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Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair while also aiming to reduce congestion through the implementation of different traffic control and demand management strategies. These strategies can result in changes in traffic volume distributions, which in turn affect the level of pavement deterioration due to traffic loading. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with a modified cost function taking care of the dynamic pricing along the I-66 tolled facility. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process. Doctor of Philosophy Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair. Improvement in pavement rehabilitation plans can lead to savings in the order of tens of millions of dollars. Pavement rehabilitation plans result in work zone schedules on the transportation network. Limited roadway capacities due to work zones affect traffic assignments and routing on the roads, which impacts the selection of optimal operation strategies to manage the resulting traffic. On the other hand, the choice of any particular operation and routing strategy will result in different distributions of traffic volumes on the roads and affect the pavement deterioration levels due to traffic loading, leading to other optimal rehabilitation plans and corresponding work zones. While there have been several research efforts on infrastructure condition assessment and other research efforts on traffic control and demand management strategies, there is a wide gap in the nexus of the two fields. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process.

This research aims to improve the design and placement of runway exits at airports through analysis and modeling of the effects that exogenous causal factors have on pilots' landing behavior and exit selections. Incorporating these factors into modeling software will strengthen the software's utility by providing project teams the ability to specify which pilot motivational causal factors apply to a new or existing runway. The main findings suggest pilots' exit selections are deterministic but dependent on the presence (or absence) of six (6) causal factors. A model and two (2) case studies are presented and compared against predictions generated by existing modeling software. The results support a finding that the causal factor model improves motivation-based predictions over current modeling techniques, which are drawn from stochastic distributions. The accuracy of this model enables designers to optimize runway exit placement and geometry to maximize runway capacity. Master of Science Airport design engineers currently plan the locations and geometric characteristics of runway exits by balancing the expected fleet mix of aircraft on that runway with the capacity and delay effects that the number and placement of these exits might cause. This technique originated from research beginning in the early 1970s, which found that pilots' exit motivations primarily resulted from the capabilities and limitations of their aircraft. Since pilots tend to "fly by the numbers" (i.e., exhibit predictable approach airspeeds, power levels, wing flaps, touchdown locations, landing speeds, and braking efforts), engineers thus employed design principles in which the numbers, locations and geometries of exits were primarily functions of the physical and performance-based characteristics of the specific types of aircraft expected to utilize the runway. However, in studying more than 4 million landings by a single aircraft type (the Boeing 737-800) at 42 U.S. airports, the evidence in this thesis shows that pilots' exit selections are behaviorally motivated by more than the physics of motion. This thesis aims to refine previous research and engineering methods by showing evidence that pilots' exit selections have as much to do with the presence (or absence) of certain environmental factors within the landing system. These factors (described in detailed within) are unique to each airport's overall physical network of interconnected runways, exits, taxiways, terminals and other features. Within this network, a pilot's landing behavior and exit selection depends on the locational and relational interactions that each exit choice will have on the time and distance to their apron (gate) assignment. These "interactions" are referred to as causal factors – defined as physical features within a landing environment that pilots have little-to-no control over – but which nevertheless influence their specific exit selections. Two (2) runway case studies provided in this thesis evidence a finding that a causal factor model reliably predicts pilots' exit selections better than current modeling techniques, which are drawn from probability-based statistical distributions. The stability and accuracy of the new model enables engineering design and project teams to optimize runway exit placement and geometry to maximize runway capacity, and can be adopted for use in both existing and future runways.

