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- All Subjects: Transportation
- All Subjects: spatial statistics
- Creators: Salon, Deborah
- 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
Informal public transport is commonplace in the developing world, but the service exists in the United States as well, and is understudied. Often called "dollar vans", New York's commuter vans serve approximately 120,000 people every day (King and Goldwyn, 2014). While this is a tiny fraction of the New York transit rider population, it is comparable to the total number of commuters who ride transit in smaller cities such as Minneapolis/St Paul and Phoenix. The first part of this study reports on the use of commuter vans in Eastern Queens based on a combination of surveys and a ridership tally, all conducted in summer 2016. It answers four research questions: How many people ride the vans? Who rides the commuter vans? Why do they ride commuter vans? Do commuter vans complement or compete against formal transit? Commuter van ridership in Eastern Queens was approximately 55,000 with a high percentage of female ridership. Time and cost savings were the main factors influencing commuter van ridership. Possession of a MetroCard was shown to negatively affect the frequency of commuter van ridership. The results show evidence of commuter vans playing both a competing and complementary role to MTA bus and subway transit. The second part of this study presents a SWOT analysis results of commuter vans, and the policy implications. It answers 2 research questions: What are the main strengths, weaknesses, opportunities and threats of commuter vans in Eastern Queens? and How do the current policies, rules and regulations affect commuter van operation? The SWOT analysis results show that the commuter van industry is resilient, performs a necessary service, and, with small adjustments that will help reduce operating costs and loss of profits have a chance of thriving in Eastern Queens and the rest of New York City. The study also discusses the mismatch between policy and practice offering recommendations for improvement to ensure that commuter vans continue to serve residents of New York City.
ContributorsMusili, Catherine (Author) / Salon, Deborah (Thesis advisor) / King, David (Committee member) / Kelley, Jason (Committee member) / Arizona State University (Publisher)
Created2017
Description
Bicycle sharing systems (BSS) operate on five continents, and they change quickly with technological innovations. The newest “dockless” systems eliminate both docks and stations, and have become popular in China since their launch in 2016. The rapid increase in dockless system use has exposed its drawbacks. Without the order imposed by docks and stations, bike parking has become problematic. In the areas of densest use, the central business districts of large cities, dockless systems have resulted in chaotic piling of bikes and need for frequent rebalancing of bikes to other locations. In low-density zones, on the other hand, it may be difficult for customers to find a bike, and bikes may go unused for long periods. Using big data from the Mobike BSS in Beijing, I analyzed the relationship between building density and the efficiency of dockless BSS. Density is negatively correlated with bicycle idle time, and positively correlated with rebalancing. Understanding the effects of density on BSS efficiency can help BSS operators and municipalities improve the operating efficiency of BSS, increase regional cycling volume, and solve the bicycle rebalancing problem in dockless systems. It can also be useful to cities considering what kind of BSS to adopt.
ContributorsCui, Wencong (Author) / Kuby, Michael (Thesis advisor) / Salon, Deborah (Committee member) / Thigpen, Calvin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Transportation infrastructure in urban areas has significant impacts on socio-economic activities, land use, and real property values. This dissertation proposes a more comprehensive theory of the positive and negative relationships between property values and transportation investments that distinguishes different effects by mode (rail vs. road), by network component (nodes vs. links), and by distance from them. It hypothesizes that transportation investment generates improvement in accessibility that accrue only to the nodes such as highway exits and light rail stations. Simultaneously, it tests the hypothesis that both transport nodes and links emanate short-distance negative nuisance effects due to disamenities such as traffic and noise. It also tests the hypothesis that nodes of both modes generate a net effect combining accessibility and disamenities. For highways, the configuration at grade or above/below ground is also tested. In addition, this dissertation hypothesizes that the condition of road pavement may have an impact on residential property values adjacent to the road segments. As pavement condition improves, value of properties adjacent to a road are hypothesized to increase as well. A multiple-distance-bands approach is used to capture distance decay of amenities and disamenities from nodes and links; and pavement condition index (PCI) is used to test the relationship between road condition and residential property values. The hypotheses are tested using spatial hedonic models that are specific to each of residential and commercial property market. Results confirm that proximity to transport nodes are associated positively with both residential and commercial property values. As a function of distance from highway exits and light rail transit (LRT) stations, the distance-band coefficients form a conventional distance decay curve. However, contrary to our hypotheses, no net effect is evident. The accessibility effect for highway exits extends farther than for LRT stations in residential model as expected. The highway configuration effect on residential home values confirms that below-grade highways have relatively positive impacts on nearby houses compared to those at ground level or above. Lastly, results for the relationship between pavement condition and residential home values show that there is no significant effect between them.
