and improved data collection and storage methods.

This research is concerned with the study of the behaviour of the vehicle/driver combination on motorways, in order to understand the effects which occur at higher flows and the potential to alleviate the problems. A simulation model was developed on the basis of the action point model, where the drivers' thresholds of perception and action were calibrated and measured by analysing vast sets of microscopic data. A comprehensively instrumented vehicle was used to collect the data for the project. A filter programme was developed for the raw data to overcome the effects of vibration and noise, which occurs due to many dynamic factors and circumstances in collecting the data. Three main situations were distinguished in developing the simulation model: 1) The Approach Process: This is concerned with the behaviour of a driver when approaching slower vehicle ahead. The research shows drivers are more likely to rely on the time-to-collision in controlling their approach process. However, other factors were measured, such as the perception threshold and the deceleration level. 2) The Following Process: This is concerned with the behaviour in close following situation. Many understandings were obtained from the analysis, probably the main one was that drivers tend to follow with shorter headways at high speeds, and no obvious threshold was found for the relative speed. Other behavioural elements investigated included driver speed control and the deceleration level during the close following process. 3) The Lane Changing Process: This is a very complicated process, which has many parameters and factors that are difficult to model. Relative speed and distance were used to determine thresholds for drivers' decisions to change lane. However, a complicated procedure was necessary to achieve a realistic behaviour in the model. The model was validated by comparing the output with the real traffic data. The calibrated and validated simulation model was further developed to investigate and assess the effects of two different Automatic-Cruse-Control [ACC] systems on traffic on motorways. The research shows that in general ACC systems work better with longer headway in reducing speed and acceleration variation. The comparison between the two tested systems showed that each has its own positive and negative side. Finally the safety benefits of such systems probably cannot be gained until the percentage of the equipped vehicles is very high.

The City of Austin collects and compiles data on Austin Police Department (APD) traffic stops annually. In early 2021, they released the data for 2020 APD traffic stops. This report adapts contemporary research methods to analyze this dataset with an aim of discerning whether and to what degree racial bias occurred throughout the city. Largely a spatial analysis, key findings point to East Austin’s historic areas of racial segregation—now areas of recent gentrification—as primary districts for disparate police traffic stop outcomes, though Black and Hispanic/Latino drivers were also found to be overrepresented in stops and searches across most of the city. This report concludes with a set of nine recommendations, first among them for Austin voters: vote “yes” for Proposition A and “no” for Proposition B on Austin’s May 6, 2023, ballot.

Automobile traffic has been a longstanding source of air pollution in human communities. The target of major regulations in the past few decades, the transportation sector has gone through significant changes, ranging from shifts in vehicle fleet composition to natural and artificial disruptions to traffic patterns. Both an essential form of transportation as well as a major source of air pollution, traffic, to this day, remains a human necessity and a public health challenge. As such, measuring and modeling the temporal and spatial distribution of traffic related air pollution (TRAP) is a critical necessity for exposure scientists, epidemiologists, and other public health professionals.In this dissertation, we investigate methods to measure TRAP in response to recent trends and disruptions in traffic patterns and composition, with a particular focus on California State. To this end, we employ novel combinations of big data, including regulatory air quality data in addition to traffic, land-use, and internet-of-things network data. It is divide into five chapters: an introduction (chapter 1), three chapters of original research (chapters 2-4), and a discussion of the conclusions of the work (chapter 5).Chapter 2 evaluates the near-road air quality impacts of traffic disruptions associated with the COVID-19 pandemic. Following global activity stoppages associated with stay-at-home measures, studies reported improved air quality in several cities and countries around the world. While widely observed, many studies could not properly attribute the decline of traffic in this chapter evaluates the relationship between traffic volume as reported by the California Department of Transportation and near-road NO and NO2 emissions at seven EPA monitoring sites in California state: four in Northern California and three in Southern California.Chapter 3 models the spatial distribution of non-tailpipe emissions-related PM2.5 chemical species and oxidative potential in Southern California. Combining gold-standard filter samples, land-use data, and a novel internet-of-things low-cost sensor network dataset in a spatial regression (Co-Kriging with External Drift) model, we create a set of exposure surfaces for exposures that serve as tracers of TRAP. Results indicate that compared to typical modelling techniques, namely land-use regressions, the addition of low-cost sensor data improves model accuracy and precision.Chapter 4 evaluates the associations between exposures modeled in Chapter 3 and the ischemic placental disease (IPD) in a prospectively-followed pregnancy cohort of 178 women. Air quality regulation have resulted in declines in tailpipe emissions in recent years. As stated earlier, TRAP is also generated from non-tailpipe sources, including brake and tire wear. Concerned that brake and tire wear particles contain metals and other organic compounds that could harm fetal health, this study uses a logistic regression model to estimate exposure outcome associations. Compared to conventional exposures, namely PM2.5 and black carbon, we find stronger associations between IPD and exposures more specific to brake and tire wear, such as barium, as well as oxidative potential markers.At the time of filing, Chapter 2 has been published in Environmental Science and Technology Letters, Chapter 3 has been published in Environment International, and Chapter 4 is currently in preparation for submission to an academic journal.

Roadway facilities have become an integral part of the growth of cities, countries, and nations. Transportation systems keep society operational and improve the quality of life by allowing businesses to grow and service more people, providing access to the delivery of items from far and near locations, and the freedom to explore new horizons. But unforeseen events such as the COVID-19 pandemic disrupted such operations by changing the utilization of these roadway networks.This research studied the effects of COVID-19 mandates on roadway travel in all 50 states, and the impact of volume changes by vehicle classification in the State of Ohio. In addition, this research generated a regression model to predict traffic volumes for each vehicle class group utilizing recent societal factors impacting the roadway network. Traffic volume data for all 50 states were collected from the Federal Highway Administration’s traffic volume database, and Ohio’s volume vehicle classification data was collected from the Ohio Department of Transportation’s Traffic Monitoring Management System. Results showed that in 40 of the 50 states, significant changes in traffic volumes were observed. In Ohio, Class 1 to 4 vehicles were negatively affected, as a significant decline in traffic volumes (-17.35 percent) was observed from 2018 to 2020. The most increase recorded was among Class 8 to 13 vehicles from 2018 to 2021 (28.21 percent). Significant changes to traffic volumes and traffic composition have an impact on pavement design models which heavily rely upon accurate traffic forecasts. Future research efforts should continue to monitor if the traffic volume and composition changes are long-term impacts or simply associated with the pandemic.

