Doctoral Dissertations

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https://hdl.handle.net/10919/11041

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Airport Performance Metrics Analysis: Application to Terminal Airspace, Deicing, and Throughput
Alsalous, Osama (Virginia Tech, 2022-06-08)
The Federal Aviation Administration (FAA) is continuously assessing the operational performance of the National Airspace System (NAS), where they analyze trends in the aviation industry to help develop strategies for a more efficient air transportation system. To measure the performance of various elements of the aviation system, the FAA and the International Civil Aviation Organization (ICAO) developed nineteen key performance indicators (KPIs). This dissertation contains three research studies, each written in journal format, addressing select KPIs. These studies aim at answering questions that help understand and improve different aspects of airport operational efficiency. In the first study, we model the flight times within the terminal airspace and compare our results with the baseline methodology that the FAA uses for benchmarking. In the second study, we analyze the efficiency of deicing operations at Chicago O'Hare (ORD) by developing an algorithm that analyzes radar data. We also use a simulation model to calculate potential improvements in the deicing operations. Lastly, we present our results of a clustering analysis surrounding the response of airports to demand and capacity changes during the COVID-19 pandemic. The findings of these studies add to literature by providing a methodology that predicts travel times within the last 100 nautical miles with greater accuracy, by providing deicing times per aircraft type, and by providing insight into factors related to airport response to shock events. These findings will be useful for air traffic management decision makers in addition to other researchers in related future studies and airport simulations.
Application of Deep Learning in Intelligent Transportation Systems
Dabiri, Sina (Virginia Tech, 2019-02-01)
The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application.
Assessment of Vehicle-to-Vehicle Communication based Applications in an Urban Network
Kim, Taehyoung (Virginia Tech, 2015-06-23)
Connected Vehicle research has emerged as one of the highest priorities in the transportation systems because connected vehicle technology has the potential to improve safety, mobility, and environment for the current transportation systems. Various connected vehicle based applications have been identified and evaluated through various measurements to assess the performance of connected vehicle applications. However, most of these previous studies have used hypothetical study areas with simple networks for connected vehicle environment. This study represents connected vehicle environment in TRANSIMS to assess the performance of V2V communication applications in the realistic urban network. The communication duration rate and spatial-temporal dispersion of equipped vehicles are investigated to evaluate the capability of V2V communication based on the market penetration rate of equipped vehicles and wireless communication coverage in the whole study area. The area coverage level is used to assess the spatial-temporal dispersion of equipped vehicles for two study areas. The distance of incident information propagation and speed estimation error are used to measure the performance of event-driven and periodic applications based on different market penetration rates of equipped vehicles and wireless communication coverage in both morning peak and non-peak times. The wireless communication coverage is the major factor for event-driven application and the market penetration rate of equipped vehicles has more impact on the performance of periodic application. The required minimum levels of deployment for each application are determined for each scenario. These study findings will be useful for making decisions about investments on deployment of connected vehicle applications to improve the current transportation systems. Notably, event-driven applications can be reliably deployed in the initial stage of deployment despite the low level of market penetration of equipped vehicles.
Characterization of High Porosity Drainage Layer Materials for M-E Pavement Design
Zhang, Yinning (Virginia Tech, 2015-02-12)
The objective of this study is to characterize the properties of typically adopted drainage layer materials in VA, OK, and ID. A series of laboratory tests have been conducted to quantify the volumetric properties, permeability and mechanical properties of the laboratory-compacted asphalt treated and cement treated permeable base specimens. The modified test protocols to determine the dynamic modulus of the drainage layer materials have been provided, which can be followed to determine the dynamic modulus of the drainage layers as level 1 input in Mechanistic-Empirical (M-E) pavement design. The measured dynamic moduli have been used to calibrate the original NCHRP 1-37A model to facilitate its application on drainage layer materials for prediction of the dynamic modulus as level 2 input. The compressive strength of the cement treated permeable base mixture of different air void contents has also been quantified in laboratory. Numerical simulations are conducted to investigate the location effects and the contribution of the drainage layer as a structural component within pavement. The optimal air void content of the drainage layer is recommended for Virginia, Oklahoma and Idaho based on the laboratory-determined permeability and the predicted pavement performances during 20-year service life.
Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation
Zhou, Guoqing (Virginia Tech, 2011-01-17)
A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in traditional decision tree induction algorithms, when applied in the pavement treatment strategies. The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial attribute data in the pavement database. With the proposed algorithm, one distinct event occurrence can associate with two or multiple attribute values that occur simultaneously in spatial and temporal domains. This research dissertation describes the details of the proposed CL-DT algorithms and steps of realizing the proposed algorithm. First, the dissertation research describes the detailed colocation mining algorithm, including spatial attribute data selection in pavement databases, the determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a neighborhood relationship-based spatial joins of table instances for many prevalent co-locations with one prevalent co-location; and the distance event-type constraint condition is a Euclidean distance between a set of attributes and its corresponding clusters center of attributes. The dissertation research also developed the spatial feature pruning method using the multi-resolution pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection in the pavement management database, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. In this step, co-location mining rules are used to guide the decision tree generation and induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one's expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes.
A comparison of driving characteristics and environmental characteristics using factor analysis and k-means clustering algorithm
Jung, Heejin (Virginia Tech, 2012-08-10)
The dissertation aims to classify drivers based on driving and environmental behaviors. The research determined significant factors using factor analysis, identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into three groups. Two alternatives were designed to evaluate the environmental impact of driving behavior changes. In the second study case, two types of data sets were constructed: driving data and environmental data. The driving data represents driving behavior of individual drivers. The environmental data represents emissions and fuel consumption estimated by microscopic energy and emissions models. Significant factors were explored in each data set using factor analysis. A pair of factors was defined for each data set. Each pair of factors was used for each k-means clustering: driving clustering and environmental clustering. Then the factors were used to identify groups of drivers in each clustering domain. In the driving clustering, drivers were grouped into three clusters. In the environmental clustering, drivers were clustered into two groups. The groups from the driving clustering were compared to the groups from the environmental clustering in terms of emissions and fuel consumption. The three groups of drivers from the driving clustering were also mapped in the environmental domain. The results indicate that the differences in driving patterns among the three driver groups significantly influenced the emissions of HC, CO, and NOx. As a result, it was determined that the average target operating acceleration and braking did essentially influence the amount of emissions in terms of HC, CO, and NOx. Therefore, if drivers were to change their driving behavior to be more defensive, it is expected that emissions of HC, CO, and NOx would decrease. It was also found that spacing-based driving tended to produce less emissions but consumed more fuel than other groups, while speed-based driving produced relatively more emissions. On the other hand, the defensively moderate drivers consumed less fuel and produced fewer emissions.
A Computer Model to Predict Potential Wake Turbulence Encounters in the National Airspace
Fan, Zheng (Virginia Tech, 2015-02-13)
With an increasing population of super heavy aircraft operating in the National Airspace System and with the introduction of NextGen technologies, the wake vortex problem has become more important for airport capacity and the en-route air traffic operations. The vortices generated by heavy and super heavy aircraft can generate potential hazards to other aircraft on nearby flight paths. Moreover, the design of new airport procedures needs to consider the interactions between aircraft in closer paths. New methods and models are required to examine these effects before new operations are conducted in the National Airspace System (NAS). Reducing wake vortex separations to safe levels between successive aircraft is essential for NextGen operations. One approach taken recently by ICAO and the FAA is to introduce a re-categorization (ReCat) of wake vortex separations to six groups from the existing five groups employed by the FAA in the United States. Reduced aircraft separations can increase capacity in the NAS with corresponding savings in delay times at busy airports. Future NextGen operations are likely to introduce smaller aircraft separations in the en-route and in the terminal area. Such operations would require better methods to identify potential wake hazards from reduced separation operations. This dissertation describes a model to identify potential wake encounters in the future NAS. The goal of the dissertation is to describe the Enhanced Wake Encounter Model (EWEM), a model that employs a detailed NASA-developed wake model to generate wake zones for different aircraft categories under different flight conditions that can be used with aircraft flight path data to identify potential wake encounters. The main contribution of this model is to gain an understanding of potential wake encounters under future NAS operations.