Some differences in the effect of infrastructure on property values emerge between residential and commercial markets. In the commercial models, the accessibility effect for highway exits extends less than for LRT stations. Though coefficients for short distances (within 300m) from highways and LRT links were expected to be negative in both residential and commercial models, only commercial models show a significant negative relationship. Different effects by mode, network component, and distance on commercial submarkets (i.e., industrial, office, retail and service properties) are tested as well and the results vary based on types of submarket.
Consequently, findings of three individual paper confirm that transportation investments mostly have significant impacts on real-estate properties either in a positive or negative direction in accordance with the transport mode, network component, and distance, though effects for some conditions (e.g., proximity to links of highway and light rail, and pavement quality) do not significantly change home values. Results can be used for city authorities and planners for funding mechanisms of transport infrastructure or validity of investments as well as private developers for maximizing development profits or for locating developments.
Some differences in the effect of infrastructure on property values emerge between residential and commercial markets. In the commercial models, the accessibility effect for highway exits extends less than for LRT stations. Though coefficients for short distances (within 300m) from highways and LRT links were expected to be negative in both residential and commercial models, only commercial models show a significant negative relationship. Different effects by mode, network component, and distance on commercial submarkets (i.e., industrial, office, retail and service properties) are tested as well and the results vary based on types of submarket.
Consequently, findings of three individual paper confirm that transportation investments mostly have significant impacts on real-estate properties either in a positive or negative direction in accordance with the transport mode, network component, and distance, though effects for some conditions (e.g., proximity to links of highway and light rail, and pavement quality) do not significantly change home values. Results can be used for city authorities and planners for funding mechanisms of transport infrastructure or validity of investments as well as private developers for maximizing development profits or for locating developments.
ContributorsSeo, Kihwan (Author) / Michael, Kuby (Thesis advisor) / Golub, Aaron (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2016
Description
Bicyclist and pedestrian safety is a growing concern in San Francisco, CA,
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
ContributorsMcIntyre, Andrew (Author) / Salon, Deborah (Thesis advisor) / Kuby, Mike (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
With high potential for automobiles to cause air pollution and greenhouse gas emissions, there is concern that automobiles accessing or egressing public transportation may cause emissions similar to regular automobile use. Due to limited literature and research that evaluates and discusses environmental impacts from first and last mile portions of transit trips, there is a lack of understanding on this topic. This research aims to comprehensively evaluate the life cycle impacts of first and last mile trips on multimodal transit. A case study of transit and automobile travel in the greater Los Angeles region is evaluated by using a comprehensive life cycle assessment combined with regional household travel survey data to evaluate first-last mile trip impacts in multimodal transit focusing on automobile trips accessing or egressing transit. First and last mile automobile trips were found to increase total multimodal transit trip emissions by 2 to 12 times (most extreme cases were carbon monoxide and volatile organic compounds). High amounts of coal-fired energy generation can cause electric propelled rail trips with automobile access or egress to have similar or more emissions (commonly greenhouse gases, sulfur dioxide, and mono-nitrogen oxides) than competing automobile trips, however, most criteria air pollutants occur remotely. Methods to reduce first-last mile impacts depend on the characteristics of the transit systems and may include promoting first-last mile carpooling, adjusting station parking pricing and availability, and increased emphasis on walking and biking paths in areas with low access-egress trip distances.
ContributorsHoehne, Christopher G (Author) / Chester, Mikhail V (Thesis advisor) / Salon, Deborah (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2016
Description
The past two decades have been marked by disruptions in the way transportation is provided to society. Examples are carsharing, ridehailing services, and electric scooters. Understanding how sensitive travel behavior is during transportation disruptions is a key part of planning for the future of transportation. While the effects of people's attitudes and perceptions on travel behavior and choices have been studied in the past, their role in response to disruptions remains under explored. This dissertation explores the effect of attitudes on travel behavior and perceptions for two distinct disruptions: the advent of autonomous vehicles (AVs) and the COVID-19 pandemic. Before diving into such elaborate relationships, it is important to understand how attitudinal data is collected and measured. Thus, a study of the effects of different survey methods on the collection of attitudes towards transportation disruptions is performed. This dissertation finds that having a favorable perception of AVs is the most important factor in defining one’s willingness to use them. More importantly, those who only heard about AVs without knowing much about them were actually less likely to have a favorable perception when compared to those who never heard of AVs prior to the survey, reinforcing the need for thoughtful education and awareness initiatives. Additionally, gender also played an important role in expectations about the AV Future: not only are women less interested in using AVs as a pooled ride service, but also that the effect of attitudes on defining that choice was different for men and women. Regarding the COVID-19 pandemic, two different attitudes towards COVID were identified: concern about the effects of the COVID-19 response, and concern about the health effects of the coronavirus. Both shaped the ways people traveled, and how often they did so. These findings reinforce the need for the broad collection of attitudinal data and the incorporation of such parameters on future travel forecasting.