This thesis estimates potential runway throughput gains using a reduced wake separation based on the 123 most prevalent aircraft in the United States fleet. The analysis considers Runway Occupancy Time (ROT) constraint factors and existing geometric design factors. This research extracts the historic data from Airport Surface Detection Equipment Model X (ASDE-X) for analysis. The Runway Exit Design Interactive Model (REDIM) is used to optimize the runway exit locations and reduce ROT. The runway throughput and safety factors are generated from a Monte Carlo runway simulator. This thesis focuses on selected US airport runways that could benefit from geometric optimization. The study aims to estimate ROT improvements through improved runway exit locations and the changes in runway throughput considering ROT constraint factors. The results of the thesis show that Dallas Fort Worth International Airport (DFW) runway 35C and Denver International Airport (DEN) runway 16R have the potential to improve the ROT. After the optimization to locate runway exits, the ROT time of the RECAT group F and G aircraft (greater than 90% of the arrivals) was reduced by three to five seconds (a very significant effect). After the ROT reductions and with the application of reduced wake separation criteria with the ROT constraint factor applied, the arrival-only capacity of DFW runway 35C improved by 3.5 arrivals per hour. The arrival-only capacity on DEN runway 16R improved by 2.14 arrivals per hour. Both runways maintained a probability of violation between time-based separation and ROT time at around 1.5%. The study concludes that the application of reduced wake separation criteria alone is a necessary but insufficient condition to improve the efficiency of arrival runways. Through careful improvements of runway exit locations, reductions in ROT provide reliability and efficiency to the operation of runways. Master of Science This thesis estimates potential runway throughput gains using a reduced wake separation based on the 123 most prevalent aircraft in the United States fleet. The analysis considers Runway Occupancy Time (ROT) constraint factors and existing geometric design factors. This research extracts the historic data from Airport Surface Detection Equipment Model X (ASDE-X) for analysis. The Runway Exit Design Interactive Model (REDIM) is used to optimize the runway exit locations and reduce ROT. The runway throughput and safety factors are generated from a Monte Carlo runway simulator. This thesis focuses on selected US airport runways that could benefit from geometric optimization. The study aims to estimate ROT improvements through improved runway exit locations and the changes in runway throughput considering ROT constraint factors. The results of the thesis show that Dallas Fort Worth International Airport (DFW) runway 35C and Denver International Airport (DEN) runway 16R have the potential to improve the ROT. After the optimization to locate runway exits, the ROT time of the RECAT group F and G aircraft (greater than 90% of the arrivals) was reduced by three to five seconds (a very significant effect). After the ROT reductions and with the application of reduced wake separation criteria with the ROT constraint factor applied, the arrival-only capacity of DFW runway 35C improved by 3.5 arrivals per hour. The arrival-only capacity on DEN runway 16R improved by 2.14 arrivals per hour. Both runways maintained a probability of violation between time-based separation and ROT time at around 1.5%. The study concludes that the application of reduced wake separation criteria alone is a necessary but insufficient condition to improve the efficiency of arrival runways. Through careful improvements of runway exit locations, reductions in ROT provide reliability and efficiency to the operation of runways.

This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and environmental impact of the Urban Air Mobility (UAM) or Advanced Air Mobility (AAM). UAM is a concept aerial transportation mode designed for intracity transport of passengers and cargo utilizing autonomous (or piloted) electric vehicles capable of Vertical Take-Off and Landing (VTOL) from dense and congested areas. While the industry is preparing to introduce this revolutionary mode in urban areas, realizing the scope and understanding the factors affecting the attractiveness of this mode is essential. The success of UAM depends on its operational efficiency and the relative utility it offers to current travelers. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior using revealed preference data and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. Chapter II presents a methodology to estimate commuter demand for UAM operations in the Northern California region. A mode-choice model is calibrated from the commuter mode-choice behavior observed in the survey data. An integrated demand estimation framework is developed utilizing the calibrated mode-choice model to estimate UAM demand and place vertiports. The feasibility of commuter UAM operations in Northern California is further analyzed through a series of sensitivity analyses. This study was published in Transportation Research Part A: Policy and Practice journal. In an effort to analyze the feasibility of UAM operations in different use cases, demand estimation frameworks are developed to estimate UAM demand in the airport access trips segment. Chapter III and Chapter IV focus on developing the UAM Concept of Operations (ConOps) and demand estimation methodology for airport access trips to Dallas-Fort Worth International Airport (DFW)/Dallas Love Field Airport (DAL) and Los Angeles International Airport (LAX), respectively. Both studies utilize the latest available originating passenger survey data to understand arriving passengers' mode-choice behavior at the airport. Mode-choice conditional logit models are calibrated from the survey data, further used to estimate UAM demand. The