Over the past decade more people are opting in to purchase and own their own personal vehicle and in some instances several, which means that road accidents rates are doomed to increase. And this presents a challenge to the government, individuals and the collective community as car accidents are in most cases life threatening and a hazard to society. Thus, this paper aims to tackle this issue and dig deep to explore the main factors contributing to the increase of car accidents rate. The dataset used in this research is data collected from traffic accidents events captured by the department of transportation, law-enforcement agencies, and traffic cameras continuously in the United States from 2016 to 2020. Two models were performed to predict the impact of car accidents on road traffic, with a focus on the leading factors contributing to road accidents. Results showed that the main two factors affecting car accidents rate are traffic caused by work rush hour and population density. Furthermore, this research can be used to create solutions to limit and decrease car accidents in cities, such as adopting the working from home concept, facilitating the ownership of self-driving vehicles, creating a seamless public transportation infrastructure, and distributing rush hours throughout the day to name a few.

A study is described concerned with assessment and prediction of noise from large civil engineering construction sites. The relevance of this topic is demonstrated in the first chapter by reference to recent national and international standards and legislation. A combined noise and postal social survey was conducted around an urban highway construction site to determine the factors controlling construction noise annoyance. Dose-response criteria were measured and compared for both construction and traffic noise and the importance of attitudinal factors in determination of construction noise annoyance was demonstrated. Other conclusions concern the utility of postal surveys to the execution of medium scale investigations of this type. Two chapters are concerned with the description and pre- diction of noise from common construction equipment and processes. Typical sound pressure-time traces and frequency spectra are presented which reveal that machines may be assigned to categories that have significant bearing on noise propagation. Assumption involved in exposure prediction and measurement were tested and an exposure prediction method for earthmoving operations developed and validated. Particular attention was paid to characteristics and measurement of noise from impact pile drivers A strategic method of exposure prediction was studied by reference to the example of 'cut and cover' tunnelling. This allowed the determination of four estimates of noise exposure which were related to different construction techniques and the assessment of the contribution of each machine type to the total exposure. A study was made of the degree of noise control available for each machine type. This information was used to determine the effectiveness of various extents of use of noise-controlled equipment. Appendices are included which contain details of computer-based data analysis methods, questionnaires and other results.

There is increased focus on renewable energy, environmental protection and sustainable development in the sectors of transportation, hence this has necessitated a lot of studies into the types of transportation mechanism and their impact on our environment. Aside other major pollutants, fossil fuels have catered to the energy demands of an increasingly busy and industrialized society and cities, but has also seen objections as to their adverse consequences on the environment and how much pollution it contributes to the environment. Due to the forced restrictions and lockdowns from Covid-19, which saw an extremely minimal usage of conventional transportation across cities, it was observed that pollution levels in cities across the India for example, drastically slowed down just within few days (Mahato et al, 2020), same has been the report in many other cities across the world. This underlines the impact which industry, human activity and traffic has on the environment, by the quality of air and how much of an effect it has on the people who live in such regions.Keywords: Air pollution, hazards, multiple regression, environment, traffic

The use of GPS navigation apps has been surging with increasing the use of smartphones. People used to use a map to find a way to their destination, but now the navigation apps provide us with several options that can be taken as the best path for drivers. Also Connected and Autonomous Vehicles (CAVs) have been attracting attention and have improved a lot in tandem with the popularization of electric vehicles. Also, it is expected that autonomous driving which means that the car is self-driven instead of being driven by a person will emerge in the near future. This autonomous driving will rely on algorithms that will determine the best path once the destination is set and will perform dynamic routing to adapt to road network congestion during the trip. These algorithms that suggests the best paths will affect road traffic flow. Furthermore, when more CAVs rely on the same best path algorithm in the future, it will lead to a significant impact on a city’s road network traffic if an adversary can intentionally cause real or virtual disruptions in an few strategic locations.This thesis presents the results of simulation experiments to determine vulnerability in road network in the city of Davis in California. Specifically, by developing a simulation model, this study models how disruption in one part of the network can spread and how this is related to or not related to some of the topological properties of the network. Lastly, this thesis discusses a way to minimize the effect of the vulnerability.

This study focuses on investigating the contributions of traffic management centers to enhancing people’s driving experiences and impacting their level of satisfaction and happiness. Data was collected in the United Arab Emirates through two distinct surveys; the first aimed at drivers (number of respondents: 155), and the second aimed at traffic management center operators (number of respondents: 15). The drivers survey aimed to collect data about drivers’ pain points experienced while driving in the United Arab Emirates and showed that slow drivers on fast lanes and sudden lane changing are the biggest challenges reported. On the operators’ side, the data collected showed that operators reported observing these challenges from their side as well. Operators also notably reported the need for advanced technology to help better manage and respond to real time traffic situations remotely from traffic management centers. Both surveys conducted showed a need and potential for the contributions of traffic management centers in enhancing and upgrading the quality of life for citizens through the application of technological solutions and the development of supporting legislation. Supplementary data from similar surveys was also used to validate, expand the knowledge and provide a holistic view of the topic. The study indicated that traffic management centers can impact the happiness and satisfaction of citizens by enhancing their driving experience, given that they are designed and equipped in a way that suits the city and society trends and cultures. Recommendations for implementation of such design choices were given along five pillars considering administration (based on best practice and Benchmarking), technology (results of local and international TMC surveys), media and communication (international survey and the expansion of technology and social media), operations and legislation (Based on results of the driver’s survey, that shows some gaps in the legislations which can be enhanced).

Humans have transformed much of the natural landscape and are continuing to do so at an accelerated rate, compromising natural areas that serve as important habitat for many species. Roads impact much of the environment as they fragment habitat and introduce traffic noise into the acoustic environment, deferentially affecting wildlife in roadside habitat. I explored how traffic noise affects the detection of birds based on whether their vocalizations were masked by traffic noise. Masked species detection was not affected by an increase in traffic noise amplitude, while there was a negative effect of traffic noise amplitude on unmasked species detection, an unexpected result. Conducting more experiments on individual species detection will help ecologists better understand the changes in behavior that influence detection. The effect of human activity on the environment should be better understood by more than just ecologists. Yet, people in the United States fall behind other developed countries in their understanding of many scientific processes, such as evolution. Improved evolutionary knowledge leads people to have a higher acceptance of evolution, and biology educators are responsible for improving evolution education to promote more acceptance. For example, biology students seem committed to survival-based reasoning of evolution, but there are other important evolutionary forces to consider, such as sexual selection. Multiple selection pressures can act on a species, including pressures that select for traits that are maladaptive for survival. Through interviews, we explored how selection for the same and different trait variants affected student reasoning of evolution. When asked to describe evolution in a scenario where selection favored the same variant of a trait, students relied on survival-based reasoning. When students were presented with a scenario where different selection pressures selected for different trait variants, most students described how sexual selection acted on the traits of the population and included reproductive potential as a component of fitness and inheritance in their descriptions of evolution. Teaching examples with scenarios where different selection pressures are selecting for different traits may improve student ability to reason about the role of sexual selection in evolution and the role of reproductive potential in fitness, improving overall understanding of evolution. Advisor: Joseph T. Dauer

