Willingness to make sacrifices within a romantic relationship can indicate the well-being and stability of that relationship. Why people make these sacrifices are fueled by different types of motivations, such as whether they truly enjoy taking care of their partner (intrinsic and altruistic motivations) or whether they feel pressured or obligated to make a sacrifice (extrinsic motivation). The hypotheses were that those who received affectionate touch would be more willing to sacrifice and have a higher intrinsic and altruistic motivation to do so than those who do not receive touch. Affectionate touch also was predicted to promote willingness to sacrifice by making one’s relationship more salient and thus leading one to be more focused on their partner’s needs. Inconsistent with expectations, receiving no touch led to more altruistic motivation than receiving touch. Although touch did not influence participants' relationship salience, the higher a participant's relationship salience was, the more altruistic their motives were.In addition, motivations for sacrifice predicted willingness to sacrifice in expected ways. While our hypotheses were not supported in terms of touch, we discuss possible explanations and the implications of other results.
The rapid growth in urban population poses significant challenges to moving city dwellers in a fast and convenient manner. This thesis contributes to solving the challenges from the viewpoint of public transit passengers by improving their on-vehicle experience. Traditional transportation
research focuses on pursuing minimal travel time of vehicles on the road network, paying no attention to people inside the vehicles. In contrast, the research in this thesis is passenger-driven, concerning the role of the on-vehicle experience in mobility planning through the public transit
systems. The primary goal of the thesis is to address the following problem: Given an urban public transit network, how can we plan for the optimal experience of passengers in terms of their service preference? There are several challenges we have to address to meet this goal. First, a model or a simulator that captures not only the road traffic, but also the behaviors of passengers and other relevant
factors is a prerequisite for this research but has seldom been developed previously. Second, to plan for passengers’ mobility concerning the influence among passengers as well as multiple service preferences is computationally intensive, especially on a city scale. To achieve the research goal and overcome the challenges, this thesis develops a joint traffic passenger simulator, which simulates the road traffic, behaviors of passengers and on-vehicle environment dynamics. Specifically, the simulator combines the urban road traffic, the interactions among the passengers and the infrastructures that support certain on-vehicle services,
such as on-vehicle Wi-Fi, to provide a passenger-level simulation. A separate passenger behavior model and on-vehicle Wi-Fi service model are designed to run jointly with SUMO, a mature traffic simulator, for simulating the passenger behaviors and on-vehicle travel experience. A
joint simulator for the bus transit system in the city of Porto, Portugal has been implemented and tested by comparing the simulation to the real passenger data. To configure the background passenger flow in the simulation, real passenger data are used. The data were collected by an entry-only system and the destination information was missing.
This thesis contributes a machine learning algorithm, called semi-supervised self-training, to infer the missing destinations with a high inference confidence level.
Given the simulation platform, the passenger mobility planning problem can be formalized as a multi-agent path planning (MAPP) problem, where multiple passengers may interfere with each other when contending for service resources. The mobility planning operates on the client passengers (i.e., a subset of the overall passengers who request the planning service from our planner). State-of-the-art MAPP solvers, such as M*, do not scale well to such a MAPP problem. This thesis proposes the soft-collision-free M* (SC-M*), a generalized version of M*, to efficiently
handle the MAPP task under complex urban environments (i.e., with a large client passenger size and multiple types of client passengers requesting multiple types of service resources). We evaluate the performance of the SC-M* through a case study of the bus transit system in Porto,
Portugal and the experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability in run time and success rate.
The global sharing economy, e.g., AirBnB and Uber, is projected to generate roughly $335 billion by 2025.
The rise of sharing economy has drawn enormous attention from academia and led to policy intervention debates. However, three questions that are essential to a better understanding of sharing economies remain unanswered: 1) can we identify, from unstructured data (product images), the key dimensions of interpretable attributes that affect consumers’ choices, and provide guidelines for sharing economy platform for optimizing images to improve the product demand, 2) can a scalable economic model be developed to disentangle factors that influence AirBnB hosts’ decisions on the type of property photos to post, and to explore photograph policies that platforms such as AirBnB can employ to improve the profitability for both
the hosts and the platform, and 3) are there demand interactions/externalities that arise across sharing
economies to provide policy implication. This dissertation contributes to the relevant literature by filling the gap. To achieve this objective, I apply economic theory to a large-scale demand data leveraging advanced machine learning techniques in computer vision and deep learning models.