Development and Applications of Multi-Objectives Signal Control Strategy during Oversaturated Conditions
Adam, Zaeinulabddin Mohamed Ahmed (Virginia Tech, 2012-08-10)
Managing traffic during oversaturated conditions is a current challenge for practitioners due to the lack of adequate tools that can handle such situations. Unlike under-saturated conditions, operation of traffic signal systems during congestion requires careful consideration and analysis of the underlying causes of the congestion before developing mitigation strategies. The objectives of this research are to provide a practical guidance for practitioners to identify oversaturated scenarios and to develop a multi-objective methodology for selecting and evaluating mitigation strategy/ or combinations of strategies based on a guiding principles. The research focused on traffic control strategies that can be implemented by traffic signal systems. The research did not considered strategies that deals with demand reduction or seek to influence departure time choice, or route choice. The proposed timing methodology starts by detecting network's critical routes as a necessary step to identify the traffic patterns and potential problematic scenarios. A wide array of control strategies are defined and categorized to address oversaturation problematic scenarios. A timing procedure was then developed using the principles of oversaturation timing in cycle selection, split allocation, offset design, demand overflow, and queue allocation in non-critical links. Three regimes of operation were defined and considered in oversaturation timing: (1) loading, (2) processing, and (3) recovery. The research also provides a closed-form formula for switching control plans during the oversaturation regimes. The selection of optimal control plan is formulated as linear integer programming problem. Microscopic simulation results of two arterial test cases revealed that traffic control strategies developed using the proposed framework led to tangible performance improvements when compared to signal control strategies designed for operations in under-saturated conditions. The generated control plans successfully manage to allocate queues in network links.
Development of Aircraft Wake Vortex Dynamic Separations Using Computer Simulation and Modeling
Roa Perez, Julio Alberto (Virginia Tech, 2018-06-29)
This dissertation presents a research effort to evaluate wake vortex mitigation procedures and technologies in order to decrease aircraft separations, which could result in a runway capacity increase. Aircraft separation is a major obstacle to increasing the operational efficiency of the final approach segment and the runway. An aircraft in motion creates an invisible movement of air called wake turbulence, which has been shown to be dangerous to aircraft that encounter it. To avoid this danger, aircraft separations were developed in the 1970s, that allows time for wake to be dissipated and displaced from an aircraft's path. Though wake vortex separations have been revised, they remain overly conservative. This research identified 16 concepts and 3 sub-concepts for wake mitigation from the literature. The dissertation describes each concept along with its associated benefits and drawbacks. All concepts are grouped, based on common dependencies required for implementation, into four categories: airport fleet dependent, parallel runway dependent, single runway dependent, and aircraft or environmental condition dependent. Dynamic wake vortex mitigation was the concept chosen for further development because of its potential to provide capacity benefit in the near term and because it is initiated by air traffic control, not the pilot. Dynamic wake vortex mitigation discretizes current wake vortex aircraft groups by analyzing characteristics for each individual pair of leader and follower aircraft as well as the environment where the aircraft travel. This results in reduced aircraft separations from current static separation standards. Monte Carlo simulations that calculate the dynamic wake vortex separation required for a follower aircraft were performed by using the National Aeronautics and Space Administration (NASA) Aircraft Vortex Spacing System (AVOSS) Prediction Algorithm (APA) model, a semi-empirical wake vortex behavior model that predicts wake vortex decay as a function of atmospheric turbulence and stratification. Maximum circulation capacities were calculated based on the Federal Aviation Administration's (FAA) proposed wake recategorization phase II (RECAT II) 123 x 123 matrix of wake vortex separations. This research identified environmental turbulence and aircraft weight as the parameters with the greatest influence on wake vortex circulation strength. Wind has the greatest influence on wake vortex lateral behavior, and aircraft mass, environmental turbulence, and wind have the greatest influence on wake vortex vertical position. The research simulated RECAT II and RECAT III dynamic wake separations for Chicago O'Hare International (ORD), Denver International Airport (DEN) and LaGuardia Airport (LGA). The simulation accounted for real-world conditions of aircraft operations during arrival and departure: static and dynamic wake vortex separations, aircraft fleet mix, runway occupancy times, aircraft approach speeds, aircraft wake vortex circulation capacity, environmental conditions, and operational error buffers. Airport data considered for this analysis were based on Airport Surface Detection Equipment Model X (ASDE-X) data records at ORD during a 10-month period in the year 2016, a 3-month period at DEN, and a 4-month period at LGA. Results indicate that further reducing wake vortex separation distances from the FAA's proposed RECAT II static matrix, of 2 nm and less, shifts the operational bottleneck from the final approach segment to the runway. Consequently, given current values of aircraft runway occupancy time under some conditions, the airport runway becomes the limiting factor for inter-arrival separations. One of the major constraints of dynamic wake vortex separation at airports is its dependence on real-time or near-real-time data collection and broadcasting technologies. These technologies would need to measure and report temperature, environmental turbulence, wind speed, air humidity, air density, and aircraft weight, altitude, and speed.