former study is published in the AIAA Aviation 2021 Conference proceeding, and the latter is published in ICNS 2021 Conference proceedings. UAM vertiport capacity may be a barrier to the scalability of UAM operations. A heavy concentration of UAM demand is observed in specific areas such as Central Business Districts (CBD) during the spatial analysis of estimated UAM demand. However, vertiport size could be limited due to land availability and high infrastructure costs in CBDs. Therefore, operational efficiency is critical for capturing maximum UAM demand with limited vertiport size. The study included in Chapter V focuses on analyzing factors impacting vertiport capacity. A discrete-event simulation model is developed to simulate a full day of commuter operations at the San Francisco Financial District's busiest vertiport. Besides calculating the capacity of different fundamental vertiport designs, sensitivity analyses are carried to understand the impact of several assumptions such as service time at landing pads, service time at parking stall, charging rate, etc. The study explores the importance of pre-positioning UAM vehicles during the time of imbalance between arrival and departure requests. This study is published in ICNS 2021 Conference proceedings. Community annoyance from aviation noise has often been a reason for limiting commercial operations at several major airports globally. Busy airports are located in urban areas with high population densities where noise levels in nearby communities could govern capacity constraints. Commercial aviation noise is only a concern during landing and take-offs. Hence, the impact is limited to communities close to the airport. However, UAM vehicles would be operated at much lower altitudes and have more frequent taking-off and landing operations. Since the UAM operations would mostly be over dense urban spaces, the noise potential is significantly high. Chapter VI includes a study on preliminary estimation of noise levels from commuter UAM operations in Northern California and the Dallas-Fort Worth region. This study is published in the AIAA Aviation 2021 Conference proceedings. The final chapter in this dissertation explores the impact of airspace restrictions on UAM demand potential in New York City. Integration of UAM operations in the current National Airspace System (NAS) has been recognized as critical in developing the UAM ecosystem. Several pieces of urban airspace are currently controlled by Air Traffic Control (ATC), where commercial operation density is high. Even though the initial operations are expected to be controlled by the current ATC, the extent to which UAM operations would be allowed in the controlled spaces is still unclear. As the UAM system matures and the ecosystem evolves, integrating UAM traffic with other airspace management might relax certain airspace restrictions. Relaxation of airspace restrictions could increase the attractiveness of UAM due to a decrease in travel time/cost and relatively more optimal placement of vertiports. Quantifying the impact of different levels of airspace restrictions requires an integrated framework that can capture utility changes for UAM under different operational ConOps. This analysis uses a calibrated mode-choice model, restriction-sensitive vertiport placement methodology, and demand estimation process. This study has been submitted for ICNS 2022 Conference. Doctor of Philosophy Urban Air Mobility (UAM) or Advanced Air Mobility (AAM) are concept transportation modes currently in development. It proposes transporting passengers and cargo in urban areas using all-electric Vertical Take-Off and Landing (eVTOL) vehicles. UAM is a multi-modal concept involving low-altitude aerial transport. The high capital costs involved in developing vehicles and infrastructure suggests the need for meticulous planning and strong strategy development in the rolling out of UAM. Moreover, urban travelers are relatively more sensitive to travel time savings and travel time reliability; therefore, the efficiency of UAM is critical for its success. This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and the environmental impact of UAM. To estimate the potential for UAM, we need first to understand the mode-choice making behavior of urban travelers and then estimate the relative utility UAM could possibly offer. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. The system planners would need to know the individual or combined effect of various parameters in the system, such as cost of UAM, network size of UAM, etc., on UAM potential. Therefore, sensitivity analyses with respect to UAM demand are performed against various framework parameters. Capacity constraints are not initially considered for potential demand estimation. However, like any other transportation mode, UAM could suffer from capacity issues that can cause operational delays. A simulation study is dedicated to model UAM operations at a vertiport and estimating factors affecting vertiport capacity. After observing the demand potential for certain optimistic scenarios, we realized the possibility of a large number of low-flying vehicles, which could cause annoyance and environmental impacts. Therefore, the following study focuses on developing a noise estimation framework from a full-day of UAM operations and estimating a highly annoyed population in the Bay Area and Dallas-Fort Worth Region. In our studies, modeling restricted airspaces (due to commercial operations at large airports) was always a critical part of the analysis. The urban airspaces are already quite congested in some urban areas, and we assumed that UAM would not operate in the restricted airspaces. The last study in this dissertation focuses on quantifying the impact of different levels of airspace restrictions on UAM demand potential in New York. It would help system planners gauge the level of integration required between the UAM and National Airspace System (NAS).

Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The final product of this study was the development of the Low-Boom Supersonic Aircraft Model (LBSAM). The development progress took three years to be completed, and during each year, a version of the model with the preliminary predictions was made available to NASA. Each of the three versions of the model predicts future supersonic commercial services. What differentiates each version is the data, method, and aircraft type/design implemented; the latest version of the model is more realistic and provides a higher number of functionalities. The first version of the model predicted the possible supersonic commercial service for three aircraft types: each with two variations. An 18-seat, 40-seat, and 60-seat low-boom and non-low-boom aircraft were analyzed. The second version of the model analyzed a 20-seat and 40-seat low-boom, non-low-boom aircraft with restrictions and non-low-boom aircraft without restrictions. The latest version of the model tries to estimate potential demand for a 43-seat and a 52-seat supersonic low-boom aircraft design. The low-boom concept refers to the implementation of technology that reduces the loudness of a sonic boom. A non-low-boom concept refers to an aircraft flying faster than Mach 1 with the technology's implementation that reduces the loudness of a sonic boom. The final results suggest that for a 52-seat LBSA, the potential worldwide demand is as follows. • 33.4 million seats worldwide. Assuming an overland range of 3,200 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.98. • 772 aircraft needed worldwide. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. • 1,032 one-way OD pairs where LBSA can operate. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. The LBSAM is mainly driven by the cost per passenger mile values calculated for each one-way Origin-Destination (OD) pair. Additional uncertainties in the model include the market share and annual aircraft utilization. The market share refers to the percent of the demand that will switch from current subsonic commercial services to commercial supersonic services. During the three-year work, we considered a market share of 50% and 100%. Aircraft utilization refers to the number of hours that the airline will be able to use the aircraft. The majority of the projections were based on a 3,500-hour aircraft utilization. Doctor of Philosophy Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. An aircraft flying faster than the speed of sound is known as an aircraft flying at supersonic speed. Current commercial aircraft fly at subsonic speed. Subsonic speed refers to aircraft flying at a speed lower than the speed of sound. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The purpose of this dissertation effort is to provide a better understanding of what could be the potential commercial demand for supersonic flight in the near future. We consider all the benefits and characteristics of supersonic flight and studied in detail what percentage of the travelers might be willing to migrate from the current subsonic market to the supersonic market. We estimated this ratio by studying the spending behavior of passengers in the current market. How much more are passengers willing to pay to save time? We can infer how much travelers value their time by comparing direct flights versus flights with an intermediate stop. The results show that a demand of 33.4 million seats could be reached by the year 2040. The supersonic market would consist of more than one thousand one-way origin-destination pairs worldwide, and more than seven hundred supersonic aircraft are expected to satisfy the forecast demand.

Vehicle recognition and trajectory tracking plays an integral role in many aspects of Intelligent Transportation Systems (ITS) applications; from behavioral modeling and car-following analyses to congestion prevention, crash prediction, dynamic signal timing, and active traffic management. This dissertation aims to improve the tasks of multi-object detection and tracking (MOT) as it pertains to urban traffic by utilizing the domain knowledge of traffic flow then utilize this improvement for applications in real-time traffic performance assessment, safety evaluation, and driver behavior modeling. First, the author proposes an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. This framework introduces the concept of virtual traffic lanes representing the eight standard National Electrical Manufacturers Association (NEMA) movements within an intersection as spatio-temporal clusters utilized for movement classification and vehicle re-identification. The proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation. The results obtained for a case study and on the AI City benchmark dataset indicate the high ability of the proposed framework in obtaining reliable turn count, speed estimates, and efficiently resolving the vehicle identity switches which occur within the intersection due to detection errors and occlusion. The author then proposes the utilization of the high accuracy and granularity trajectories obtained from video inference to develop a real-time safety-based driver behavior model, which managed to effectively capture the observed driving behavior in the site of study. Finally, the developed model was implemented as an external driver model in VISSIM and managed to reproduce the observed behavior and safety conflicts in simulation, providing an effective decision-support tool to identify appropriate safety interventions that would mitigate those conflicts. The work presented in this dissertation provides an efficient end-to-end framework and blueprint for trajectory extraction from road-side traffic video data, driver behavior modeling, and their applications for real-time traffic performance and safety assessment, as well as improved modeling of safety interventions via microscopic simulation. Doctor of Philosophy Traffic crashes are one of the leading causes of death in the world, averaging over 3,000 deaths per day according to the World Health Organization. In the United States alone, there are around 40,000 traffic fatalities annually. Approximately, 21.5% of all traffic fatalities occur due to intersection-related crashes. Intelligent Transportation Systems (ITS) is a field of traffic engineering that aims to transform traffic systems to make safer, more coordinated, and 'smarter' use of transport networks. Vehicle recognition and trajectory tracking, the process of identifying a specific vehicle's movement through time and space, plays an integral role in many aspects of ITS applications; from understanding how people drive and modeling that behavior, to congestion prevention, on-board crash avoidance systems, adaptive signal timing, and active traffic management. This dissertation aims to bridge the gaps in the application of ITS, computer vision, and traffic flow theory and create tools that will aid in evaluating and proactively addressing traffic safety concerns at urban intersections. The author presents an efficient, real-time framework for extracting reliable vehicle trajectories from roadside cameras, then proposes a safety-based driving behavior model that succeeds in capturing the observed driving behavior. This work is concluded by implementing this model in simulation software to replicate the existing safety concerns for an area of study, allowing practitioners to accurately model the existing safety conflicts and evaluate the different operation and safety interventions that would best mitigate them to proactively prevent crashes.