In the first chapter, I investigate the economic impact of images and lower-level image factors that influence property demand in AirBnB. Employing Difference-in-Difference analyses on a sixteen-month AirBnB panel dataset spanning 7,423 properties, I find that units with verified photos (taken by AirBnB photographers) generate 8.9% more demand, or $3,500 more revenue per year on average. Leveraging deep learning techniques to classify aesthetic quality of more than 510,000 property photos, I show that 41% of the coefficient of verified photos is explained by the high image quality in these photos. Next, I identify 12
human-interpretable image attributes from photography and marketing literature relevant for real estate photography that capture image quality as well as consumer taste. I quantify (using computer vision algorithms) and characterize unit images to evaluate the empirical marginal effects of these interpretable attributes on demand. The results reveal that verified images not only differ significantly from low-quality
photos, but also from high-quality unverified photos on most of these features. The treatment effect of verified photos becomes statistically insignificant once controlling for these 12 attributes, suggesting that AirBnB’s photographers not only improve the quality of the image but also align it with the taste of potential consumers. This implies there is significant value in optimizing images in e-commerce settings on these attributes. From an academic standpoint, this study provides one of the first large-scale empirical evidence that directly connects systematic lower-level and interpretable image attributes to product demand. This
contributes to, and bridges, the photography and marketing (e.g., staging) literature, which has traditionally ignored the demand side (photography) or did not implement systematic characterization of images (marketing). Lastly, these results provide immediate insights for housing and lodging e-commerce managers (of AirBnB, hotels, realtors, etc.) to optimize product images for increased demand. In the second chapter, I investigate how AirBnB hosts make decisions on the quality of property images to post. Prior literature has shown that the images play the role of advertisements. Particularly, compared to lower quality amateur images, high quality professional images can increase the present demand by approximately 9% (Zhang et al. 2018). However, there exist a large number of amateur images on AirBnB, even when AirBnB was providing professional photography service for free to all the hosts. I posit that the host’s decision on what quality of images to post depends not only on the advertising impact of images on the present demand and on the cost of images, but also on the impact of images on the future demand. Thus,
some hosts would be hesitant to post professional images because professional images can create unrealistically high expectations for the guests, especially if the actual property is not as good as what the images portray and if the hosts are unable to provide a high-level service to match those expectations. This would result in the satisfaction level of guests to decrease, who would then write a bad review or not write any review at all; and since the number/quality of reviews is one of the key drivers in generating new
bookings, this will adversely affect the future demand. I build a structural model of demand and supply, where the demand side entails modeling of guests’ decisions on which property to stay, and the supply side entails modeling of hosts’ decisions on what quality of images to post and what level of service to provide in each period. I estimate the model on a unique one-year panel data consisting of a random sample of 958 AirBnB properties in Manhattan (New York City) where I observe hosts’ monthly choices of the quality of
images posted and the level of service provided. The key findings are: 1) guests who pay more attention to
images tend to care more about reviews, 2) hosts incur considerable costs for posting above-average quality
of image, and 3) hosts are heterogenous in their abilities in investing service effort. In counterfactual analyses, I compare the impact of the current photography policy (offering free high-level images to hosts) and of two proposed policies (offering a menu of free medium-level images to hosts) on the property demand. I show that the proposed policies, though dominated by the current policy in the short-run, outperform the currently policy in the long-run. Noticeably, hosts who might end up using amateur images
to avoid the dissatisfactory gap under the current policy, now use free medium-level images to make more
revenues under the proposed policy. In the third chapter, I examine how ride sharing services such as Uber/Lyft affect the demand for home sharing services such as AirBnB. The existing research has largely focused on the impact of sharing economy on incumbent industries while ignoring the interactions among sharing economies. In this study,
I examine how ride sharing services such as Uber and Lyft affect the demand for home sharing services such as AirBnB. The identification strategy hinges on a natural experiment where Uber and Lyft exited Austin in May 2016 in response to the introduction of new regulations in Austin that targeted ride sharing services. Applying the Difference-in-Difference approach on a 9-month balanced longitudinal data spanning 7,300 AirBnB properties across 7 US cities, I find that the exit of Uber/Lyft led to a decrease of 9.6% in the AirBnB property demand, which is equivalent to a decrease of $6,482 in the annual revenue to the host of an average property. I further find that the exit of Uber/Lyft reduced the (geographic) demand dispersion of AirBnB. The demand became more concentrated in areas with access to better public transportation services. Moreover, the properties farther from downtown experienced greater decreases in their demand in the absence of Uber/Lyft. The results indicate that Uber and Lyft affect the demand for
AirBnB properties primarily by reducing the transportation costs to and from AirBnB properties that otherwise have poor access to transportation services. The research effort is a first step toward understanding the positive externalities between sharing economies and provides policy implication.