Development of an Aircraft Landing Database and Models to Estimate Aircraft Runway Occupancy Times
Mirmohammadsadeghi, Navid (Virginia Tech, 2020-09-04)
This dissertation represents the methodologies used to develop an aircraft landing database and predictive models for estimating arrival flight runway occupancy times. In the second chapter, all the algorithms developed for analyzing the airport surface radar data are explained, and detailed statistical information about various airports in the United States in terms of landing behavior is studied. In the third chapter a novel data-driven approach for modeling aircraft landing behavior is represented. The outputs of the developed approach are runway occupancy time distributions and runway exit utilizations. The represented hybrid approach in the third chapter is a combination of machine learning and Monte Carlo simulation methods. This novel approach was calibrated based on two years of airport radar data. The study's output is a computer application, which is currently being used by the Federal Aviation Administration and various airport consulting firms for analyzing and designing optimum runway exits to optimize runway occupancy times at airports. In the fourth chapter, four real-world case scenarios were analyzed to show the power of the developed model in solving real-world challenges in airport capacity. In the fifth chapter, pilot motivational behaviors were introduced, and three methodologies were used to replicate motivated pilot behaviors on the runway. Finally, in the sixth chapter, a neural network approach was used as an alternative model for estimating runway occupancy time distributions.
Development of Sustainable Traffic Control Principles for Self-Driving Vehicles: A Paradigm Shift Within the Framework of Social Justice
Mladenovic, Milos (Virginia Tech, 2014-08-22)
Developments of commercial self-driving vehicle (SDV) technology has a potential for a paradigm shift in traffic control technology. Contrary to some previous research approaches, this research argues that, as any other technology, traffic control technology for SDVs should be developed having in mind improved quality of life through a sustainable developmental approach. Consequently, this research emphasizes upon the social perspective of sustainability, considering its neglect in the conventional control principles, and the importance of behavioral considerations for accurately predicting impacts upon economic or environmental factors. The premise is that traffic control technology can affect the distribution of advantages and disadvantages in a society, and thus it requires a framework of social justice. The framework of social justice is inspired by John Rawls' Theory of Justice as fairness, and tries to protect the inviolability of each user in a system. Consequently, the control objective is the distribution of delay per individual, considering for example that the effect of delay is not the same if a person is traveling to a grocery store as opposed to traveling to a hospital. The notion of social justice is developed as a priority system, with end-user responsibility, where user is able to assign a specific Priority Level for each individual trip with SDV. Selected Priority Level is used to determine the right-of-way for each self-driving vehicle at an intersection. As a supporting mechanism to the priority system, there is a structure of non-monetary Priority Credits. Rules for using Priority Credits are determined using knowledge from social science research and through empirical evaluation using surveys, interviews, and web-based experiment. In the physical space, the intersection control principle is developed as hierarchical self-organization, utilizing communication, sensing, and in-vehicle technological capabilities. This distributed control approach should enable robustness against failure, and scalability for future expansion. The control mechanism has been modeled as an agent-based system, allowing evaluation of effects upon safety and user delay. In conclusion, by reaching across multiple disciplines, this development provides the promise and the challenge for evolving SDV control technology. Future efforts for SDV technology development should continue to rely upon transparent public involvement and understanding of human decision-making.
Digital Mix Design for Performance Optimization of Asphalt Mixture
Li, Ying (Virginia Tech, 2015-03-27)
Asphalt mix design includes the determination of a gradation, asphalt content, other volumetric properties, the evaluation of mechanical properties and moisture damage potentials. In this study, a computational method is developed to aid mix design. Discrete element method (DEM) was used to simulate the formation of skeleton and voids structures of asphalt concrete of different gradations of aggregates. The optimum gradation could be determined by manipulating the particle locations and orientations and placing smaller particles in the voids among larger particles. This method aims at an optimum gradation, which has been achieved through experimental methods. However, this method takes the mechanical properties or performance of the mixture into consideration, such as inter-aggregate contacts and local stability. A simple visco-elastic model was applied to model the contacts between asphalt binder and aggregates. The surface texture of an aggregate particle can be taken into consideration in the inter-particle contact model. The void content before compactions was used to judge the relative merits of a gradation. Once a gradation is selected, the Voids in Mineral Aggregate (VMA) can be determined. For a certain air void content, the mastics volume or the binder volume or the asphalt content can be determined via a digital compression test. The surface area of all the aggregates and the film thickness can be then calculated. The asphalt content can also be determined using an alternative approach that is based on modeling the inter-particle contact with an asphalt binder layer. In this study, considering the necessity of preservation of the compaction temperature, the effect of various temperatures on Hot Mix Asphalt (HMA) samples properties has been evaluated. As well, to evaluate the effect of this parameter on different grading, two different grading have been used and samples were compacted at various temperatures. Air voids also influence pore water pressure and shrinkage of asphalt binder and mixture significantly. The shrinkage is measured on a digital model that represents beams in a steel mold and is defined as the linear autogenous deformation at horizontal direction.