The responsibilities of meteorologists have evolved over time from simply providing a forecast to needing to also understand how those predictions will impact society and then communicating those risks in a clear, concise, and consistent manner. Increased motor vehicle crash numbers due to adverse weather conditions represent one such impact worthy of further study. Snowfall, in particular, significantly increases the overall risk of a crash, which can result in extensive property damage, severe injuries, and even loss of life.This project seeks to supplement traffic crash information in Nebraska by assessing how snowfall impacts crashes across the state. Crash data were obtained from the Nebraska Department of Transportation (NDOT) for the years 2007 through 2017. Total number of crashes, injuries, and fatalities are first evaluated temporally by year, month, day of week, and time of day. Crashes with snow reported by the investigating official as the primary or secondary weather condition were then identified. Variations between snow-related crashes and all crashes were calculated to identify how snowfall affects temporal crash distributions. It was found that snow-related crashes increase crash totals on weekends as well as during the morning hours relative to all crashes. The spatial distribution of snow-related crashes was also assessed at the individual crash, county, and NDOT district locations. The versatility of the newly developed Nebraska Winter Severity Index (NEWINS) is also evaluated to determine its utility for determining how different winter storm severities impact snow-related crash totals. Furthermore, the individual meteorological variables that go into creating the NEWINS are also assessed. In general, it was found using the NEWINS that longer duration, larger winter storms caused the highest number of crashes. In addition, winter events with higher snowfall totals and lower visibilities also resulted in higher crash totals. Lastly, high impact crash days were identified from daily crash totals and the occurrence of multi-vehicle chain‑reaction crashes, the largest of which resulted in a 31-car pileup. This project seeks to provide insight into when and where Nebraskans are typically at greater risk of a crash due to winter weather conditions. Advisor: Mark R. Anderson

Abstract: Grasslands ecosystems are increasingly subject to anthropogenic alterations, including conversion to cropland or tame pasture, and dissection by urban infrastructure. Linear disturbances, like roads, oil and gas pipelines, and electrical transmission lines, can fragment these ecosystems, directly alter underlying soils, and serve as vectors for plant invasions. A novel construction method, adapted from oil and gas extraction in the northern boreal forest, uses technology to redistribute equipment weight during construction in native grassland in an effort to decrease the footprint created from industrial development. The technology is comprised of an interlocking matrix of three-ply wooden mats, called access mats, used to create temporary road and work areas.This study was conducted on the University of Alberta’s Mattheis Research Ranch to examine the impact of various access mat placement treatments on native grassland of the Dry Mixedgrass prairie in SE Alberta, and reflected ongoing construction activities of ATCO’s Eastern Alberta Transmission Line, which was located nearby. Treatments were conducted in which the timing and duration of mat placement were varied from spring to fall, and from 6 to 24 weeks, on each of two ecosites (loam and loamy-sand), and monitored for up to 3 years of recovery. Main areas of focus were soil physical attributes, such as soil bulk density, penetration resistance, and water infiltration, along with nutrient supply rates, as well as vegetation responses such as biomass and nutrient components. Results of the field trials indicated traffic without mats (TWOM) increased soil penetration resistance (PR), while traffic with mats (TWM) initially had lower PR when mats were removed and soil moisture contents (SMC) two to three times the Control; however, SMC and PR within the TWM returned to Control levels six weeks after mat removal. Grass biomass was reduced under mats in place for 12 weeks or more compared to Controls in the first and second year (p = 0.0007) of recovery after treatment application. Introduced and ruderal forb biomass increased during first year of recovery (p < 0.0001). Biomass responses were more apparent on loamy-sand soils than loam soils. Light levels of traffic in this study did not create impacts that differed from the Controls for either grasses or introduced forbs under TWOM. Soil nutrient availability increased under TWM the year of treatment application relative to Controls; more specifically, nitrogen was ten times higher, sulfur double, iron four times higher, and manganese five times higher than those measured in the Control. Available soil nutrients quickly dissipated and led to levels similar to those of the Control by the following spring. Soil available nutrients under TWOM saw slight or no increase compared to Controls. Under the conditions of this study soil physical properties and vegetation were maintained close to those of the Controls under TWM when mats were in place for no longer than 6 weeks, while simultaneously mitigating the effects of wheeled industrial traffic. Results indicate that soil and vegetation recovery are influenced by the dominant soil texture as well as the season and duration of access mat use. Mats moderated soil compaction at the cost of vegetation biomass in placements longer than six weeks. Mats would be recommended to extend work time frame and possibly increase levels of traffic with consideration for the plant community underneath, and removal before plant death or with a revegetation strategy for recovery in place.

Twelve million residents of Lagos, Nigeria use on public transit daily. However limited mobility options strain the existing transit infrastructure making daily commutes slow, unreliable and expensive. Transit planners lack adequate data to manage and control traffic flows. Residents lack transit information to support them in optimizing their transit decision. This paper presents BITDANFO: a native Android application that supports the real-time sensing and aggregation of traffic patterns and transit information. BITDANFO was built to assess the viability of using GPS-enabled mobile phones to collect structured, crowd-sourced and machine-generated traffic data. The goal is the development an information collection mechanism capable of supporting the collection of real-time information on traffic conditions from the crowd. The main contribution of this project is a design for a scalable, peer-to-peer, cloud-based traffic information collection system. We also explore features in the design of BITDANFO that were generated from a thorough review of current approaches to crowdsourced traffic information collection.

Collaboration is an important aspect of many successful natural systems, but it is rare to find in transportation systems. However, recent advances in the standardization of communication technologies, improvements in unmanned aerial systems, and deployments of large autonomous vehicle fleets could be used to collaboratively optimize entire traffic networks and improve the safety of self-driving cars. This thesis considers how unmanned aerial systems could use communication to provide useful information to self-driving cars and transportation systems. A custom unmanned aerial system is designed and built to study dedicated short-range communication (DSRC) technology. The physical layer of DSRC is studied extensively using the unmanned aerial system and concerns are given for antenna design. Then a simulation environment is built to study large scale implementation of communication and unmanned aerial systems in traffic networks. This simulation environment is shown to be useful for a wide array of traffic studies. Finally, considerations are given for future work.