Two experimental EBSD data sets acquired at the Universite de Lorraine, France, along with data analysis using the commercial indexing software as well as a new spherical indexing approach. All input and output files from the data analysis are made available.
This thesis contributes new knowledge toward understanding the relationship between capacity procurement and power system reliability through rigorous analysis of generator-level availability data. In Chapter 2 I analyze four years of data (2012-2015) from the Generating Availability Data System (GADS) database maintained by the North American Electric Reliability Corporation
(NERC) to evaluate key assumptions made by power system planners when determining capacity requirements. Using block subsampling and binomial modeling, I demonstrate that large unavailable capacity events have occurred with much greater frequency than should be expected if current-practice assumptions hold.
In Chapter 3 I propose a nonhomogeneous Markov model to explain the observed correlated failures. I use logistic regression to fit a simple model specification that allows generator transition probabilities to depend on ambient temperatures and system load. I fit the model using 23 years of GADS data for the PJM Interconnection (PJM), the largest system operator by generation capacity in North America. Temperature and load are each statistically significant for two-thirds of generators. Temperature dependencies are observed in all generator types, but are most pronounced for diesel and natural gas generators at low temperatures and nuclear generators at high temperatures. The nonhomogeneous Markov model predicts system-level unavailable capacity substantially better than
the homogeneous Markov model used currently by industry.
In Chapter 4, joint work with Luke Lavin, I quantify the reliability risks implied by temperature dependence in PJM’s generator fleet. We modify an open-source resource adequacy modeling tool to allow generator availability to depend on temperature. We then parameterize the tool for PJM’s system using temperature-dependent forced outage rates developed in Chapter 3. We find that temperature dependence substantially increases capacity requirements to achieve the target level of reliability, though PJM procures still more than our model finds is required. Given the seasonality in temperatures and loads, we also demonstrate that average annual capacity requirements could be significantly reduced were PJM to set separate monthly targets, rather than a single annual target. Finally, we explore the resource adequacy implications of various future generator resource and climate change scenarios for PJM.
My first chapter explores the relationship between readmission reduction efforts and hospital costs. A total hospital operating cost function is estimated using
over 5,000 observations from 2,129 US hospitals from the period 2012 - 2017. Using these cost estimates, I estimate a hospitals marginal cost of a 1% readmission
reduction for a single monitored disease. The average marginal cost of reducing risk-adjusted readmission rates for monitored diseases varies from $1,186,689 to $3,844,643. Significantly higher marginal cost are found for hospitals
with the highest number of dual-eligible patients, with hospitals spending up to an extra $839,027 to reduce readmission rates. These results contribute to
the growing literature on the burden of quality incentive programs on hospitals serving disproportionately low-income populations. The second chapter adds to the growing literature on the Hospital Readmission Reduction Program by describing the financial incentives faced by hospitals, estimating their magnitude and distribution, and testing whether hospitals facing larger financial incentives are more likely to improve performance. I estimate the magnitude of the expected future penalty for one additional readmission
across hospitals and procedures and find that on average hospitals can expect a penalty increase two periods in the future for one additional readmission today of: $27,906.74 for an additional AMI readmission, $39,161.94 for heart
failure, and $30,574.30 for an additional pneumonia admission. I find evidence that hospitals improve their readmission rates over time for the monitored conditions
for which they have the highest marginal incentives to improve. I also find evidence that approximately 30% of hospitals have no incentive to improve performance on any condition in a given year. In the third chapter, I investigate the effect of observed hospital quality measures
on patient demand for elective procedures. Using patient-level data from the state of Florida, I estimate a multinomial logit demand model using patient comorbidities and distance between patient zipcode and hospital zipcodes to
identify the effect of a marginal decrease in Hip and Knee Replacement complication rates on hospital demand. Previous literature has investigated the impact
of changes in readmission and mortality rates on hospital demand, but have not looked into complication rates. The findings indicate that patients have a significant
willingness to travel for improved quality measures, including lower complication rates for elective hip and/or knee replacement, lower 30-day readmission rates and lower in-hospital mortality rates for patients with serious treatable
conditions. Patient preference heterogeneity inputs older patients being less willing to travel further distances.
