Matching Items (7)
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- All Subjects: Transportation
- All Subjects: spatial statistics
- All Subjects: Spatial linear mixed model
- Creators: Ozer, Hasan
- 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
Differences in climatic conditions, aircraft traffic, and maintenance practices drive airfield pavements to perform differently. Through the Federal Aviation Administration’s (FAA’s) PAVEAIR online database and the National Oceanic and Atmospheric Administration’s (NOAA’s) online public platform, historical pavement condition and climate data from nearly 200 airfields in the dry freeze (DF), dry no-freeze (DNF), wet freeze (WF), and wet no-freeze (WNF) climatic regions were collected to evaluate pavement performance and distress trends. This research details the methodologies employed in the PAVEAIR pavement inspection data retrieval and dataset organization, and further presents the results of a two-part analysis. First, rate of deterioration (ROD) of various pavement families were evaluated by fitting a linear regression to the pavement condition index (PCI). Then, historical distresses data were analyzed for various pavement families in the different climatic regions. Families were assigned with respect to climate, pavement structure (conventional asphalt or asphalt overlays), and branch type (apron, taxiway, and runway).
The regression results showed that pavements in the WF region have the highest ROD, followed by the pavements in the DNF region. In terms of branch type, in three of four climatic regions, aprons have the fastest rate of deterioration, followed by taxiways and runways, respectively. The distress analytics revealed that cracking type of distresses were the most common in all the regions regardless of the pavement family.
The results showed that climatic data alone were not adequate to characterize airfield pavement behavior due to the multivariate factors affecting pavement deterioration. An accurate pavement and distress prediction modeling effort should at least include additional information on the structure and traffic level.
ContributorsDuah, Ebenezer (Author) / Ozer, Hasan (Thesis advisor) / Kaloush, Kamil E (Committee member) / Mamlouk, Michael S (Committee member) / Arizona State University (Publisher)
Created2021
Description
Thin overlays are favored by local agencies due to their ability to extend the pavement'slifespan and enhance ride quality. However, the low thickness of thin overlays presents
some inherent challenges. The use of conventional mixes for constructing thin overlays has
led to numerous premature failures, primarily due to the relationship between compaction,
the Nominal Maximum Aggregate Size (NMAS), and lift thickness.
The current study's objective was to utilize a balanced mix design to enhance the quality
of mixes used by local agencies by developing two new dense-graded mixes and one Stone
Matrix Asphalt (SMA) mix. Local mixes were collected and studied, working closely with
industry experts. This research work aimed to identify the performance characteristics of
commonly used mixes, optimize these mixes, and design new mixes that better suit their
intended application, thereby prolonging the life of overlays.
The findings indicated that while the current mix designs are fundamentally wellstructured, they are not appropriate for the given application due to the unsuitability of a
12.5 mm NMAS for mix designs below 38 mm, especially considering that most overlays
are less than that. The results also showed that the current mixes are already optimized in
terms of cracking and rutting resistance.
Three new mixes with 9.5 mm NMAS aggregates and SBS modified binder were
designed. These include two dense-graded mixes using PG 76-22 SBS and PG 70-28 SBS
modified binders, and one SMA mix utilizing the PG 76-22 SBS modified binder. All theseii
mixes demonstrated better cracking properties compared to commonly used mixtures.
While their rutting properties were either comparable or occasionally inferior but meeting
the rutting criteria.
Based on these findings, it can be proposed that the use of a 9.5 mm NMAS mix
improves compaction and compatibility with lift thickness. Additionally, these mixes
reduce susceptibility to cracking and extend service life of the overlay. To get a superior
overlay mix, SMA can be employed as it had 2.5 times better CT Index compared to the
conventional 12.5 mm mix.
ContributorsHasan, Morshed Washif (Author) / Kaloush, Kamil E (Thesis advisor) / Noorvand, Hossein (Thesis advisor) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Thermal susceptibility is one of the biggest challenges that asphalt pavements must overcome. Asphalt mixture’s thermal susceptibility can increase problems related to permanent deformation, and the expansion-contraction phenomenon triggers thermal cracking. Furthermore, there is a common worldwide interest in environmental impacts and pavements. Saving energy and mitigating the urban heat island (UHI) effect have been drawing the attention of researchers, governments, and industrial organizations. Pavements have been shown to play an important role in the UHI effect. Globally, about 90% of roadways are made of asphalt mixtures. The main objective of this research study involves the development and testing of an innovative aerogel-based product in the modification of asphalt mixtures to function as a material with unique thermal resistance properties, and potentially providing an urban cooling mechanism for the UHI. Other accomplishments included the development of test procedures to estimate the thermal conductivity of asphalt binders, the expansion-contraction of asphalt mixtures, and a computational tool to better understand the pavement’s thermal profile and stresses. Barriers related to the manufacturing and field implementation of the aerogel-based product were overcome. Unmodified and modified asphalt mixtures were manufactured at an asphalt plant to build pavement slabs. Thermocouples installed at top and bottom collected data daily. This data was valuable in understanding the temperature fluctuation of the pavement. Also, the mechanical properties of asphalt binders and mixtures with and without the novel product were evaluated in the laboratory. Fourier transform infrared (FTIR) and scanning electron microscope (SEM) analyses were also used to understand the interaction of the developed product with bituminous materials.