Concentrated foot traffic is a significant source of stress for natural turfgrass systems. Downward foot pressure can increase soil bulk density and reduce turfgrass shoot density. Golf putting surface traffic has attempted to be simulated using specialized equipment or with humans moving in a linear walking path or side-stepping. These methods often disregard the unique movement associated with actual traffic patterns, especially localized around the hole location. Concern regarding traffic stress associated with specific golf footwear has increased due to dramatic design changes made to increase traction and stability during a golf swing. An unintended consequence of increasing golf footwear traction and stability has been a perceived increase in foot traffic stress and disruption of playing conditions on putting surfaces. Studies were conducted in 2016 and 2017 to develop a standard method of applying a known volume of foot traffic associated with playing golf and utilize this method to assess performance of two golf playing surfaces. To achieve a rapid, uniform, quantifiable rate of traffic a walking pattern designed to impose concentrated foot traffic at known rates and volumes was investigated during the 2016 and 2017 growing seasons. Data collected on key surface performance parameters at the hole location indicated differences between playing surface types. Golf footwear traffic resulted in a significant increase in clipping yield independent of playing surface type. Putting surface performance declined proportionally with traffic volume as a result of reductions in fractional green canopy cover, ball roll distance, and visual turf quality.

Unmanned aerial vehicles (drones) can be used in traffic and road monitoring applications. We investigate the benefit of using drones for simultaneous traffic state estimation and incident detection. Specifically, we propose a coupled planning and estimation framework where we adaptively navigate a drone to minimize the uncertainty on parameter and traffic state estimates. We show that the use of a drone provides significant improvement in incident detection under congested conditions. Without a drone, the estimation procedure in congested conditions is not able to distinguish between observations due to congestion under normal operating conditions and similar observations due to a reduction in capacity. To plan for multiple drones working in parallel, we investigate methods for partitioning a network such that each drone operates in a particular subnetwork. The partitioning objectives are to minimize inter-flow between partitions and to create balanced subnetworks. We implement a flow-weighted spectral partitioning algorithm and show that it performs better than simple agglomerative heuristics.

Nobody likes sitting in traffic, at least the author of this work does not. In this thesis, we take the question of how can we improve traffic? We break it down and iterate on it, finally arriving at the problem of traffic optimization, via the optimization of traffic lights. This is the main driver of this research work. Traffic light control strategies have been previously classified as static and dynamic. We typically see static controllers in the fixed-cycle or actuated controllers that are widely deployed in the traffic lights of our cities; however, truly smart traffic lights are still not a widespread technology. This work tries to ground some of the research in dynamic traffic light controllers, so that smarter traffic lights become practical and ready for roll-out. We describe a Reinforcement Learning algorithm which we designed, and which only uses information that is local to the traffic light. A key element of this algorithm is the fact that we believe the inputs to it can be obtained by existing traffic intersection technologies, making its deployment feasible. We compare this algorithm against an optimal static traffic control policy, in the pursuit of understanding the strengths of both controller types. Finally, we analyze and present the results of this comparison, and illustrate a set of opportunities that may improve traffic management through different approaches, such as optimal traffic management, following the footsteps of this work or by having drivers collaborate with load balancing.

INTRODUCTION: Air pollution in urban areas has been a growing concern in the public health sector, especially with regards to negative health effects from traffic-related air pollution. AIM: The aim of this study is to quantify air pollution levels along the Atlanta BeltLine Eastside Trail, a popular urban trail located in Downtown Atlanta, and compare the pollution levels to measurements taken along a nearby roadway. The goal is to ultimately determine if there are any significant differences between air quality along the BeltLine and air quality along nearby roadways. No statistical significance in the data would suggest that individuals utilizing the BeltLine are exposed to the same levels of traffic-related air pollutants that are present on nearby roadways. METHODS: Samples were collected along the Eastside Trail of the BeltLine and along neighboring roads over the course of 11 days using a mobile monitoring platform. Four parameters of air quality were measured- Optical Particle Counter (OPC) volume concentration, particle number concentration, median particle diameter, and black carbon levels. A paired t-test was conducted to assess any statistical significance between samples taken along the BeltLine versus samples taken along nearby roadways. RESULTS: While there was some statistical significance between recorded air pollution levels for individual days, the overall results showed no statistical significance for any of the air quality parameters that were examined. DISCUSSION: The findings of this study indicate that individuals utilizing the BeltLine have the potential to be exposed to the same levels of air pollutants found along roadways.

With the increasing interest in the application of an urban road congestion pricing, a simulation of running a charging scheme in New York City could be useful for future transportation development. The scenario is charging on the inbound to the New York Center Business District (CBD). It is used to estimate the changes in traffic volume and traffic speed. By using the New York Best Practice Model (NYBPM) to set up the charging standards, a level of traffic congestion will be estimated, and it will be distributed in a certain pattern. The charging scheme is based on the concept of tolling “equity.”

Urban parking policy formation comprises community consultation, business interest, political interest and traffic safety aspects. Local governments in Sydney have parking policies which are generally evaluated qualitatively. This thesis studies the parking choice of individuals in Burwood urban centre which is located in the inner-west of Sydney, Australia. It explores the Burwood town centre s existing parking policy quantitatively using econometric models. Past and recent parking literature have labelled experiments comparing specific parking locations and parking types. This research advances the parking literature by deriving the attributes weight such as parking search time, walk time among the short-term and all-day parking by conducting an unlabeled stated choice experiment. Based on the revealed preference survey conducted in 2011, an econometric parking choice model is developed that includes various factors which influence car driver s selection of different parking types at Burwood town centre. The results show that influence of parking price is found to be consistently significant in all parking types. Individuals are willing to pay more for on-street parking, especially which are located near restaurants, school zones, shops and services. The second study, which was conducted in June 2015, comprised stated preference survey with socio-demographic attributes. Though pricing is known to impact parking choice, there is limited understanding of the impact of parking time restriction on parking choice. This survey evaluates the difference between the willingness to pay for short-term parking and all-day parking. A mixed logit model was developed to account for the preference heterogeneity among the users. The proposed model also accounts for walking time to destination and parking search time. The findings reveal that individuals have higher Willingness to Pay to reduce their search time for short-term parking as compared to all-day parking. Furthermore, for both parking restrictions type, parkers are willing to pay more to reduce the parking search time compared to walk time. The research outcomes of this thesis would help policy makers to evaluate the existing parking restriction s location, pricing and future carpark development locations. These would not only help in easing urban traffic congestion on roads, by significantly reducing drivers cruising for parking space, but also help local governments raise additional funds which can be used in the maintenance and development of carparks.