The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways. Doctor of Philosophy The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

Previous studies show that pavement condition properties are significant factors to enhance road safety and riding experience, and pavements with low quality might have inadequate performance in terms of safety and riding experience. Pavement Management System (PMS) databases include pavement properties for each segment of the road collected by the agencies. Understanding the impact of road characteristics on crash frequency is a key step to prevent crashes. Whereas other studies analyzed the effect of different characteristics such as International Roughness Index (IRI), Rutting Depth (RD), Annual Average Daily Traffic (AADT), this thesis analyzed the effect of Critical Condition Index (CCI) on crash frequency, in addition to the other factors identified in previous studies. Other characteristics such as Percentage of Heavy Vehicles, Road Surface Condition, Road Lighting Condition, and Driver Conditions are taken into the consideration. The scope of the study is the interstate highway system in Fairfax County, Virginia. Negative Binomial, Least Square and Nominal Logistic Models were developed, showing that the CCI value is a significant factor to predict the number of crashes, and that it has different effect for different values of AADT. The result of this study is a substantial step towards developing an integrated transportation control and infrastructure management framework. Master of Science Many factors cause crashes in the roads. Although there is a common sense that road characteristics such as asphalt quality are important in terms of road safety, there are few studies that scientifically prove that statement. In addition, asphalt maintenance decisions making process is mainly based on cost benefit optimization, and traffic safety is not considered at the process. The purpose of this study is to analyze crashes and road characteristics related to each crash to understand the effect of those characteristics on crash frequency, and eventually, to build a model to predict the number of crashes at each part of the road. The model can help transportation agencies to have a better understanding in terms of safety consequences of their infrastructure management plans. The scope of this study is the highway interstate system in Northern Virginia. Results suggest that pavement condition has a significant impact on crash frequency.

The Federal Aviation Administration (FAA) has been modernizing the United States' air transportation system within a series of initiatives called the Next Generation Air Transportation System (NextGen). The goal of NextGen is to increase the safety, efficiency, capacity, predictability, and resiliency of American Air Traffic Control (ATC) by implementing satellite-based communication, and navigation systems. Because of the vast oceanic areas controlled by Oakland, New York, and Anchorage air traffic control centers, improving oceanic operations is significant for the United States. According to the FAA, oceanic flights generate 31% of passenger revenue and 40% of cargo revenue in U.S.-controlled airspace. New NextGen procedures offer the opportunity for aircraft to save fuel consumption by allowing oceanic flights to fly at more efficient routes and flight levels. This dissertation investigates three areas to improve flight operations over oceanic airspace. The first area studies the operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace. This research effort uses two approaches as follows: 1) statistical flight analysis, and 2) simulation-based analysis. The second area provides an optimization technique to improve the current procedures for assigning flights to the Organized Track System (OTS) in the Atlantic Ocean based on the Collaborative Decision Making (CDM) concept. The third area investigates the potential savings of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. To quantify the operational benefits of the proposed procedures, a fast-time simulation tool, the Global Oceanic (GO) model, is developed and employed. The GO model is a microscopic flight simulation tool that has been developing by the Air Transportation Systems Laboratory at Virginia Tech offering realistic and inexpensive evaluations of novel technologies and procedures to improve flight operations over global oceanic airspace. the results of these studies are analyzed in terms of fuel consumption, travel distance, travel time, level of service, and potential air traffic controllers' workload. Doctor of Philosophy The economic growth and social connectivity of nations are highly correlated to effective and efficient air transportation systems. The Federal Aviation Administration (FAA) has initiated a program to modernize America's air transportation system and make flight operations safer, and more efficient. This program is called the Next Generation Air Transportation System (NextGen) and its goal is transforming the communication and navigation technologies to satellite-based systems. Improving oceanic flights is one of the main concerns of the NextGen program since the United States controls massive oceanic areas in the Atlantic and the Pacific Ocean. The FAA needs to evaluate the benefits and costs of advanced technologies and procedures to justify the NextGen initiatives. The FAA has employed computer simulation tools to support decisions for future infrastructure investments and encourage airlines to equip their aircraft with more advanced avionics. The Global Oceanic (GO) model is a microscopic flight simulation tool developed jointly by the Air Transportation Systems Laboratory at Virginia Tech and the FAA providing quick, realistic, and inexpensive evaluations of advanced procedures to improve flight operations over oceans. This dissertation investigates the operational benefit of three advanced procedures using the GO model. The areas to improve flight operations over oceanic airspace are as follows: 1) operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace, 2) operational benefits of an optimization technique for flight assignments to the Organized Track System (OTS) in the Atlantic Ocean, 3) operational benefits of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. These studies quantify the potential savings of these procedures in terms of reducing fuel consumption, travel distance, travel time, greenhouse gas emissions, and potential air traffic controllers' workload.

