Browsing by Author "Abbas, Montasir M."
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- Access Control Design on Highway InterchangesRakha, Hesham A.; Flintsch, Alejandra Medina; Arafeh, Mazen; Abdel-Salam, Abdel-Salam Gomaa; Dua, Dhruv; Abbas, Montasir M. (Virginia Center for Transportation Innovation and Research, 2008-01-01)The adequate spacing and design of access to crossroads in the vicinity of freeway ramps are critical to the safety and traffic operations of both the freeway and the crossroad. The research presented in this report develops a methodology to evaluate the safety impact of different access road spacing standards. The results clearly demonstrate the shortcomings of the AASHTO standards and the benefits of enhancing them. The models developed as part of this research were used to compute the crash rate associated with alternative section spacing. The study demonstrates that the models satisfied the statistical requirements and provide reasonable crash estimates. The results demonstrate an eight-fold decrease in the crash rate when the access road spacing increases from 0 to 300 m. An increase in the minimum spacing from 90 m (300 ft) to 180 m (600 ft) results in a 50 percent reduction in the crash rate. The models were used to develop lookup tables that quantify the impact of access road spacing on the expected number of crashes per unit distance. The tables demonstrate a decrease in the crash rate as the access road spacing increases. An attempt was made to quantify the safety cost of alternative access road spacing using a weighted average crash cost. The weighted average crash cost was computed considering that 0.6, 34.8, and 64.6 percent of the crashes were fatal, injury, and property damage crashes, respectively. These proportions were generated from the field observed data. The cost of each of these crashes was provided by VDOT as $3,760,000, $48,200, and $6,500 for fatal, injury, and property damage crashes, respectively. This provided an average weighted crash cost of $43,533. This average cost was multiplied by the number of crashes per mile to compute the cost associated with different access spacing scenarios. These costs can assist policy makers in quantifying the trade-offs of different access management regulations.
- Air pollution perception in ten countries during the COVID-19 pandemicLou, Baowen; Barbieri, Diego Maria; Passavanti, Marco; Hui, Cang; Gupta, Akshay; Hoff, Inge; Lessa, Daniela Antunes; Sikka, Gaurav; Chang, Kevin; Fang, Kevin; Lam, Louisa; Maharaj, Brij; Ghasemi, Navid; Qiao, Yaning; Adomako, Solomon; Mirhosseini, Ali Foroutan; Naik, Bhaven; Banerjee, Arunabha; Wang, Fusong; Tucker, Andrew; Liu, Zhuangzhuang; Wijayaratna, Kasun; Naseri, Sahra; Yu, Lei; Chen, Hao; Shu, Benan; Goswami, Shubham; Peprah, Prince; Hessami, Amir; Abbas, Montasir M.; Agarwal, Nithin (2021-06-21)As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.
- Airport Performance Metrics Analysis: Application to Terminal Airspace, Deicing, and ThroughputAlsalous, 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.
- Analysis of Potential Wake Turbulence Encounters in Current and NextGen Flight OperationsSchroeder, Nataliya (Virginia Tech, 2011-01-31)Wake vortices pose a threat to a following aircraft, because they can induce a roll and compromise the safety of everyone on board. Caused by a difference in pressure between the upper and the lower part of the wings, these invisible flows of air are a major hazard and have to be avoided by separating the aircraft at considerable distances. One of the known constraints in airport capacity for both departure and arrival operations is the large headway resulting from the wake spacing separation criteria. Reducing wake vortex separations to a safe level between successive aircraft can increase capacity in the National Airspace System (NAS) with corresponding savings in delay times. One of the main goals of the Wake Encounter Model (WEM) described in this thesis is to assess the outcome from future reduced separation criteria in the NAS. The model has been used to test probable encounters in today's operations, and can also be used to test NextGen scenarios, such as Close Parallel Approaches and reduced in-trail separation flights. This thesis presents model enhancements to account for aircraft turning maneuvers, giving the wake a more realistic shape. Three major airspaces, New York, Southern California and Atlanta, were analyzed using the original and the enhanced WEM to determine if the enhanced model better represents the conditions in today's operations. Additionally, some analysis on the wake lateral travel for closely spaced runways is presented in this thesis. Finally, some extension tools for post -analysis, such as animation tool and various graphs depicting the interactions between wake pairs were developed.
- Application of Deep Learning in Intelligent Transportation SystemsDabiri, 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.
- Application of Naturalistic Truck Driving Data to Analyze and Improve Car Following ModelsHiggs, Bryan James (Virginia Tech, 2011-12-02)This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data.
- Assessment of Vehicle-to-Vehicle Communication based Applications in an Urban NetworkKim, 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.