Dynamic Modeling and Lateral Stability Analysis of Long Combination Vehicles
Zhang, Zichen (Virginia Tech, 2022-10-28)
This study provides a comprehensive modeling evaluation of the dynamic stability of Long Combination Vehicles (LCVs) that are commonly operated on U.S. highways, using multibody dynamic simulations in MATLAB/Simulink®. The dynamic equations for a tractor with two trailers connected by an A-frame converter dolly (A-Dolly) are developed. The dynamic model is used for running MATLAB® simulations, with parameters that are obtained through measurements or obtained from other sources. The simulation results are verified using track test data to establish a baseline model. The baseline model is used for parametric studies to evaluate the effect of trailer cargo weight, center of gravity (CG) longitudinal location, and trailer wheelbase. The dynamic model is further used to analyze both single-trailer and double-trailer trucks through nondimensionalization. The nondimensionalization method has the added advantage of enabling studies that can more broadly apply to various truck configurations. The simulation results indicate that increasing the trailer wheelbase reduces rearward amplification due to the damping effect of the longer wheelbase. A larger momentum ratio due to increased trailer gross weight increases rearward amplification. The detailed models of pneumatic disc and drum brakes in LCVs, including the airflow delay and thermal characteristics, are also developed and are coupled with the articulated vehicle dynamic models. The disc and drum brake braking performance are evaluated and compared in straight-line braking and combined steering and braking at a 150-ft J-turn maneuver. In straight-line braking, the simulation results indicate that disc brakes provide significantly shorter braking distance than drum brakes at highway speeds on a dry road, mainly due to their larger braking torque. On a slippery road surface, however, the greater braking torque causes more frequent wheel lockup and ABS activation at higher speeds, and disc brakes do not provide a substantially shorter braking distance than drum brakes. The simulations also point out that the disc brakes' cooling capacity is higher than the drum brake, with the cooling efficiency heavily dependent on the airflow speed. At higher driving speeds, the airflow accelerates to a turbulent flow and increases the convection efficiency. For braking in-turn maneuvers, at higher entering speeds, disc brakes decelerate the vehicle slightly sooner and then scrub speed faster, resulting in better roll stability when compared with drum brakes.
Emotional Impacts on Driver Behavior: An Emo-Psychophysical Car-Following Model
Higgs, Bryan James (Virginia Tech, 2014-09-09)
This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions.
Enhanced Air Transportation Modeling Techniques for Capacity Problems
Spencer, Thomas Louis (Virginia Tech, 2016-09-02)
Effective and efficient air transportation systems are crucial to a nation's economy and connectedness. These systems involve capital-intensive facilities and equipment and move millions of people and tonnes of freight every day. As air traffic has continued to increase, the systems necessary to ensure safe and efficient operation will continue to grow more and more complex. Hence, it is imperative that air transport analysts are equipped with the best tools to properly predict and respond to expected air transportation operations. This dissertation aims to improve on those tools currently available to air transportation analysts, while offering new ones. Specifically, this thesis will offer the following: 1) A model for predicting arrival runway occupancy times (AROT); 2) a model for predicting departure runway occupancy times (DROT); and 3) a flight planning model. This thesis will also offer an exploration of the uses of unmanned aerial vehicles for providing wireless communications services. For the predictive models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian methods are preferred since their output are distributions that can be directly inputted into air transportation modeling programs. Additionally, we exhibit how analysts will be able to decouple AROT and DROT predictions from the traditional 4 or 5 groupings of aircraft currently in use. Lastly, for the flight planning model, we present a 2-D model using presently available wind data that provides wind-optimal flight routings. We improve over current models by allowing free-flight unconnected to pre-existing airways and by offering finer resolutions over the current 2.5 degree norm.