Road administrators have to make decisions regarding the maintenance and rehabilitation of many existing jointed Portland Cement Concrete (PCC) pavements in the road network. Since these pavements are in general expensive to rehabilitate, agencies often opt for overlaying the deteriorated PCC pavement with Hot Mix Asphalt (HMA), resulting in a composite pavement. Unfortunately, the tensile stresses and strains at the bottom of the overlay developed from the movement of the joints, which are caused by the traffic and the changes in temperature, will create cracks on the surface known as reflective cracking. Reflective cracking can reduce the life of a pavement by allowing water or other particles to get into the underlying layers, which causes the pavement structure to lose strength. To improve the performance of the composite pavement, road agencies have studied mitigations techniques to delay the initiation and propagation of those cracks reflected from the PCC joints and cracks. Traditionally, these studies have relied only on laboratory testing or nondestructive tests. This dissertation expands the traditional approach by adding full-scale Accelerate Pavement Testing (APT) to a laboratory effort to investigate enhanced asphalt overlays that delay the initiation and propagation of cracks reflected from the PCC joints. The study was organized into three complementary experiments. The first experiment included the first reflective cracking study of hot-mix asphalt (HMA) overlays over jointed Portland cement concrete pavements (PCCP) conducted at the Virginia APT facility. A Heavy Vehicle Simulator (HVS) was used to compare the reflective cracking performance of a Stone Matrix Asphalt (SMA) control mix with a modified mix with a synthetic fiber. The discussion includes the characterization of the asphalt mixes, the pavement structure, construction layout, the equipment used, the instrumentation installed, and lessons learned. Results showed that the fiber-modified mix had a higher resistance to fracture, which increases the pavement life by approximately 50%. The second experiment compared the cracking resistance of the same control and modified mixes in the laboratory. Four cracking resistance tests were performed on each mix. These four tests are: (1) Indirect Tensile Asphalt Cracking Test (IDEAL-CT), which measures the Cracking Test index (CTindex); (2) Semicircular Bend Test-Illinois (SCB-IL), which measures the critical strain energy release rate (Jc); (3) Semicircular Bend-Louisiana Transportation Research Center (SCB-LTRC), which measures the Flexibility Index (FI); and (4) Overlay Test (OT), which measures the Cracking Propagation Rate (CPR). The results from the four tests showed that the fiber-modified mix had a better resistance to cracking, confirming the APT test results. The laboratory assessment also suggested that the IDEAL-CT and SCB-IL test appear to be the most practical for implementation. The third phase evaluated the performance of mixes designed with a high content of Reclaimed Asphalt Pavement (RAP) and an enhanced asphalt-rubber extender, which comprises three primary components: plain soft bitumen, fine crumb rubber and an Activated Mineral Binder Stabilizer (AMBS). The experiment evaluated the fracture resistance of nine mixes designed with different rates of recycled asphalt pavement (RAP) and asphalt-rubber, compare them with a traditional mix, and propose an optimized mixture for use in overlays of concrete pavements. The mixes were designed with different rates of RAP (15, 30, 45%) and asphalt-rubber extender (0, 30, and 45%) following generally, the design requirements for an SMA mix in Virginia. The laboratory test recommended in the second experiment, IDEAL-CT and SCB-IL, were used to determine the fracture resistance of the mixes. The results showed that the addition of RAP decreases fracture resistance, but the asphalt-rubber extender improves it. A mix designed that replaced 30% of the binder with asphalt-rubber extender and 15% RAP had the highest resistance to fracture according to both. Also, as expected, all the mixed had a low susceptibility to rutting. Doctor of Philosophy Reflective cracking can reduce the life of a pavement by allowing water or other particles to get into the underlying layers, which causes the pavement structure to lose strength. To improve the performance of the composite pavement, road agencies have studied mitigations techniques that will delay the initiation and propagation of those cracks reflected from the PCC joints. Traditionally, these studies rely only on laboratory testing or nondestructive tests that will assist in the decision-making stage in a short time manner. This dissertation focusses on a reflective cracking study conducted through Accelerate Pavement Testing (APT) using a Heavy Vehicle Simulator (HVS) and laboratory testing. The first task used an HVS to evaluate reflective cracking of a Stone Matrix Asphalt (SMA) control mix and a modified mix with synthetic fiber. One lane was constructed with two layers of 1.5-inches of a control Stone Matrix Asphalt (SMA) mix and the second lane with an SMA mix modified with the synthetic fiber. Results from APT demonstrated that the modified SMA has a higher resistance to fracture which increases the pavement life by approximately 50%. The second task estimated the fracture resistance of the mixes studied in task one following the laboratory test: Indirect Tension Asphalt Cracking Test (IDEAL-CT), Texas Overlay Test (OT), Semi-Circular