Transportation systems are the economic foundation of any regional development. Our reliance on transportation to move goods and resources and to ensure access to labor to increase productivity, all have tremendous impact on revenue generation and growth. Traffic congestion is an inevitable byproduct of economic growth; the costs of traffic is not just time wasted, but also include the financial loss and environmental impacts of fuel being wasted. As an effort to understand congestion formation, this project investigates modeling traffic as a network and uses a percolation model to identify a normal traffic pattern as exhibited by the inhabitants of the region. Using real street maps from the OpenStreetMap project, morning, noon, and evening rush-hour traffic zones in Westwood Village were created to simulate the travel behavior of the inhabitants. The street bottlenecks identified for a 24-hr period were then compared to those formed from a uniform traffic flow. The results from this study may provide the foundation for a reasonable starting configuration for a self-organizing traffic light network that can dynamically adapt to unexpected demand in real-time.

The ever-increasing demands on transportation systems have led to the need for a robust and universal method for the analysis and forecasting of vehicular traffic systems. Traditional methods are mainly model-based, that is, the analysis is performed by investigating a mathematical model that represents the target dynamics of a traffic system. On the other hand, contemporary efforts have focused on utilizing artificial intelligence (AI) to model or forecast vehicular traffic dynamics. Despite these large efforts, there is still no single best-performing method for the analysis and forecasting of vehicular traffic dynamics. This is due to the very well known fact that the unpredictable behaviors involved in a traffic system, like human interaction and weather, leads to a very complicated high-dimensional nonlinear dynamical system. Therefore, it is difficult to obtain a mathematical or AI model that explains all events and time evolution of vehicular traffic dynamics. Even if such a model could be attained, it would not lead to a robust and universal way of traffic analysis and forecast, due to its need of extensive parameter tuning. Thus, in contrast to the model or AI-based approach, it is necessary to develop data-driven methods that can identify dynamically important spatiotemporal structures of traffic phenomena. In this thesis, we demonstrate how the Koopman operator theory can offer a model and parameter-free, data-driven approach to accurately analyzing and forecasting traffic dynamics. The Koopman operator theory framework is a rapidly developing theory in dynamical systems that offers powerful methods for analyzing complex nonlinear systems. The effectiveness of this framework is demonstrated by an application to the Next Generation Simulation (NGSIM) data set collected by the US Federal Highway Administration and the Performance Measurement System (PeMS) data set collected by the California Department of Transportation. By obtaining a Koopman mode decomposition (KMD) of the data sets, we are able to accurately reconstruct our observed dynamics, distinguish any growing or decaying modes, and obtain a hierarchy of coherent spatiotemporal patterns that are fundamental to the observed dynamics. Furthermore, it is demonstrated how the KMD can be utilized to accurately forecast traffic dynamics by obtaining a decomposition of a subset of the data, that is then used to predict a future subset of the data.

Standard traffic engineering methodologies rely heavily on traffic data collected in the field for the design and planning of roadways and intersections. This data can be used to build microsimulation models, which are versatile and realistic tools for analyzing traffic scenarios. Sometimes, however, time and budget do not allow for the collection of high quality data in the field, but answers to questions about traffic scenarios are still needed. This thesis provides a review of data that is typically available to the public online as well as existing traffic engineering methodologies that will be useful in manipulating that data. It presents an empirically derived method for estimating left turn, thru, and right turn counts at intersections based on tube counts on surrounding roadways and the characteristics of the intersection. It then presents an exploration of the distribution of directionality of traffic throughout the day. Finally, it presents a method for converting tube counts on an approach to an intersection to equivalent lane volumes so that signal timings can be estimated.

Autophagy is a conserved lysosomal dependent pathway employed by cells during stress conditions as an alternative source of nutrients to maintain cellular homeostasis and promote survival. The pathway is negatively regulated by the mechanistic target of rapamycin (mTOR) and induced by depletion of nutrients. Over the last decade input in the form of vesicular traffic from an array of cellular organelles (e.g. Golgi, ER, endocytic pathway and mitochondria) has been shown to be required for the delivery of proteins, enzymes and lipids during progression of the autophagic pathway. However, how these organelles switch from their constitutive roles to supply the autophagic pathway with proteins and lipids upon induction is not fully understood. In addition the extent to which these cellular organelles modulate autophagosomal growth and mTOR-Insulin signaling remains incompletely understood. The main goal of this thesis was to uncover novel traffic regulators of the Rab GTPase family required for starvation-induced autophagy in Drosophila fat body cells and evaluate their role in mTOR signaling regulation. To this end we carried a reverse screen using RNAi to knockdown 30 of the 33 Drosophila Rab GTPases. We show Rab 2, 7 and 14 GTPases are required for the induction and growth of autophagosomes and autolysosomal function. Rab5 is required for autophagic vesicle induction and growth, endocytosis and lysosomal maturation. Lastly, that Rab6 is required for the sorting of lysosomal hydrolases, autolysosome turnover and the regulation of mTOR signaling via regulation of the insulin receptor localization in fat body cells. Altogether we uncovered novel regulators in the vesicular traffic regulator Rab GTPase family required for autophagy and mTOR-Insulin signaling regulation in Drosophila.

The exceptional rise in usages of mobile devices such as smartphones and tablets has contributed to a massive increase in wireless network trac both Cellular (3G/4G/LTE) and WiFi. The unprecedented growth in wireless network trac not only strain the battery of the mobile devices but also bogs down the last-hop wireless access links. Interestingly, a signicant part of this data trac exhibits high level of redundancy in them due to repeated access of popular contents in the web. Hence, a good amount of research both in academia and in industries has studied, analyzed and designed diverse systems that attempt to eliminate redundancy in the network trac. Several of the existing Trac Redundancy Elimination (TRE) solutions either does not improve last-hop wireless access links or involves inecient use of compute resources from resource-constrained mobile devices. In this research, we propose TailoredRE, a personalized cloud-based trac redundancy elimination system. The main objective of TailoredRE is to tailor TRE mechanism such that TRE is performed against selected applications rather than application agnostically, thus improving eciency by avoiding caching of unnecessary data chunks. In our system, we leverage the rich resources of the cloud to conduct TRE by ooading most of the operational cost from the smartphones or mobile devices to its clones (proxies) available in the cloud. We cluster the multiple individual user clones in the cloud based on the factors of connectedness among users such as usage of similar applications, common interests in specic web contents etc., to improve the eciency of caching in the cloud. This thesis encompasses motivation, system design along with detailed analysis of the results obtained through simulation and real implementation of TailoredRE system.