Evaluating Pavement Response and Performance with Different Simulative Tests
Huang, Yucheng (Virginia Tech, 2017-06-30)
Simulative tests refer to the Full-scale accelerated pavement testing (APT) and laboratory wheel tracking testing, which are widely used for evaluation of pavement responses and performance under a controlled and accelerated damage conditions in a compressed limited time. This dissertation focuses on comparative evaluations under ALF, MMLS 3 and APA tests, in terms of rut depth, strain response, seismic stiffness, and contact stress using both experimental and numerical simulation results. Test slabs extracted from the ALF test lanes, are trafficked with the MMLS3 under comparable environmental conditions at laboratory in Virginia Tech. Some specimens were cut from the slabs for APA tests at VTRC. It is found that the monitored parameters yielded by the MMLS 3 test were comparable to the related full-scale ALF test results in terms of intrinsic material characteristics and pavement performance. The wireless sensor network based on Internet of things technology is implemented in laboratory for the MMLS 3 test, which provides a convenient solution for researchers on long-term observation and monitoring without being physically presented. The numerical simulations of ALF, MMLS 3 and APA in ABAQUS are used to supplement the investigation on the pavement response and performance under repeated moving loading. The viscoelastic-viscoplastic model is adopted to characterize rate and temperature dependent properties of asphalt mixtures. The 3D finite element models are capable of predicting the pavement response at critical locations while underestimates the rut depth because the permanent deformation induced by volumetric change cannot be represented in simulation. According to the test results, a power law function fits well for the accumulated rut depth versus number of load repetitions before the material reaches tertiary stage in MMLS 3 test. The rut depth development of APA tests exhibits a close-to-liner regression with number of load cycles after the initial 500 load repetitions. A regression model for predicting rut depth after 500 loads has a satisfying agreement with the experimental measurement. The calibrated MEPDG fatigue model can be used to estimate the allowable load repetitions in MMLS 3 trafficking. Besides, the effects of tire configuration, tire pressure, axle load amplitude, wheel load speed and temperature on pavement responses are investigated in this dissertation using the finite element model. It is concluded that MMLS 3 is an effective, economic and reliable trafficking tool to characterize rutting and fatigue performance of pavement materials with due regard to the relative structures. MMLS 3 test can be employed as the screen testing for establishing full-scale testing protocols as desired or required, which will significantly enhance economics of APT testing.
General Aviation Demand Forecasting Models and a Microscopic North Atlantic Air Traffic Simulation Model
Li, Tao (Virginia Tech, 2015-01-06)
This thesis is focused on two topics. The first topic is the General Aviation (GA) demand forecasting models. The contributions to this topic are three fold: 1) we calibrated an econometric model to investigate the impact of fuel price on the utilization rate of GA piston engine aircraft, 2) we adopted a logistic model to identify the relationship between fuel price and an aircraft's probability of staying active, and 3) we developed an econometric model to forecast the airport-level itinerant and local GA operations. Our calibration results are compared with those reported in literature. Demand forecasts are made with these models and compared with those prepared by the Federal Aviation Administration. The second topic is to model the air traffic in the Organized Track System (OTS) over the North Atlantic. We developed a discrete-time event model to simulate the air traffic that uses the OTS. We proposed four new operational procedures to improve the flight operations for the OTS. Two procedures aim to improve the OTS assignments in the OTS entry area, and the other two aim to benefit flights once they are inside the OTS. The four procedures are implemented with the simulation model and their benefits are analyzed. Several implementation issues are discussed and recommendations are given.
High Automobile Emissions: Modeling Impacts and Developing Solutions
Park, Sangjun (Virginia Tech, 2008-09-03)
In the last few years, scientific consensus is that emission of greenhouse gases (GHGs) into the atmosphere is contributing to changes in the earth's climate. While uncertainty remains over the pace and dimensions of the change, a consensus on the need for action has grown among the public and elected officials. In part, this shift has been accelerated by concern over energy security and rising fuel prices. The new political landscape has led many cities, states, and regions to institute policies aimed at reducing GHG emissions. These policies and emerging initiatives have significant implications for the transportation planning process. The transportation sector accounts for approximately 27% of GHG production in the U.S. (as of 2003) and while the U.S. accounts for only roughly 5% of the world's population, it is estimated that it produces over 20% of the world's GHG emissions. Note that this does not include "lifecycle" emissions that result from the processes undertaken to extract, manufacture, and transport fuel. Carbon dioxide represents approximately 96% of the transportation sector's radiative forcing effects. Unlike conventional air pollutants, carbon dioxide emissions