Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

Runway systems at airports have been identified as a major source of delay in the aviation system and efficient runway operations are, therefore, important to maintain and/or increase the capacity of the entire aviation system. The goal of the airport runway scheduling problem is to schedule a set of aircraft and minimize a given objective while maintaining separation requirements and enforcing other operational constraints. Uncertain factors such as weather, surrounding traffic and pilot behavior affect when aircraft can be scheduled, and these factors need to be considered in planning models. In this thesis we propose two stochastic programs to address the stochastic airport runway scheduling problem and similarly structured machine scheduling problems. In the first part, we develop a two-stage stochastic integer programming model and analyze it by developing alternative formulations and solution methods. As part of our analysis, we first show that a restricted version of the stochastic runway scheduling problem is equivalent to a machine scheduling problem on a single machine with sequence dependent setup times and stochastic due dates. We then extend this restricted model by considering characteristics specific to the runway scheduling problem and present two different stochastic integer programming models. We derive some tight valid inequalities for these formulations, and we propose a solution methodology based on sample average approximation and Lagrangian based scenario decomposition. Realistic data sets are then used to perform a detailed computational study involving implementations and analyses of several different configurations of the models. The results from the computational tests indicate that practically implementable truncated versions of the proposed solution algorithm almost always produce very high quality solutions. In the second part, we propose a sampling based stochastic program for a general machine scheduling problem with similar characteristics as the airport runway scheduling problem. The sampling based approach allows us to capture more detailed aspects of the problem, such as taxiway operations crossing active runways. The model is based on the stochastic branch and bound algorithm with several enhancements to improve the computational performance. More specifically, we incorporate a method to dynamically update the sample sizes in various parts of the branching tree, effectively decreasing the runtime without worsening the solution quality. When applied to runway scheduling, the algorithm is able to produce schedules with makespans that are 5% to 7% shorter than those obtained by optimal deterministic methods. Additional contributions in this thesis include the development of a global cost function, capturing all relevant costs in airport runway scheduling and trading off different, sometimes conflicting, objectives. We also analyze the impact of including environmental factors in the scheduling process.

Now a day, one of the most attractive research topics in the area of Intelligent Traffic Control is Inter-vehicle communication (V2V communication). In V2V communication, a vehicle can communicate to its neighbouring vehicles even in the absence of a central Base Station. The concept of this direct communication is to send vehicle safety messages one-to-one or one-to- many vehicles via wireless connection. Such messages are usually short in length and have very short lifetime in which they must reach the destination. The Inter-vehicle communication system is an ad-hoc network with high mobility and changing number of nodes, where mobile nodes dynamically create temporary sensor networks and transferring messages from one network to others by using multiple hops due to limitation of short range. The goal of the project is to investigate some basic research questions in order to organize such sensor networks and at the same time highlight the appropriate routing protocol that support mobile ad hoc networks in an efficient and reliable manner. In our investigation, we have answered the technical issues in order to construct a V2V communication system. We have also studied some mobile ad hoc network routing protocols in detail and then selected the DSR (Dynamic Source Routing) for our V2V communication and then simulated it according to our system requirements. We are quite satisfied by the result of DSR, but at the same time much more work is required to come up with an absolute application for the end user.

The current project is an examination of the effectiveness of decision-support graphics in a simulated real-world task, and of the role those graphics should play in training. It is also an attempt to apply a theoretical account of memory performance-transfer-appropriate processing-to naturalistic decision making. The task in question is a low-fidelity air traffic control simulation. In some conditions, that task includes decision-support graphics designed to explicitly represent elements of the task that normally must be mentally represented-namely, trajectory and relative altitude. The assumption is that those graphics will encourage a type of processing different from that used in their absence. If so, then according to the theory of transfer-appropriate processing (TAP), the best performance should occur in conditions in which the graphics are present either during both training and testing, or else not at all. For other conditions, the inconsistent presence or absence of the graphics should lead to mismatches in the type of processing used during training and testing, thus hurting performance. A sample of 205 undergraduate students were randomly assigned to four experimental and two control groups. The results showed that the support graphics provided immediate performance benefits, regardless of their presence during training. However, presenting them during training had an apparent overshadowing effect, in that removing them during testing significantly hurt performance. Finally, although no support was found for TAP, some support was found for the similar but more general theory of identical elements.

The air traffic system in the United States is currently undergoing a complete overhaul known as "NextGen". NextGen is the FAA's initiative to update the antiquated National Airspace System (NAS) both procedurally and technologically to reduce costs to the users and negative impacts on the general public. There are currently numerous studies being conducted that are focused on finding optimal solutions to the problems of congestion, delay, and the high fuel and noise footprints associated aircraft operations. These studies require accurate simulation techniques to assess the potential benefits and drawbacks for new procedures and technology. One common method uses air traffic control radar data. As an aircraft travels through the air traffic control system, its latitude, longitude, and altitude are recorded at set intervals. From these values, estimates of groundspeed and heading can be derived. Researchers then use this data to estimate aircraft performance parameters such as engine thrust and aircraft configuration, variables essential to estimate fuel burn, noise, and emissions. This thesis creates a more accurate method of simulating aircraft performance based solely on air traffic control radar data during the arrival process. This tool will allow the benefits of different arrival procedures to be compared at a variety of airports and wind conditions before costly flight testing is required. The accuracy of the performance estimates will be increased using the Tool for Assessing Separation and Throughput (TASAT), a fast-time Monte Carlo aircraft simulator that can simulate multiple arrivals with a mixture of different aircraft types. The tool has succeeded in matching various recorded radar profiles and has produced fuel burn estimates with an RMS error of less than 200 pounds from top of descent to landing when compared to high fidelity operational data. The output from TASAT can also be ported to FAA software tools to make higher quality predictions of aircraft noise and emissions.