In the cool-season region of the United States, golf courses traditionally grow high-input grasses like creeping bentgrass (Agrostis stolonifera, L.), Kentucky bluegrass (Poa pratensis, L.), annual bluegrass (Poa annua, L.), and/or perennial ryegrass (Lolium perenne, L.) on fairways. Grass species exist that are more sustainable than those currently being used for golf course fairway turf. Low-input fine fescue species could be able to withstand the pressure from typical turfgrass disease and stresses while producing acceptable turf and excellent playing quality with fewer overall inputs of pesticides, water, and fertilizer. Little research has been conducted on these species in a fairway setting, so golf course managers have been hesitant to use fine fescues. This project conducted research to overcome these barriers and thus begin using low-input fine fescues for fairways on golf courses throughout the northern United States. The objective of the first experiment was to evaluate fine fescue species’ performance as fairway turfgrass under an acute drought. Field trials were conducted at two locations under a rainout shelter. Mixtures that contained large proportions (>33%) of Chewings fescue [Festuca rubra ssp. commutata (Thuill.) Nyman] had the greatest green cover at the end of the drought period. The marginal effects summary revealed no significant differences among species success after drought. Overall, this study found that fine fescues can provide acceptable turf quality and playability on golf course fairways resulting in lower irrigation inputs. The objectives of the second project were to determine the effect of the plant growth regulator trinexapac-ethyl on the performance of fine fescue mixtures when managed as a golf course fairway and identify fine fescue mixtures that perform well under traffic stress. The marginal effects summaries showed hard fescue [Festuca trachyphylla (Hack.) Krajina], slender creeping red fescue [Festuca rubra ssp. litoralis (G.F.W. Meyer) Auquier.], strong creeping red fescue (Festuca rubra ssp. rubra Gaudin), and sheep fescue (Festuca ovina, L.) had the greatest component effect on visual turfgrass quality, and were all statistically similar. Strong creeping red fescue was more susceptible to dollar spot disease (caused by Sclerotinia homoeocarpa F.T. Bennett) than the other species. The third experiment evaluated fine fescue species and mixtures for snow mold resistance on three golf courses in Minnesota. In the spring of 2013, 2014, and 2015, there was no damage from snow mold. These grasses may be resistant to the pathogens; however, our observations in higher cut fine fescue suggest that snow mold and snow scald diseases can be a problem in these grasses. Although the objective to determine if fine fescue fairways require fungicides at currently-recommended application rates to survive winter snow mold pressure was not accomplished, turf quality data taken over 2 years was analyzed. Mixtures maintained significantly better turfgrass quality than any of the five species alone.

Fatigue damage can be defined by a decrease in stiffness of Asphalt Concrete (AC) under repeated traffic loading. For each cycle of traffic loading, tensile strain develops at the bottom of AC layer of an asphalt pavement. Some localized damages occur in the material at minute-scale due to this developed tensile strain. These damages cause decrease in stiffness (E) of AC. Damage caused by a single vehicle is small. However the accumulated damage is not small if a large number of vehicles are considered over the design life of an asphalt pavement. After certain level of damage accumulation, bottom-up fatigue cracking initiates and forms alligator cracking at the surface. Like traffic loading, repeated day-night temperature cycle causes damages in AC. Damage due to a single day-night temperature fluctuation may be small. However the accumulated damage due to a large number of day-night temperature cycles may not be small. In this study, fatigue damage due to traffic loading is termed as traffic-induced fatigue damage, and fatigue damage due to temperature is termed as temperature-induced fatigue damage. The recently developed AASHTOWare Pavement Mechanistic-Empirical (ME) Design Guide predicts the fatigue performance of AC based on repeated traffic-induced tensile strain at the bottom of AC layer. Cyclic thermal strain due to day-night temperature fluctuation is not considered due to the fact that there is no closed-form solution or model available for calculating thermal fatigue damage. This study, for the first time, develops a closed-form equation for calculating the temperature-induced fatigue damage of AC. To develop the model, beam fatigue testing was conducted using different AC mixtures in the laboratory. The mechanical beam fatigue test data was correlated with the actual cyclic temperature loading test data. The model was then validated using an unknown test data. To that end, the developed model was calibrated for field conditions using the Falling Weight Deflectometer (FWD) test data. The developed model is used to evaluate fatigue damages of 34 Long-Term Pavement Performance (LTPP) test sections. Fatigue damage predicted by the traditional AASHTOWare Pavement ME Design approach, which considers only traffic-induced fatigue damage, is compared to the fatigue damage by the developed model which considers both traffic- and temperature-induced fatigue. Results show that the error may decrease by up to 29% through the incorporation of temperature-induced fatigue damage in the AASHTOWare Pavement ME Design approach. This means the reliability of alligator cracking prediction can be improved through the use of the developed thermal fatigue model. It is therefore suggested to include the temperature-induced fatigue damage model, which is developed in this study, in the AASHTOWare Pavement ME Design Software.

Encouraging people to cycle more often is well supported in the academic literature due to the numerous positive economic, social and environmental benefits that are associated with the use of bicycles as a form of transportation. Despite these benefits, the use of the bicycle for day to day transportation remains relatively low outside of European and Asian countries, with one of the main barriers to encouraging more people to cycle more often being related to the perceived and actual dangers associated with riding a bicycle. Using a case-control methodology, this research investigated what characteristics contribute to where bicycle accidents occur in proportion to where to people cycle. Logistic regression analysis identified that the probability of being involved in a bicycle-motor vehicle (BMV) accident increases when specific characteristics are present and decreases with the presence of on road cycle lanes. Of the characteristics identified as being significant, accident probability is highest at intersections, with all types of intersections increasing accident probability compared to non- intersection locations. In addition to intersections, this research also identified that accident probability increases with the presence of high traffic volumes, School zones and driveways.

Several studies have determined the use of seat belts to be one of the major contributing factors in the reduction of fatalities and injury severities associated with motor vehicle crashes. Some studies have found that there is a relationship between drivers and their front passengers in terms of seat belts usage. The objective of this thesis study was to evaluate the seat belts usage rates in Dayton, Ohio based on vehicle type, gender, age, day of the week, time of observation, and person type driver or passenger. Data for this thesis was collected from thirteen sites in Greater Dayton, Ohio by direct observations at interchange ramps and intersections. The binary logistic regression model was used to investigate some independent variables of seat belt usage rates of drivers and their outboard (front seat) passengers in Dayton, Ohio, that is, the binary logistic regression model was used to identify factors that may play a role in relation to seat belt usage. The results from the binary logistic regression modeling show that the person type and vehicle type are significant factors affecting the likelihood of seat belt usage. There were no significant interactions identified between the factors studied.The odds of using seat belt by drivers are higher than the odds of using seat belt by their passengers. Also, the odds of occupants of passenger cars and sport utility vehicles to be belted are higher than the odds of using seat belt by pickup truck occupants. There is no statistically significant difference between van and pickup truck occupants in terms of their seat belt use. Moreover, the pickup truck and van occupants have the lowest seat belt usage rates.In order to increase seat belt usage rates, this study recommends for enforcement officials to pay more attention with pickup truck and van occupants when checking out unbelted vehicle occupants. This persistence will make them increase their seat belt usage, which eventually will increase their chances of saving their lives in case they get involved in severe crashes. The drivers should be encouraged to persuade their passengers to use seat belts. The seat belt law should be upgraded to a primary law from the current ineffective (i.e., difficult to enforce) secondary law if the state of Ohio seriously wants to increase the seat belt usage in the state.

