Matching Items (7)
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- All Subjects: Transportation
- All Subjects: spatial statistics
- Creators: Niemczyk, Mary
- Status: Published
Description
As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
ContributorsOshan, Taylor Matthew (Author) / Fotheringham, A. S. (Thesis advisor) / Farmer, Carson J.Q. (Committee member) / Rey, Sergio S.J. (Committee member) / Nelson, Trisalyn (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Federal Flight Deck Officer (FFDO) program was mandated legislatively, as part of the Homeland Security Act of 2002. This study replicated earlier research that investigated pilots’ opinions of the current state of the FFDO program based on interviews. A Likert survey was created to allow simpler quantitative collection and analysis of opinions from large groups of pilots. A total of 43 airline pilots participated in this study. Responses to the Likert questions were compared with demographics, searching for significance through a Pearson chi-square test and frequencies were compared to earlier research findings. Significant chi-square results showed that those familiar with the program were more likely to agree the program should continue, it was effective, the screening and selection process of program applicants was adequate and the Federal Air Marshal Service’s management of the FFDO program was effective. Those with Military experience were more likely to disagree it was reasonable that FFDOs were required to pay for their own room and board during training or train on their own time. All those who shared an opinion agreed there should be a suggestion medium between FFDOs and their management. Unlike the prior study, all those familiar with the program agreed the weapons transportation and carriage procedures were adequate. Furthermore, all those who shared an opinion found the holster locking mechanism adequate, which was another reversal of opinion from the prior study. Similar to the prior study, pilots unanimously agree FFDOs were well trained and agreed that the program was effective and should continue.
ContributorsFerrara, Marc, M.S (Author) / Niemczyk, Mary (Thesis advisor) / Nullmeyer, Robert (Committee member) / Branaghan, Russell (Committee member) / Arizona State University (Publisher)
Created2017
Description
This study examined the impact of Situation Presence Assessment Method (SPAM) administration on air traffic control (ATC) students’ task workload and performance in high-fidelity ATC simulations. ATC students performed high-fidelity en-route simulations in two conditions: baseline conditions (without SPAM questions) and SPAM conditions. The data collected show that while workload in the two conditions were not significantly different, there was a trend of higher mental workload in SPAM conditions than in baseline conditions. Performance immediately following SPAM questions was revealed to be poorer than that preceding the SPAM questions and that over the equivalent time periods in the baseline conditions. The results suggest that a "Ready" signal before a SPAM question may not be enough to eliminate the impact of SPAM administration on ATC students’ workload and performance in high-fidelity en-route simulations.
ContributorsZhang, Chao, M.S (Author) / Niemczyk, Mary (Thesis advisor) / Pearson, Michael (Committee member) / Nullmeyer, Robert (Committee member) / Arizona State University (Publisher)
Created2016
Description
To ensure safety is not precluded in the event of an engine failure, the FAA has
established climb gradient minimums enforced through Federal Regulations.
Furthermore, to ensure aircraft do not accidentally impact an obstacle on takeoff due to
insufficient climb performance, standard instrument departure procedures have their own
set of climb gradient minimums which are typically more than those set by Federal
Regulation. This inconsistency between climb gradient expectations creates an obstacle
clearance problem: while the aircraft has enough climb gradient in the engine inoperative
condition so that basic flight safety is not precluded, this climb gradient is often not
strong enough to overfly real obstacles; this implies that the pilot must abort the takeoff
flight path and reverse course back to the departure airport to perform an emergency
landing. One solution to this is to reduce the dispatch weight to ensure that the aircraft
retains enough climb performance in the engine inoperative condition, but this comes at
the cost of reduced per-flight profits.
An alternative solution to this problem is the extended second segment (E2S)
climb. Proposed by Bays & Halpin, they found that a C-130H gained additional obstacle
clearance performance through this simple operational change. A thorough investigation
into this technique was performed to see if this technique can be applied to commercial
aviation by using a model A320 and simulating multiple takeoff flight paths in either a
calm or constant wind condition. A comparison of takeoff flight profiles against real
world departure procedures shows that the E2S climb technique offers a clear obstacle
clearance advantage which a scheduled four-segment flight profile cannot provide.
established climb gradient minimums enforced through Federal Regulations.
Furthermore, to ensure aircraft do not accidentally impact an obstacle on takeoff due to
insufficient climb performance, standard instrument departure procedures have their own
set of climb gradient minimums which are typically more than those set by Federal
Regulation. This inconsistency between climb gradient expectations creates an obstacle
clearance problem: while the aircraft has enough climb gradient in the engine inoperative
condition so that basic flight safety is not precluded, this climb gradient is often not
strong enough to overfly real obstacles; this implies that the pilot must abort the takeoff
flight path and reverse course back to the departure airport to perform an emergency
landing. One solution to this is to reduce the dispatch weight to ensure that the aircraft
retains enough climb performance in the engine inoperative condition, but this comes at
the cost of reduced per-flight profits.
An alternative solution to this problem is the extended second segment (E2S)
climb. Proposed by Bays & Halpin, they found that a C-130H gained additional obstacle
clearance performance through this simple operational change. A thorough investigation
into this technique was performed to see if this technique can be applied to commercial
aviation by using a model A320 and simulating multiple takeoff flight paths in either a
calm or constant wind condition. A comparison of takeoff flight profiles against real
world departure procedures shows that the E2S climb technique offers a clear obstacle
clearance advantage which a scheduled four-segment flight profile cannot provide.
ContributorsBeard, John Eng Hui (Author) / Takahashi, Timothy T (Thesis advisor) / White, Daniel (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2017
Description
There are significantly higher rates of pilot error events during surface operations at night than during the day. Events include incidents, accidents, wrong surface takeoffs and landings, hitting objects, turning on the wrong taxiway, departing the runway surface, among others. There is evidence to suggest that these events are linked to situational awareness. Improvements to situational awareness can be accomplished through training to instruct pilots to increase attention outside of the cockpit while taxiing at night. However, the Federal Aviation Administration (FAA) night time requirements are relatively low to obtain a private pilot certification. The purpose of this study was to determine the effect of flight training experience on conducting safe and incident-free surface operations at night, collect pilot opinions on night training requirements and resources, and analyze the need for night time on flight reviews. A survey was distributed to general aviation pilots and 239 responses were collected to be analyzed. The responses indicated a higher observed incident rate at night than during the day, however there were no significant effects of night training hours or type of training received (Part 61, Part 141/142, or both) on incident rate. Additionally, higher total night hours improved pilot confidence at night and decreased incident rate. The overall opinions indicated that FAA resources on night flying were effective in providing support, but overall pilots were not in support of or against adding night time requirements to flight reviews and found night training requirements to be somewhat effective.
ContributorsWhittard, Megan (Author) / Niemczyk, Mary (Thesis advisor) / Nullmeyer, Robert (Committee member) / Hampshire, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020
Description
Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict a human error event, Loss of Separation (LOS). Six retired air traffic controllers were recruited and tested in three conditions of varying workload in an Terminal Radar Approach Control Facility (TRACON) arrival radar simulation. Communication transcripts from simulated trials were transcribed and coding schemes for Closed Loop Communication Deviations (CLCD) were applied. Results of the study demonstrated a positive correlation between CLCD and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can be used to predict LOS independent of other predictor variables, and if CLCD can be used in a model that considers many different predictor variables to predict LOS.
ContributorsLieber, Christopher Shane (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2020