Bend-Louisiana Transportation Research Center (SCB-LTRC) and Semi-Circular Bend-Illinois (SCB-IL) to estimate the Cracking Test Index (CTindex), Cracking Propagation Rate (CPR), critical strain energy release rate (Jc) and Flexibility Index (FI), respectively. Results showed that the modified mix had a better resistance to cracking, confirming the APT test results. Specifically, CTindex results showed that the modified mix is more resistant than the control, with indices of 268.72 and 67.86. The estimated Jc indicated that less energy is required to initiate a crack for the control mix that achieved 0.48 kJ/m2 compared to the modified mix with synthetic fibers 0.54 kJ/m2. FI results for the control and fibers were 2.16 and 10.71, respectively. The calculated CPR showed that the control mix propagates a crack at a higher rate of 0.188 compared to the modified mix with a CPR of 0.152. The third phase evaluated the performance of mixes designed with a high content of Reclaimed Asphalt Pavement (RAP) and an enhanced asphalt-rubber extender, which comprises three primary components: plain soft bitumen, fine crumb rubber and an Activated Mineral Binder Stabilizer (AMBS). The experiment evaluated the fracture resistance of nine mixes designed with different rates of recycled asphalt pavement (RAP) and asphalt-rubber, compare them with a traditional mix, and propose an optimized mixture for use in overlays of concrete pavements. The mixes were designed with different rates of RAP (15, 30, 45%) and asphalt-rubber extender (0, 30, and 45%) following generally, the design requirements for an SMA mix in Virginia. The laboratory test recommended in the second experiment, IDEAL-CT and SCB-IL, were used to determine the fracture resistance of the mixes. The results showed that the addition of RAP decreases fracture resistance, but the asphalt-rubber extender improves it. A mix designed that replaced 30% of the binder with asphalt-rubber extender and 15% RAP had the highest resistance to fracture according to both. Also, as expected, all the mixed had a low susceptibility to rutting.

This dissertation looks at the relationship between airport size (e.g. small, medium, large) and air service continuity and operational performance. It consists of three studies, each written in journal format. The first study analyzes the markets served pre- and post-recession while focusing on the operational strategies adopted by the top Major Carriers and Low-Cost Carriers (LCCs) in the United States. Findings show that LCCs have outpaced major carriers in terms of expanding their network and the number of markets served. During the same time, major carriers have gained a greater flight share in the markets they already serve. Post-recession, LCCs have shown preference to competing with major carriers over other LCCs. The second study investigates the declining service levels at small airports compared to large-hub airports, which continue to benefit from higher levels of service and increased airline presence. Using a fixed-effects conditional logistic regression, this study looked at factors contributing to service loss in region-to-region markets serving small communities between 2007 and 2013. Results show that 1) markets affected by a merger are indeed at a higher risk of losing service; 2) markets that are operated by a fuel-intensive, small-aircraft fleet have a higher chance to be discontinued and 3) an increased number of competitors greatly reduces potential market service loss. The third and final study proposes a new methodology to calculate original delay and propagated delays using combined aviation operational datasets that provide detailed flight information and causal factors behind delays. In addition to calculating original and propagated delay for the month of July of 2018, this study differentiated between original delays that occur during the turnaround phase, taxiing phase and en-route and incorporates causal factor information to identify the true source behind propagated delay. Two fixed-effects linear regression models were introduced that predict Total Propagated Delay and the share of propagated delay given an airport's ability to absorb upstream delay during the turnaround phase. Results show that most delay propagation chains originate at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover (i.e. least number of downstream flight legs affected) and large-hub airports have a higher ability to absorb delay at the turnaround phase compared to smaller airports given the significantly higher schedule buffer time airlines plan at large-hub airports. Doctor of Philosophy The changing nature of the air service industry is dependent on several key factors, including but not limited to the major and low-cost airlines, the frequency of service at different sized-airports and the operational performance of the airports in the system. Each airport can be classified by size based on the annual number of enplanements. This dissertation looks at the relationship between airport size (e.g. small, medium, large), service continuity and operational performance. It consists of three studies, each written in journal format. Over the past two decades, the U.S. air transportation network witnessed several economic downturns forcing airlines to shift their operational strategies, cease service or merge with an airline counterpart. The first study analyzes routes served before and after the recession by exploring the presence of major and low-cost carriers in