There are a large number of models that can be used to describe traffic flow. Although some were initially theoretically derived, there are many that were constructed with utility alone in mind. The Intelligent Driver Model (IDM) is a microscopic model that can be used to examine traffic behavior on an individual level with emphasis on the relation to an ahead vehicle. One application for this model is that it is easily molded to performing the operations for an Adaptive Cruise Control (ACC) system. Although it is clear that the IDM holds a number of convenient properties, like easily interpreted parameters, there is yet to be any rigorous examining of this model from a mathematical standpoint. This paper will place this model into the form of a vector-valued time-autonomous ODE system and analytically examine it. Additionally, the parameter estimation problem will be formulated. Simulations will demonstrate the model in practice.

Traffic Incident Management (TIM) programs are used in many states across the nation to efficiently clear interstate incidents and alleviate congestion in response to incidents. The overarching goal of this research is to maintain safety of the motoring public and incident responders while reducing congestion, secondary collisions, and traffic delays from incidents that occur on South Carolina highways. To reduce incident clearance times along the I-26 corridor between Columbia and Charleston through the implementation of a Traffic Incident Management (TIM) Program, several objectives for this thesis were established to move toward the goal, including; defining gaps between national TIM best-practices and SC TIM practices recommending programmatic changes to address gaps based on best-practices literature, developing training materials to address programmatic changes as needed, and assessing existing performance measurement metrics and suggest improvements to better evaluate changes in program effectiveness. The nationally recommended best practices were compared to the current South Carolina practices. Gaps were observed and SCDOT expressed interest in developing towing, coroner, HAZMAT, and crash investigation procedures. Successful programs involving these groups of responders were evaluated, including implementation costs and challenges. Several successful national programs, as well as those in Florida, Georgia, and numerous other states were included in this step. This specifically included incentive based towing programs, fatality removal programs, HAZMAT training procedures, and crash investigation technologies. It is recommended that these programs be considered and investigated further when implementing a SC TIM plan. It was however noted that while basic HAZMAT training is necessary, a large or full-scale program may not be cost effective. After analyzing the data, areas where data collection could be improved were determined. It is recommended that responders more consistently collect on-scene arrival time for all agencies. Additionally, due to the high number of incidents that seem to be timing out and recording recovery times that are too long, it is recommended that improvements to the data collection process be made.

Because of the vital importance of the National Airspace System (NAS) and its diagnosed growth over the next years, the planning and prediction at a strategic phase of the Traffic Flow Management (TFM) proves to be a difficult task but a useful tool to reduce the airspace congestion. Research has led to the creation of several models in order to address this challenge. Because of the complexity of the problem, the Eulerian (aggregate) approach may be the best to reduce the dimension and complexity of the problem, whilst maintaining accuracy.This study analyzes one of the latest aggregate models created, the Route-Based Aggregate Model (RBAM), and compares it to the Large-Capacity Cell Transmission Model (CTM(L)) and the Link Transmission Model (LTM). These three models share some similarities such as the non-existence of diverging nodes or, in the case of the CTM(L), the condition of submitting all the airplanes in a cell to the next cell after one time-stepBut there are also big differences which make them different enough to coexist. For example, the RBAM can be used without the need of historical data in order to model the NAS, only the information of the upcoming flight plans. Also, the RBAM is designed to base its controls from a ground perspective, allowing ground rerouting and ground delay.An explanation on how to implement the RBAM in Matlab can be found in this project, explaining the peculiarities of the translation of the cost function constraints into a Linear Programming (LP) problem, with several examples that show how the solution to the LP problem distributes the delays between ground delays and ground reroutings. Because the cost of a ground rerouting is different from the cost of a ground delay because of the extra fuel expense that the rerouting may cause (assuming always that the original route is shorter), a proper weighting of both controls is found, considering different variables such as the cost of the fuel or the cost of overtime parking at the airports for the delayed aircraft. Future research will study how to define the alternative routes for the ground rerouting and also how to implement airborne rerouting.

The MEPDG requires a multitude of traffic inputs to be defined for the design of pavement structures. These inputs include (a) base-year traffic data such as the initial two-way annual average daily truck traffic (AADTT), (b) traffic volume adjustment factors (directional and lane distribution factors, vehicle class distribution, monthly adjustment factors, hourly truck distribution factors, and traffic growth factors), (c) axle load spectra by truck class (Class 4 to Class 13) and axle type (single, tandem, tridem, and quad), and (d) general traffic inputs (lateral truck traffic wander, number of axles per truck, axle configuration and wheelbase distributions, and tire characteristics and inflation pressure).Since it is not always practical to obtain site-specific traffic data, the MEPDG assimilates a hierarchal level concept that allows pavements to be designed using statewide averages and MEPDG default values without compromising the accuracy of the pavement design. In this study, a Visual Basic for Application (VBA) code was developed to analyze continuous traffic monitoring data and generate site-specific and statewide traffic inputs. The traffic monitoring data was collected by 143 permanent traffic monitoring sites (93 automated vehicle classifier (AVC) and 50 weigh-in-motion (WIM) sites) distributed throughout the State of Ohio from 2006 to 2011. The sensitivity of the MEPDG to the various traffic inputs was evaluated using two baseline pavement designs, one for a new flexible pavement and one for a new rigid pavement. Key performance parameters for the flexible pavement included longitudinal (top-down) fatigue cracking, alligator (bottom-up) fatigue cracking, transverse (low-temperature) cracking, rutting, and smoothness (expressed using IRI), while key performance parameters for the rigid pavement included transverse cracking (% slabs cracked), joint faulting, and smoothness.The sensitivity analysis results revealed that flexible pavements are moderately sensitive to AADTT, growth rate, vehicle class distribution, and axle load spectra; and not sensitive to hourly distribution factors, monthly adjustment factors, and number of axles per truck. Furthermore, it was found that rigid pavements are moderately sensitive to AADTT, growth rate, hourly distribution factors, vehicle class distribution, and axle load spectra; and not sensitive to monthly adjustment factors and number of axles per truck. Therefore, it is recommended to estimate the AADTT and the vehicle class distribution from site-specific short-term or continuous counts and obtain the truck growth rate from ODOT Modeling and Forecasting Section (Certified Traffic). As for the other traffic inputs, statewide averages can be used for the hourly distribution factors, axle load spectra, and number of axles per truck; and MEPDG defaults can be used for the monthly adjustment factors.