ContributorsCapasso da Silva, Denise (Author) / Pendyala, Ram M (Thesis advisor) / Khoeini, Sara (Thesis advisor) / Salon, Deborah (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2021
Description
With rapid advances in technology development and public adoption, it is crucial to understand how these services will shape the future of travel depending on the extent to which people will use these services; impact the transportation and infrastructure systems such as changes in the use of transit and active modes of travel; and influence how technology developers create and update these transportation technologies to better serve people’s mobility needs. This dissertation explores how two major emerging services, namely ridehailing services and autonomous vehicles (AVs), will be used in the future when they are widely available and vastly used, and how they may impact the transportation infrastructure and societal travel patterns. The four proposed chapters use comprehensive quantitative and qualitative methods to explore the status of these technologies from theory, through robust modeling frameworks, to practice, by investigating the recent AV pilot deployments in real-world settings. In the second chapter, it was found that increased frequency of ridehailing use is significantly associated with a decrease in bus usage, suggesting that ridehailing functions more as a substitute for buses than as a complement and implying that transit agencies should explore ways to incorporate ridehailing services in their plans to enhance transit usage. Next, the third chapter showed that interest in using AVs for running errands had a positive and significant effect on AV ownership intent, even after accounting for a host of variables. The fourth chapter depicted how ridehailing experiences have a considerable effect on the willingness to ride AV-based services in both private and shared modes, suggesting that experience is crucial for future adoption of these services. Then, two recent real-world AV experiences are explored in the fifth chapter. Lessons learned from these experiments reinforced the importance of first-hand experiences in promoting AV awareness and trustworthiness, potentially leading to greater degrees of adoption. Finally, the results and discussions presented in this dissertation strengthen the body of literature on key emerging transportation technologies and inform policymakers and stakeholders to properly prepare cities and the public to welcome these technologies into our transportation system in an efficient, equitable, and complementary way.
ContributorsMagassy, Tassio Bezerra (Author) / Pendyala, Ram M (Thesis advisor) / Khoeini, Sara (Committee member) / Polzin, Steven E (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2023
Description
Walking and bicycling bring many merits to people, both physically and mentally.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
However, not everyone has an opportunity to enjoy healthy and safe bicycling and
walking. Many studies suggested that access to healthy walking and bicycling is heavily
related to socio-economic status. Low income population and racial minorities have
poorer transportation that results in less walking and bicycling, as well as less access to
public transportation. They are also under higher risks of being hit by vehicles while
walking and bicycling. This research quantifies the relationship between socioeconomic
factors and bicyclist and pedestrian involved traffic crash rates in order to establish an
understanding of how equitable access to safe bicycling and walking is in Phoenix. The
crash rates involving both bicyclists and pedestrians were categorized into two groups,
minor crashes and severe crashes. Then, the OLS model was used to analyze minor and
severe bicycle crash rates, and minor and severe pedestrian crash rates, respectively.
There are four main results, (1) The median income of an area is always negatively
related to the crash rates of bicyclists and pedestrians. The reason behind the negative
correlation is that there is a very small proportion of people choosing to walk or ride
bicycles as their commuting methods in the high-income areas. Consequently, there are
low crash rates of pedestrians and bicyclists. (2) The minor bicycle crash rates are more
related to socio-economic determinants than the severe crash rates. (3) A higher
population density reduces both the minor and the severe crash rates of bicyclists and
pedestrians in Phoenix. (4) A higher pedestrian commuting ratio does not reduce bicyclist
and pedestrian crash rates in Phoenix. The findings from this study can provide a
reference value for the government and other researchers and encourage better future
decisions from policy makers.
ContributorsWu, Feiyi (Author) / Nelson, Trisalyn (Thesis advisor) / Salon, Deborah (Committee member) / Kuby, 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