are directly tied to the amount of fuel consumed and its carbon intensity. Therefore, emissions reductions can be achieved by increasing the use of low-carbon fuels, improving fuel economy, or reducing total vehicle miles of travel - often called the three legged stool. (A fourth leg is congestion reduction, at certain optimal speeds). These same factors are related to our use of imported oil, so actions taken to reduce GHG emissions may actually produce benefits in both policy areas. The climatic risks of additional emissions associated with capacity projects must be balanced against the mobility, safety, and economic needs of a community or region. Consequently, this dissertation attempts to quantify the impacts of high-emitting vehicles on the environment and to propose solutions to enhance the currently-used high-emitting vehicle detection procedures. In addition, fuel consumption and emission models for high-speed vehicles are developed in order to provide more reliable estimates of vehicle emissions and study the impact of vehicle speeds on vehicle emissions. The dissertation extends the state-of-the-art analysis of high emitting vehicles (HEVs) by quantifying the network-wide environmental impact of HEVs. The literature reports that 7% to 12% of HEVs account for somewhere between 41% to 63% of the total CO emissions, and 10% are responsible for 47% to 65% of HC emissions, and 10% are responsible for 32% of NOx emissions. These studies, however, are based on spot measurements and do not necessarily reflect network-wide impacts. Consequently, the research presented in this dissertation extends the state-of-knowledge by quantifying HEV contributions on a network level. The study uses microscopic vehicle emission models (CMEM and VT-Micro model) along with pre-defined drive cycles (under the assumption that the composite HEV and VT-LDV3 represent HEVs and NEVs, respectively) in addition to the simulation of two transportation networks (freeway and arterial) to quantify the contributions of HEVs. The study demonstrates that HEVs are responsible for 67% to 87% of HC emissions, 51% to 78% of CO emissions, and 32% to 62% of the NOX emissions for HEV percentages ranging from 5% to 20%. Additionally, the traffic simulation results demonstrate that 10% of the HEVs are responsible for 50% to 66% of the I-81 HC and 59% to 78% of the Columbia Pike HC emissions, 35% to 67% of the I-81 CO and 38% to 69% of the Columbia Pike CO emissions, and 35% to 44% of the I-81 NOX and 35% to 60% of the Columbia Pike NOX emissions depending on the percentage of the normal-emitting LDTs to the total NEVs. HEV emission contributions to total HC and CO emissions appear to be consistent with what is reported in the literature. However, the contribution of NOX emissions is greater than what is reported in the literature. The study demonstrates that the contribution of HEVs to the total vehicle emissions is dependent on the type of roadway facility (arterials vs. highways), the background normal vehicle composition, and the composition of HEVs. Consequently, these results are network and roadway specific. Finally, considering that emission control technologies in new vehicles are advancing, the contribution of HEVs will increase given that the background emission contribution will decrease. Given that HEVs are responsible for a large portion of on-road vehicle emissions, the dissertation proposes solutions to the HEV screening procedures. First, a new approach is proposed for estimating vehicle mass emissions from concentration remote sensing emission measurements using the carbon balance equation in conjunction with either the VT-Micro or PERE fuel consumption rates for the enhancement of current state-of-the-art HEV screening procedures using RSD technology. The study demonstrates that the proposed approach produces reliable mass emission estimates for different vehicle types including sedans, station wagons, full size vans, mini vans, pickup trucks, and SUVs. Second, a procedure is proposed for constructing on-road RS emission standards sensitive to vehicle speed and acceleration levels. The proposed procedure is broadly divided into three sub-processes. In the first process, HE cut points in grams per second are developed as a function of a vehicle's speed and acceleration levels using the VT-Micro and CMEM emission models. Subsequently, the HE cut points in grams per second are converted to concentration emissions cut points in parts per million using the carbon balance equation. Finally, the scale factors are computed using either ASM ETW- and model-year-based standards or engine-displacement-based standards. Given the RS emissions standards, the study demonstrated that the use of on-road RS cut points sensitive to speed and acceleration levels is required in order to enhance the effectiveness of RS. Finally, the dissertation conducted a study to develop fuel consumption and emissions models for high-speed vehicles to overcome the shortcomings of state-of-practice models. The research effort gathered field data and developed models for the estimation of fuel consumption, CO₂, CO, NO, NO2, NOx, HC, and PM emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars were tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates were measured using two portable emission measurement systems. Models were developed using these data producing minimum errors for fuel consumption, CO₂, NO2, HC, and PM emissions. Alternatively, the NO and NOx emission models produced the highest errors with a least degree of correlation. Given the models, the study demonstrated that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed driving.