- Aviation Global Demand Forecast Model Development: Air Transportation Demand Distribution and Aircraft Fleet EvolutionFreire Burgos, Edwin R. (Virginia Tech, 2017-09-08)The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040. This research project intends to enhance the GDM capabilities. A Fratar model is implemented for the distribution of the forecast demand during each year. The Fratar model uses a 3,974 by 3,974 origin-destination matrix to distribute the demand among 55,612 unique routes in the network. Moreover, the GDM is capable to estimate the aircraft fleet mix per route and the number of flights per aircraft that are needed to satisfy the forecast demand. The model adopts the aircraft fleet mix from the Official Airline Guide data for the year 2015. Once the aircraft types are distributed and flights are assigned, the GDM runs an aircraft retirement and replacement analysis to remove older generation aircraft from the network and replace them with existing or newer aircraft. The GDM continues to evolve worldwide aircraft fleet by introducing 14 new generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA.
- Bluetooth based dynamic critical route volume estimation on signalized arterialsGharat, Asmita (Virginia Tech, 2011-09-09)Bluetooth Data collection technique is recently proven as a reliable data collection technique that provides the opportunity to modify traditional methodologies to improve system performance. Actual volume in the network is a result of the timing plans which are designed and modified based on the volume which is generated using existing timing plans in the system. This interdependency between timing plan and volume on the network is a dynamic process and should be captured to obtain actual traffic states in the network. The current practice is to calculate synthetic origin destination information based on detector volume that doesn't necessarily represent the volume scenario accurately. The data from Bluetooth technology can be utilized to calculate dynamic volume on the network which can be further used as input for signal timing design. Application of dynamic volume improves the system performance by providing the actual volume in system to design optimal timing plans. This thesis proposes a framework that can be used to integrate data obtained from the Bluetooth technology with the traditional methods to design timing plans. The proposed methodology utilizes the origin destination information obtained from Bluetooth data, detector data, characteristics of intersections such as number of lanes, saturation flow rate and existing timing plans as a basis for the calculation of the dynamic volume for the various movements at each intersection. The study shows that using the Bluetooth based OD matrix to calculate accurate dynamic volumes results in better system performance compared to the traditional way of using the static detector volume alone.
- Calibration and Comparison of the VISSIM and INTEGRATION Microscopic Traffic Simulation ModelsGao, Yu (Virginia Tech, 2008-09-05)Microscopic traffic simulation software have gained significant popularity and are widely used both in industry and research mainly because of the ability of these tools to reflect the dynamic nature of the transportation system in a stochastic fashion. To better utilize these software, it is necessary to understand the underlying logic and differences between them. A Car-following model is the core of every microscopic traffic simulation software. In the context of this research, the thesis develops procedures for calibrating the steady-state car-following models in a number of well known microscopic traffic simulation software including: CORSIM, AIMSUN, VISSIM, PARAMICS and INTEGRATION and then compares the VISSIM and INTEGRATION software for the modeling of traffic signalized approaches. The thesis presents two papers. The first paper develops procedures for calibrating the steady-state component of various car-following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car-following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways. Second, the study compares the logic used in both the VISSIM and INTEGRATION software, applies the software to some simple networks to highlight some of the differences/similarities in modeling traffic, and compares the various measures of effectiveness derived from the models. The study demonstrates that both the VISSIM and INTEGRATION software incorporate a psycho-physical car-following model which accounts for vehicle acceleration constraints. The INTEGRATION software, however uses a physical vehicle dynamics model while the VISSIM software requires the user to input a vehicle-specific speed-acceleration kinematics model. The use of a vehicle dynamics model has the advantage of allowing the model to account for the impact of roadway grades, pavement surface type, pavement surface condition, and type of vehicle tires on vehicle acceleration behavior. Both models capture a driver's willingness to run a yellow light if conditions warrant it. The VISSIM software incorporates a statistical stop/go probability model while current development of the INTEGRATION software includes a behavioral model as opposed to a statistical model for modeling driver stop/go decisions. Both software capture the loss in capacity associated with queue discharge using acceleration constraints. The losses produced by the INTEGRATION model are more consistent with field data (7% reduction in capacity). Both software demonstrate that the capacity loss is recovered as vehicles move downstream of the capacity bottleneck. With regards to fuel consumption and emission estimation the INTEGRATION software, unlike the VISSIM software, incorporates a microscopic model that captures transient vehicle effects on fuel consumption and emission rates.
- Characterization of High Porosity Drainage Layer Materials for M-E Pavement DesignZhang, 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 RehabilitationZhou, 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 algorithmJung, 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 Estimate Benefits of Data Link Mandates and Reduced Separations across North Atlantic Organized Track SystemGunnam, Aswin Kumar (Virginia Tech, 2013-01-04)The International Civil Aviation Organization (ICAO) proposed to introduce new operational strategies across the North Atlantic (NAT) airspace. This includes Minimum Navigation Performance Specifications (MNPS) airspace to increase the capacity and efficiency of the North Atlantic Organized Track System (NAT OTS). A numerical integration and simulation model called North Atlantic Simulation and Modeling (NATSAM) is developed to study the effects of these new strategies on NAT system performance. The model is capable of investigating the effects of implementing different operational policies and strategies proposed by ICAO such as Reduced Lateral Separation Minimum (RLatSM), NAT Region Data link mandate (DLM), Reduced Longitudinal Separation Minimum (RLongSM), cruise-climb profiles, variable Mach number profiles, step-climbs and other operational concepts to be studied by the ICAO. NATSAM models the individual flight performance using the Base of Aircraft Data (BADA) 3.9 model to calculate the flight profiles and fuel burn. The model employs simple heuristics to execute flight track assignment in the organized track system for each scenario. Detailed outputs and also aggregated outputs are provided by the model from which various key performance indicators (KPI) can be derived to assess the performance of the system.