The modified pavements showed desirable results in reducing overall pavement temperatures and suppressing the temperature gradient, a key to minimize thermal cracking. The comprehensive laboratory tests showed favorable outcomes for pavement performance. The use of a pavement design software, and life cycle/cost assessment studies supported the use of this newly developed technology. Modified pavements would perform better than control in distresses related to permanent deformation and thermal cracking; they reduce tire/pavement noise, require less raw material usage during their life cycle, and have lower life cycle cost compared to conventional pavements.
ContributorsObando Gamboa, Carlos Javier (Author) / Kaloush, Kamil (Thesis advisor) / Mamlouk, Michael (Committee member) / Ozer, Hasan (Committee member) / Fini, Elham (Committee member) / Zapata, Claudia (Committee member) / Arizona State University (Publisher)
Created2022
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
United States Air Force airfield PAVER pavement management system enterprise data was reviewed for 67 networks. The distress survey extents and severity fields were joined with treatment costs estimated using RSMeans to determine the costliest distress. In asphalt surfaced pavements Longitudinal/transverse cracking, weathering, and block cracking resulted in the most pavement condition index (PCI) deducts while the costliest distresses are weathering, block cracking and longitudinal cracking. In portland cement concrete surfaced pavements linear cracking, joint seal damage, and joint spalling resulted in the most PCI deducts while the costliest distresses are joint seal damage, linear cracking, and corner spalling. The results of this data were then compared to airfield attributes: Pavement Temperature Group, Dominant American Association of State Highway and Transportation Officials (AASHTO) Soil Classification, Pavement- Transportation Computer Assisted Structural Engineering (PCASE) Climate Zone, and years since last maintenance. Maps showing the Pavement Temperature Group, Dominant AASHTO Soil Classification, and PCASE Climate Zone are included in Appendix A. Alligator cracking is most prevalent at the airfields with PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt) and 58-22 (Niagara and Vandenberg). Rutting is most prevalent at PTG 64-34 (Ellsworth, Fairchild, Hill, and Offutt). An increasing trend of joint spalling, corner spalling, and corner break with decreasing soil quality (AASHOTO A-1 to A-8 soils). The PCASE Climate Zone Cost Indices the cost index for weathering is approximately double in the moist region over the dry region. The cost index for block cracking is approximately double in the cold region over the hot region. It is recommended that the agency review its pavement performance modeling in the pavement management system to increase the recommendation of pavement preservation treatments and review the use of higher quality materials for pavement maintenance treatments.
ContributorsThevenot, Ronald (Author) / Kaloush, Kamil (Thesis advisor) / Mamlouk, Michael S. (Thesis advisor) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Several ways exist to improve pavement performance over time. One suggestion is to tailor the asphalt pavement mix design according to certain specified specifications, set up by each state agency. Another option suggests the addition of modifiers that are known to improve pavement performance, such as crumb rubber and fibers. Nowadays, improving asphalt pavement structures to meet specific climate conditions is a must. In addition, time and cost are two crucial settings and are very important to consider; these factors sometimes play a huge role in modifying the asphalt mix design needed to be set into place, and therefore alter the desired pavement performance over the expected life span of the structure. In recent studies, some methods refer to predicting pavement performance based on the asphalt mixtures volumetric properties.
In this research, an effort was undertaken to gather and collect most recent asphalt mixtures’ design data and compare it to historical data such as those available in the Long-Term Pavement Performance (LTPP), maintained by the Federal Highway Administration (FHWA). The new asphalt mixture design data was collected from 25 states within the United States and separated according to the four suggested climatic regions. The previously designed asphalt mixture designs in the 1960’s present in the LTPP Database implemented for the test sections were compared with the recently designed pavement mixtures gathered, and pavement performance was assessed using predictive models.
Three predictive models were studied in this research. The models were related to three major asphalt pavement distresses: Rutting, Fatigue Cracking and Thermal Cracking. Once the performance of the asphalt mixtures was assessed, four ranking criteria were developed to support the assessment of the mix designs quality at hand; namely, Low, Satisfactory, Good or Excellent. The evaluation results were reasonable and deemed acceptable. Out of the 48 asphalt mixtures design evaluated, the majority were between Satisfactory and Good.
The evaluation methodology and criteria developed are helpful tools in determining the quality of asphalt mixtures produced by the different agencies. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure.
In this research, an effort was undertaken to gather and collect most recent asphalt mixtures’ design data and compare it to historical data such as those available in the Long-Term Pavement Performance (LTPP), maintained by the Federal Highway Administration (FHWA). The new asphalt mixture design data was collected from 25 states within the United States and separated according to the four suggested climatic regions. The previously designed asphalt mixture designs in the 1960’s present in the LTPP Database implemented for the test sections were compared with the recently designed pavement mixtures gathered, and pavement performance was assessed using predictive models.
Three predictive models were studied in this research. The models were related to three major asphalt pavement distresses: Rutting, Fatigue Cracking and Thermal Cracking. Once the performance of the asphalt mixtures was assessed, four ranking criteria were developed to support the assessment of the mix designs quality at hand; namely, Low, Satisfactory, Good or Excellent. The evaluation results were reasonable and deemed acceptable. Out of the 48 asphalt mixtures design evaluated, the majority were between Satisfactory and Good.
The evaluation methodology and criteria developed are helpful tools in determining the quality of asphalt mixtures produced by the different agencies. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure.
ContributorsKaram, Jolina Joseph (Author) / Kaloush, Kamil (Thesis advisor) / Mamlouk, Michael (Thesis advisor) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2020