Air Traffic Management (ATM) aims at ensuring safe and efficient movement of aircraft in the airspace. The National Airspace System is currently undergoing a comprehensive overhaul known as NextGen. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision making is essential. These tools should aid in accommodating the air traffic throughput increase, while limiting controller workload and ensuring high safety levels. In the National Airspace System (NAS), the goal of en-route Traffic Flow Management (TFM) is to balance air traffic demand against available airspace capacity, in order to ensure a safe and expeditious flow of aircraft, both under nominal and perturbed conditions. The objective of this thesis is to develop a better understanding of how to analyze, model and simulate air traffic in a given airspace, under both nominal and degraded conditions. First, a new framework for en-route Traffic Flow Management and Airspace Health Monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale 3D flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation for optimizing en-route Air Traffic is proposed. It takes into account a controller taskload model based on flow geometry, in order to estimate airspace capacity. The simulations run demonstrate the importance of sector constraints and traffic demand patterns in estimating the throughput of an airspace. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid (VIL) are incorporated in the model, representing weather perturbations on the same data set used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. Simulations under perturbed conditions are then run according to different objectives. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model.

IH-35 is a major north-south interstate highway across the State of Texas. It is an important business corridor, conveniently connecting four large Texas cities, Austin, Dallas, Fort Worth, and San Antonio, as well as facilitating trade between Mexico and the United States. During construction of the SH-71/IH-35 Interchange, the Austin District of the Texas Department of Transportation (TxDOT) has had to close the main lanes of IH-35 and re-route traffic. Three main lane closures happened during three weekends in 2011. During those closures, a parallel route, the SH-130 toll road, was made free to travelers. TxDOT provided both pre-trip and en-route information about the closure. They used radio, TV, portable message sign (PMS), and dynamic message signs (DMS) to inform commuters about the closure. To inform travelers passing through Austin about the closure and the existing alternative (SH-130 was toll free), they even collaborated with Dallas and San Antonio TxDOT district personnel. However, usage of SH130 was less than anticipated, and there was significant traffic queuing on IH-35. In this study, we tried to document the quantity of traffic that used the alternative path during the IH-35 closure and explore options for relieving delays on IH-35 during future closures. / text

The Normal Operations Safety Survey (NOSS) is an observational methodology to collect safety data during normal Air Traffic Control (ATC) operations. It aims to inform organizations about safety matters by using trained ATC staff to take a structured look at everyday operations. By monitoring normal operations through the use of direct over-the-shoulder observations, it is believed that safety deficiencies can be identified in a proactive manner prior to the occurrence of accidents or incidents. NOSS was developed as a collaborative effort between the International Civil Aviation Organization, ATC providers, controller representatives, government regulators, and academics to fill a gap in available ATC safety information. System designers consider three basic assumptions: the technology needed to achieve the system production goals, the training necessary for people to operate the technology, and the regulations that dictate system behavior. These assumptions represent the expected performance. When systems are deployed, however, particularly in realms as complex as ATC, they do not perform quite as designed. NOSS aims to capture the operational drift that invariably occurs upon system deployment. NOSS captures how the ATC system operates in reality, as opposed to how it was intended to operate. NOSS is premised on the Threat and Error Management (TEM) framework. TEM frames human performance in complex and dynamic settings from an operational perspective by simultaneously focusing on the environment and how operators respond to that environment. TEM posits that threats and errors are a part of everyday operations in ATC and must be managed in order to maintain safety margins. This dissertation describes NOSS and its contributions to ATC safety management systems. It addresses the validity and reliability of NOSS data and presents case studies from field trials conducted by a number of ATC providers. / text

This dissertation aims at improving the efficiency, robustness, and flexibility of airport operations through intelligent gate assignment. Traditional research on gate assignment focuses on the accommodation of passengers' demands such as walking time of passengers, and the robustness of gate assignment. In spite of its importance on the ramp operations, there is a lack of research to account ramp congestion when gates are assigned. Therefore, this dissertation proposes a new perspective on the gate assignment that accounts for ramp congestion. For that purpose, a ramp operations model based on observations at Atlanta airport is presented to understand the characteristics of aircraft movement on the ramp. The proposed gate assignment problem minimizes passenger-time spent on ramp areas. In addition, this dissertation is conducted to satisfy the needs of passengers, aircraft, and operations from the perspectives of passengers. Using actual passenger data at a major hub airport, the proposed gate assignment is assessed by means of passengers' transit time, passengers' time spent on the ramp, and passengers' waiting time for a gate. Results show that the proposed gate assignment outperforms the current gate assignment in every metric. This dissertation also analyzes the impact of gate assignment on departure metering, which controls the number of pushbacks in order to reduce airport congestion. Then, some of departing flights are held at gates, so it increases the chance of gate conflict, which reduces the efficiency of departure metering as well as ramp operations. In order to analyze the impact of gate assignment on departure metering, this dissertation simulates departure processes at two airports. Results show that the proposed robust gate assignment reduces the occurrence of gate conflicts under departure metering and helps to utilize gate-holding times to some extent.