- A Computer Model to Estimate Commercial Aviation Fuel Consumption and Emissions in the Continental United StatesZou, Zhihao (Virginia Tech, 2013-01-03)A comprehensive model is developed to estimate and predict the fuel consumption and emissions by domestic commercial aviation in the Continental United States. Most of the existing fuel consumption and emission models are limited in their ability to predict the annual fuel burn for air transportation at the national level. For example, those models either require real track data or are developed only to model single flight scenarios. The model developed in this thesis is part of a software framework called the Transportation Systems Analysis Model (TSAM). The model has the capability to estimate fuel consumption and emissions for millions of domestic flights in a year in the continental U.S. TSAM is a nationwide, long-distance, multimodal travel demand forecast model developed at Virginia Tech. The model enables TSAM to quantify fuel and emission metrics for various modes of transportation. The EUROCONTROL Base of Aircraft Data (BADA) is employed as the Aircraft Performance Model to simulate individual flight profiles and calculate fuel burn rates. Fuel consumption on the ground (taxi mode) is estimated separately. Different operational conditions like wind states, terminal area detour, cruise altitude and airport elevation are considered in the model. Emissions of HC, CO, NOx and SOx are computed inside the Landing/Take-off (LTO) cycle based on the fuel consumption estimate, while greenhouse gas of CO2 is calculated for the complete flight cycle.
- A Computer Model to Predict Potential Wake Turbulence Encounters in the National AirspaceFan, 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.
- Connecting the Opens: Open Access, Open Education, Open DataPotter, Peter J.; Walz, Anita R.; DePauw, Karen P.; Jhangiani, Rajiv; Artiles, Mayra S.; Abbas, Montasir M.; Petters, Jonathan L.; Young, Philip (Virginia Tech. University Libraries, 2018-03-19)Open practices represent opportunities to align scholarly and instructional processes with scholarly ideals, ethical stances, real work impacts, and aspirations for a more just and equitable world. There are many types of “open.” The three we will discuss, open access, open education, and open data practices may appear distinct and siloed from each other; This is only a surface-level view. In reality, these open practices areas have tremendous areas of overlap. Their underlying values reflect similar aspirations for the common good, and aims of overcoming some shared problems found in research and instruction in higher education and in society in general. This panel features students, faculty, and administrators with wide range of expertise in the three areas of open access, open education, and open data. Join us for a stimulating conversation in which we come to understand the differences and similarities between the opens, their purposes, and their potential. Presenters: Peter Potter, Anita Walz Panelists: Karen DePauw, Rajiv Jhangiani, Philip Young, Jon Petters, Mayra Artiles, Monty Abbas This event was part of Virginia Tech’s Open Education 2018 Symposium and was attended by many graduate students from Preparing the Future Professoriate. Video credit: Abram Diaz-Strandberg
- Development and Applications of Multi-Objectives Signal Control Strategy during Oversaturated ConditionsAdam, 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 a High-Speed Rail Model to Study Current and Future High-Speed Rail Corridors in the United StatesVandyke, Alex J. (Virginia Tech, 2011-05-31)A model that can be used to analyze both current and future high-speed rail corridors is presented in this work. This model has been integrated into the Transportation Systems Analysis Model (TSAM). The TSAM is a model used to predict travel demand between any two locations in the United States, at the county level. The purpose of this work is to develop tools that will create the necessary input data for TSAM, and to update the model to incorporate passenger rail as a viable mode of transportation. This work develops a train dynamics model that can be used to calculate the travel time and energy consumption of multiple high-speed train types while traveling between stations. The work also explores multiple options to determine the best method of improving the calibration and implementation of the model in TSAM. For the mode choice model, a standard C logit model is used to calibrate the mode choice model. The utility equation for the logit model uses the decision variables of travel time and travel cost for each mode. A modified utility equation is explored; the travel time is broken into an in-vehicle and out-of-vehicle time in an attempt to improve the model, however the test determines that there is no benefit to the modification. In addition to the C-logit model, a Box-Cox transformation is applied to both variables in the utility equation. This transformation removes some of the linear assumptions of the logit model and thus improves the performance of the model. The calibration results are implemented in TSAM, where both existing and projected high-speed train corridors are modeled. The projected corridors use the planned alignment for modeling. The TSAM model is executed for the cases of existing train network and projected corridors. The model results show the sensitivity of travel demand by modeling the future corridors with varying travel speeds and travel costs. The TSAM model shows the mode shift that occurs because of the introduction of high-speed rail.
- Development of Aircraft Wake Vortex Dynamic Separations Using Computer Simulation and ModelingRoa 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.