High-Speed Roll Stability Evaluation of A-Double Tractor-Trailers
Zheng, Xiaohan (Virginia Tech, 2023-01-03)
The effect of center of gravity (CG) height and lateral and longitudinal off-centering on high-speed roll stability of A-double tractor trailers with 28-ft and 33-ft straight-rail and drop-frame trailers is evaluated through simulation and track testing. The changes in CG position due to the type of trailer (straight-rail vs. drop-frame) and laterally and longitudinally off-centered loads are considered. The simulation results show that imbalanced trailer loading induces roll instability and increases the likelihood of trailer rollover. Additionally, for equal loading conditions, the drop-frame trailers exhibit better roll stability than straight-rail trailers because of the lower CG. The simulation evaluation of 28-ft A-doubles is complemented with track testing of 33-ft trailers in alike (Drop-Drop and Straight-Straight) and mixed (Drop-Straight and Straight-Drop) arrangements of front and rear trailers, for various steering maneuvers that represent highway driving, such as exit ramp, obstacle avoidance, etc. The test trailers include specially designed load frames for emulating a loaded trailer in various loading conditions, outriggers for preventing trailer rollover, and durability structures for withstanding the torsional and bending moments resulting from the tests. Various sensors, including GPS, LiDAR units, accelerometers, string pots, and pressure transducers, are used, along with an onboard data acquisition (DAQ) system, for collecting the necessary data for post-analysis. Analysis of the test data indicates that the Drop-Drop configuration exhibits higher roll stability than the Straight-Straight configuration. For mixed trailers, the Drop-Straight configuration exhibits higher roll stability in exit ramps, but lower obstacle avoidance stability. Equipping the trailers with a roll stability control (RSC) system improves roll stability in terms of increasing the rollover threshold speed and tolerating more aggressive lane change steering maneuvers for A-doubles in various conditions. The RSC performance increases further when the brake application is synchronized between the two trailers to account for any lateral dynamic delay that naturally occurs. A novel interconnected RSC system is proposed to eliminate the lag between the RSC modules with a new control logarithm. The proposed RSC system increases the trailers' roll stability by 16% when compared with independent RSC systems that are commonly used for A-doubles.
Identification and Characterization of Damaging Road Events
Altmann, Craig Tyler (Virginia Tech, 2020-06-12)
In the field of vehicle durability, many individuals are focusing on methods for better replicating the durability a user will experience throughout the typical design lifespan of a vehicle (e.g., 100,000 miles). To estimate user durability a means of understand the types of damaging events and driving styles of uses must be understood. The difficulty with accurately estimating customer usage is, firstly, there is a large pool of possible roads for a user to drive along, for example, there are over 4 million miles of public roads in the United States, alone [1]. In addition, while measurements of these surfaces could be collected it would be impractical for two reasons, the first is the financial and extreme time burden this would take. Second, when collecting measurements of a road surface only the current state of a road surface can be measured, thus as a road deteriorates or is repaved the measurements collected would no longer be an accurate representation of the road. It should be mentioned that even, if all of the road surfaces were measured performing simulation and analysis of all of these road surfaces would be computationally intensive. Instead, it would be beneficial if select events that account for a significant portion of the damage a vehicle experiences can be identified. These damaging events could then be used in more complex vehicle simulation models and as input and validation of proving ground and laboratory durability testing. The objective of this research is to provide a means for improved estimation of vehicle durability, specifically a means for identifying, characterizing, and grouping unique separable damaging events from a road profile measurement. In order to achieve this objective a measure that can be used to identify separate damaging events from a road profile is developed. This measure is defined as Localized Pseudo Damage (LPD), which identifies the amount of damage each individual road excitation makes to the total accumulated damage for a single load path in a vehicle system. LPD is defined as a damage density to minimize the effect of measurement spacing on the resulting metric. The developed LPD measure is causal in that the value of LPD at a location is not affected by any future locations. In addition, for a singular event (e.g., impulse or step) in the absences of other excitations, the LPD value at the singular event location is equivalent to the total pseudo damage divided by the step size at the location. Once a measure of pseudo damage density is known at multiple locations along a road profile for multiple load paths of interest, then separable damaging events can be identified. To identify separable damaging events the activity of the vehicle system must be considered because separate damaging events can only occur when a region of inactivity is present across all load paths. Subsequently, an optimization problem is formed to determine the optimal active regions to maintain. The cost function associated with the optimization problem is defined to minimize the cost (number of locations maintained in damaging events) and maximize the benefit (the amount of pseudo damage maintained). Lastly, a statistical test is developed to assess if separate damaging events can be considered to be from the same general class of events based on their damage characteristics. The developed assessment methods establish the similarity between two more separable damaging events based on application specific user defined inputs. In the development, two example similarity metrics are defined. The first similarity metric is in terms of distance and the second is in terms of likelihood (probability). The developed statistical analysis uses the current state-of-the-art in clustering algorithms to allow for multiple damaging events to be identified and grouped together.

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Browsing Doctoral Dissertations by Author "Abbas, Montasir M."

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