Congestive heart failure (HF) is a complex disease that remains one of the leading causes of death in the world today, affecting over 5.5 million people in the United States alone and contributing to 1 in every 9 deaths nationwide. Currently, total heart transplantation is considered
the most effective treatment for end stage HF, but there are on average 3000 donor hearts available annually, while there are more than 3500 patients on the transplant waitlist on any given day. For those patients, mechanical circulatory support (MCS), as a bridge-to-transplant (BTT) or more permanent destination therapy (DT), has been employed as an effective alternative for end stage HF patients. However, long-term use of these devices are associated
with life-threatening complications, the most common of which are thromboembolic events triggered by artificial blood-contacting surfaces and hemolysis due to high shear stresses generated by blood flow through MCS devices.
The goal of this research is to develop a torsion-based ventricular assist device (tVAD) to support the failing heart as either a BTT or DT while eliminating the risk of thromboembolic complications common to all cardiac assist devices currently on the market by avoiding blood
contact with artificial surfaces. This approach to cardiac support is inspired by the contractile mechanics of healthy human hearts, which produce a “wringing” motion during systole that allows the ventricles to empty more completely and reduces transmural stresses acting on the heart walls. This dissertation describes: 1) parametric computational simulations used to evaluate the effects of applied apical torsion (AAT) on global cardiovascular hemodynamics to determine optimal design parameters and their effects on regional cardiac biomechanics and determine the
working limitations of such applied torsion therapy; and 2) development of a method for superficial attachment of the tVAD to the epicardium of the heart. Results from the parametric computational simulations representing the most aggressive level of tVAD assist, where the applied rotation angle was 75 degrees and the device coverage
area was 24% up the ventricle (from apex towards the base), yielded increases in left ventricular ejection fraction and stroke work of 49% and 72%, respectively, when compared to a baseline HF model. However, based on the evaluation of regional cardiac biomechanics at the epicardial
and endocardial nodes at the base of the device and the ventricle, applied rotation angles of 65 degrees resulted in large increases in maximum principal strains (ΔE), where all nodes had ΔE ≥0.40, and increases in maximum principal stresses (ΔT), where nearly 75% of the nodes at
ΔT>100 kPa. These results both suggest that supra-physiological levels of AAT could potentially cause damage to the myocardium. Additionally, results of lap-shear tests for the adhesion energies of candidate surgical adhesives suggest that the 316L stainless steel bonded
with an octyl/butyl cyanoacrylate bioadhesive has the potential to secure the tVAD to the epicardium as it actuates on the heart.
With the convergence within the digital ecosystem today, access to digital technology is now a multilevel phenomenon closely tied to the access to one or more of the following: a compatible device, the Internet, and a facilitating service/application. This makes it difficult to disentangle
mobile services and Internet services in studies on the digital divide. As countries in Sub-Saharan Africa (SSA), which have the lowest adoption rates globally, look to leverage digital technologies as a tool to drive economic and social development, there is a need for continued and novel approaches to understanding the digital divide. This thesis proposes a new approach to conceptualizing the digital divide and characterizes the three levels of the digital divide:
inequalities in access, use, and benefits from use in SSA, using Nigeria as a case study. This work also critically examines the effect of recent pricing policies in Nigeria on the digital divide, as well as the effect of other sociodemographic, socioeconomic, and behavioral factors at the individual level. In Chapter 2, I run a choice-based conjoint experiment to understand the impact of access to
over-the-top (OTT) services on individual preferences for different mobile services – cellular calls, text, or the different services on the Internet – or not using any mobile service (the first level digital divide). I find that when OTTs are introduced into the market, mobile users are less
likely to go without mobile services or to use a traditional service. I also find that this effect is significant in a market with a pay-as-you-go business model. The results also reveal that mobile users are price sensitive, therefore pricing policies may aid in bridging the first and second levels
of the digital divide. The findings indicate that customers’ preferences in the mobile market are changing and OTT access could be a tool in closing the first-level digital divide. Therefore, I recommend that policies to drive Internet access, especially OTT, should be explored. In Chapter 3, I use a panel data approach to estimate the effect of reduction in the prices of mobile Internet plans on the volume of use of the Internet, cellular calls, and text messaging services (the second-level digital divide). I find that the reduction in the prices is associated with an increase in the volume of data used and a decrease in the volume of texts sent by an individual. However, reducing the prices of mobile Internet plans does not “close” the second level digital divide across socioeconomic groups. I did not see a convergence in the volume of use of any of the mobile services across any demographic subgroups. These findings suggest that more robust policies that are targeted at specific subgroups are needed to reduce the existing
second-level mobile technology digital divide that exists in developing countries. In Chapter 4, I draw on the Uses and Gratifications Theory, the Unified Theory of Acceptance
and Use of Technology, and factor analysis to examine the differences in the frequency and type of Internet use (second-level digital divide) and the differences in outcomes from Internet use (third-level digital divide). I find that females, the older population, and individuals with a lower
level of education are the digitally disadvantaged subset of the population. I also find that high technical skills are associated with high frequency of use of the Internet for personal development, social, and business activities. I also find that encouragement from family and friends as well as intention to increase Internet use in the future are associated with increased frequency of use of the Internet for consuming news content and social interaction respectively. This supports arguments in an earlier work that improving access to over-the-top applications such as WhatsApp could increase Internet use. In examining the determinants of the third-level digital divide, I find that using the Internet for social activities such as using social networks and communicating with family and friends have the greatest impact on offline outcomes. I also find
that individuals with a high level of education are more likely to get positive health outcomes and less likely to get personal development outcomes, such as getting a job or completing an online training, from the Internet.