Mathematical models are a mathematical abstraction of the physical reality which is of great importance to understand the behavior of a system, make estimations and predictions and so on. They range from models based on physical laws to models learned empirically, as measurements are collected, and referred to as data-driven models. A model is based on a series of choices which influence its complexity and realism. These choices represent trade-offs between different competing objectives including interpretability, scalability, accuracy, adequation to the available data, robustness or computational complexity. The thesis investigates the advantages and disadvantages of models based on physical laws versus data-driven models through the example of signalized queuing networks such as urban transportation networks.The dynamics of conservation flow networks are accurately represented by a first order partial differential equation. Using Hamilton-Jacobi theory, the thesis underlines the importance to leverage physical laws to reconstruct missing information (\emph{e.g.} signal or bottleneck characteristics) and estimate the state of the network at any time and location. Noise and uncertainty in the measurements can be integrated in the model. When measurements are sparse, the state of the network cannot be estimated at every time and location on the network. Instead, the thesis shows how to leverage other characteristics, such as periodicity. From deterministic dynamics, the thesis derives the probability distribution functions of physical entities (\emph{e.g.} waiting time, density) by marginalizing the periodic variable. Using a Dynamic Bayesian Network formulation and exploiting the convexity structure of the system, the thesis shows how this modeling leads to realistic estimations and predictions, even when little measurements are available. Finally, the thesis investigates how sparse modeling and dimensionality reduction can provide insights on the large scale behavior of the network. Large scale dynamics and patterns are hard to model accurately based on physical laws. They can be discovered through data mining algorithms and integrated into physical models.

Background: According to data from the most recent Ohio Family Health Survey (2008), 15.4 percent of Ohio children have been told that they have asthma. Asthma-related school absenteeism has been linked with environmental factors in indoor settings, which may include schools.Objective: Recognizing the significant impact of asthma on Ohio’s families, the importance of the school environment, and the paucity of relevant air quality data for schools, this study developed an Asthma Risk Screening Indicators (ARSI) predictive model for school indoor air quality (IAQ) and asthma risk based on indoor and outdoor air pollution data.Methods: The outdoor air arm of the ARSI was derived from two extant databases of outdoor air toxic releases: the Risk-Screening Environmental Indicators (RSEI) model, which quantifies TRI emissions; and the 2005 National-Scale Air Toxics Assessments (NATA), which quantifies both industrial chemical emissions and risk due to traffic (onroad mobile sources). The indoor air component was closely modeled after recommended school inspection guidelines previously used in Ohio schools under Jarod’s Law and developed with assistance from a trained sanitarian. Indoor and outdoor air monitoring in 13 central Ohio elementary schools representing a range of ARSI scores was conducted to validate the two submodel components of the ARSI model. Allergens (n=8) in indoor settled dust were analyzed, and particulate matter (PM), black carbon (BC), and endotoxin over five days during each of two seasons, fall and winter or winter and spring, were also measured. Physician-diagnosed asthma (PDA) prevalence and respiratory morbidity were assessed by surveying fourth graders in 13 central Ohio elementary schools during the 2010-2011 school year using a validated questionnaire.The ARSI model indicators were compared to measured indoor and outdoor air pollution levels and to asthma prevalence and respiratory morbidity to test their predictive significance.Results: 10.2% of students surveyed in 13 schools reported PDA. An additional 37% without PDA reported symptoms consistent with asthma potentially suggestive of undiagnosed asthma. Of students with PDA, 76% reported symptoms suggestive of poorly controlled asthma. High levels of secondhand smoke (SHS) exposure were associated both with PDA (p=0.05) and with respiratory symptoms (p

The performances of intersections and driveway access points are crucial to a road network in terms of efficiency and safety. Driver behavior and traffic flow characteristics at these locations are relatively complex. To better understand these issues and potentially provide guidance to engineers in their designs, a series of studies were performed on the driver behavior and traffic characteristics at intersections and driveway access points based on field experiments or observations. First, a countdown timers study was performed in China about their influences on driver behavior. It was found that the presence of countdown timers may encourage yellow running behavior and late entry into intersections in China. Second, a phase gradient method was proposed for the general application purpose to the studies of driver behavior and traffic characteristics at signalized intersections. A case study on red-light cameras was performed at Knoxville, TN. Third, a study was performed to learn the legal issues and arguments about the usage of red-light cameras for the purpose of generating profits. A variety of engineering measures, mainly dealing with the setting of the traffic signal, which could be potentially used by municipalities or camera vendors to trap red-light runners and thus generating more revenues from the camera system are discussed. Finally, an experiment was conducted to simulate the right-turn issues, which impact the safety and operation efficiency at intersections or driveway access points. Two turn lane geometric parameters, angle-of-turn and tangent, and their influences on driver behavior and traffic flow characteristics were studied.

Throughout the field of transportation engineering, decision makers require quality information. The information used in transportation operations, planning, and design is based, in part, on data from traffic detectors. The need for quality data has spurred innovations in data collection including the introduction of modern, commercially available, non-intrusive traffic detectors. As these new technologies become available, a need exists to understand their capabilities and limitations—especially limitations that are unique to a specific region. This thesis examined the accuracy of four non-intrusive traffic detector technologies considered for potential data collection applications on Nebraska’s highways. The technologies evaluated included the Solo Pro II video image processor (VIP), 3M Canoga Microloop 702 magnetic induction detector, Image Sensing Systems RTMS G4 microwave radar detector, and Wavetronix SmartSensor 105 microwave radar detector. These four detectors were installed at the NTC/NDOR non-intrusive detector test bed along Interstate 80 near the Giles Road interchange in Omaha, Nebraska. Data were collected in June, July, and August of 2011, and these detectors were analyzed based on the accuracy of their volume, speed, and length-based vehicle classification. The analysis in this thesis utilizes numerous graphical and statistical methods to demonstrate the significance of errors in the data from the four evaluated detectors. The impacts of lighting, rain, traffic volume, and various levels of temporal aggregation on the detectors’ accuracies were analyzed. Multiple regression analysis revealed that the volume accuracy of the Solo Pro II was affected by night lighting, as well as by the combined effect of dawn lighting and rain. The volume accuracies of the Microloop 702 and G4 were significantly affected by the combination of dusk lighting and rain, while the volume accuracy of the SmartSensor 105 was not found to be significantly affected by lighting or rain conditions. In addition to these results, this thesis analyzed the collected data in order to provide hypotheses pertaining to potential links between significant environmental factors and physical operating characteristics of the evaluated non-intrusive traffic detectors.