these markets to understand how several economic downturns have influenced the operating strategy of airlines in the US. While Low-cost carriers focused on expanding their network and offering service in an increased number of new routes, major carriers increased their presence in the markets in which they already serve. Furthermore, after the recession, low-cost carriers chose to increasingly compete with major carriers over their low-cost counterparts. The second study explored the factors that can potentially contribute to the loss of service in routes serving small communities. While airlines continue to compete on the most profitable routes, small airports recently suffered from reduced service levels and in some instance service discontinuity. Results show that 1) routes that were once served by two airlines that merged are at a higher risk of losing service; 2) routes that are operated by a fuel-intensive small aircraft fleet have a higher chance to be discontinued and 3) an increased presence of airlines competing in a route greatly reduces potential service loss. In addition to evaluating service continuity, the third and final study looks at flight delays across the US and dives into the effect of airport size on propagated delay. Delays on a flight can be caused by inefficiencies and capacity restrictions at airports and may also be the result of delay that happen earlier in the day and that propagates to multiple flights downstream that share the same resources. That is, a delay can affect multiple flights whenever these flights are all operated by the same aircraft equipment. Costing the air transportation network billions of dollars annually, the third study examines the original and propagated delays at US airports by collecting data from multiple sources to incorporate the original source and cause of delay. Results show that most delay originates at large-hub airports and are mostly concentrated at airports within the same geographical area. However, delays originating at large-hub airports were found to be the quickest to recover and large-hub airports have a higher ability to absorb delay at the turn compared to smaller airports as airlines allocate additional minutes of schedule padding at large-hub airports.

This thesis presents methods to place landing sites for the Urban Air Mobility (UAM) concept. The analysis shows an integrated approach to establish UAM landing site requirements, place landing sites based on predicted demand, and estimate the costs associated with UAM landing sites. This thesis also makes estimates of fares associated with UAM operations. The methods presented are applied to three large urban centers in the United States. The analysis assumes an all-electric, advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities to estimate UAM landing site requirements. The thesis presents the land area requirements of UAM landing sites using Federal Aviation Administration heliport design criteria considering ground-taxi configurations. The analysis performed employs a UAM vehicle with an equivalent Rotor Diameter (RD) of 43 feet. In this thesis, UAM demand is estimated using a mode choice model developed in a companion study (UAM Scenario Analysis). The methodology to locate UAM landing sites includes splitting and consolidation of UAM landing sites considering the Zillow Transaction and Assessment Dataset (ZTRAX) to introduce land-use size and cost constraints. The sites are split using a K-Means clustering method and are consolidated using a simple center of mass approach where the demand of each landing site is analogous to mass. The results presented in this thesis apply to 75 and 200 landing sites in each region and assume passenger Cost-Per-Mile (CPM) of $1.20 and $1.80, respectively. This thesis presents a comparative study on how the availability of land affects the splitting, consolidation, and relocation of UAM landing sites for each region, the number of landing sites, and the cost per passenger-mile. Master of Science This thesis aims at the landing sites for the Urban Air Mobility (UAM) concept for commuting passengers in Northern California (17 counties), Southern California (9 counties), and Dallas-Fort Worth (12 counties) region. The aircraft for this service is designed to be an all-electric advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities. The commuting trips considered is focused on passengers traveling to work from home and back. This thesis presents the land area requirements of these landing sites, which are calculated from the Federal Aviation Administration's (FAA) Advisory Circular 150/5390-2C using ground-taxi configuration for a typical representative aircraft of an equivalent rotor diameter (RD) of 43 feet. The landing sites are then split into smaller sites and consolidated into larger sites. This thesis also presents a list of plots of land located within the 0.5 statute-mile boundaries of the landing sites for relocation. This entire analysis is based on the availability of land from the Zillow Transaction and Assessment Dataset (ZTRAX). The results presented in this thesis are for 75 and 200 landing sites set in the study area for a passenger Cost-Per-Mile (CPM) of $1.2 and $1.8, respectively. The results show how the availability of land changes for different CPM for a set of landing sites and affects the splitting, consolidation, and relocation of landing sites for each region. In the end, the thesis presents conclusions and recommendations unique to each region.