In Chapter 5, I discuss this work’s contribution to the literature and some of the policy implications of the findings in Chapters 2 through 4. Findings in Chapters 2 through 4 suggest that supporting the use of OTT and social networks in developing countries would have benefits. These Internet activities, although they may not directly contribute to personal development or economic gains, typically require little technical skills. Therefore, by engaging with these
Internet activities, the digitally disadvantaged subset of the population would be able to develop the required skills to achieve benefits from Internet use. In Chapter 3, I learn that addressing the affordability barrier in a developing country is not enough to bridge the second-level digital divide. More robust policies are needed to bridge the second-level digital divide in developing countries. In Chapter 4, I learn that the digital disadvantage may simply be a reflection of
societal inequalities in the online space. Therefore, in order to bridge the digital divide, target policies that address these preexisting inequalities are recommended.
As autonomous driving vehicles are being tested on public roads, they will share the road with human-driven vehicles. It becomes important for autonomous driving vehicles to estimate human drivers’ intentions in order to interact properly with the human drivers to achieve safe and efficient experiences. The current work proposes a new cooperative driving framework which is capable of predicting other vehicles’ behaviors. The estimated prediction provides an input for a trajectory planner to perform cooperative behavior and to generate a path to react to other vehicles. The
system has three stages:
1 Abstract intention prediction;
2 Intermediate-level important points prediction;
3 Ultimate trajectory prediction.
The system bridges the gap between higher-level mission planning and behavioral execution or trajectory planning, especially in interactive scenarios. The validation
contains two aspects: Firstly, the estimated trajectory is compared with the groundtruth in datasets. Secondly, the estimated trajectory is applied to current trajectory
planners to generate cooperative plans. The second step evaluates the closed-loop performance of the behavioral estimation in the whole system. The proposed method
outperforms previous solutions in terms of collision rates, safety distance, and error when tested against a human-driven trajectory database. The method is implemented
in simulation and on a real autonomous driving platform to test its feasibility in real scenarios.
History indicates that products shape human society. For example, with theinvention of the wheel came the infrastructural development of roads, rails andother methods to commute, and the introduction of the telephone changed theways people communicate. Today’s devices such as mobile phones, wearables,etc., have brought about massive cultural change and dictate the ways humansinteract with each other, with spaces, forms, and interfaces, as well as constantlydefine the way humans perceive everyday products.
A lack of evolving product experience builds a shallow relationship between it andthe user, leading to a disposable attitude and behavior, which is problematic. Theconstant volatile behavior of owning and discarding is dangerous for theenvironment because it is unsustainable and negatively impacts the entire societyas a result causes a change of mindset towards human-relationships being moretransactional and less nostalgic (Rose, 2014). Although much work has been donein the field of emotional design, designing for love, empathy, and sustainabledesign, there is huge potential for designers to apply these theories to the designof products that change over time to satisfy users’ evolving needs. This studyexamines the role of design in motivating users to actively participate inreconfiguring products in use over time to satisfy evolving needs and drives. Thehypothesis is that such actions will build a long-term humanistic relationshipbetween users and everyday objects, which will positively impact people andthe planet.