Essays in Transportation Economics and Regional Science

This dissertation comprises three chapters that attempt to examine the effects of new infrastructure of transportation systems. This first chapter The Impact of High Speed Rail on Traffic Congestion examines the impact of high-speed rail systems on highway congestion, using the case of South Korea. I use different strategies to address the endogeneity problem of evaluating the impact of transport infrastructure on traffic congestion. The results indicate that building high-speed rail lowers demand for intercity driving by 7.5\% to 30.9\%. However, the detailed data suggests that the reduction in congestion comes primarily from night time, during which high-speed trains do not run, and building high-speed rail does not have significant impacts on traffic demand during rush hour and daytime. These findings suggest that congestion at peak time may not be relieved. This study contributes to the literature on high-speed rail and congestion and is the first to estimate the effects of building high-speed rail systems on intercity traffic congestion. The second chapter Welfare Analysis on Travel Time Saving: from Intercity Driving to High Speed Train attempts to answer whether building high-speed rail can recoup its tremendous costs of construction. The debate is rekindled in the United States because of the construction of the high-speed rail system linking San Francisco and Los Angeles. Transportation researchers haven\u27t yet arrived at a conclusive cost-benefit analysis of the transport infrastructure. This stems from the difficulty of estimating first-order benefits such as travel time saving due to lack of data. To address this issue, this study employs novel intercity driving data and the construction of high-speed rail in South Korea as quasi-natural experiment to estimate the first-order benefits. Specifically, I exploits the difference in travel time between driving and high-speed trains for 146 intercity routes. Employing nested logit demand models, I find that the value of travel time is approximately 14 cents per minute. Also, the internal rate of return of adding a new rail line has a positive sign, even when revenue of the rail companies and rent increase for the land are not taken into account. These findings suggest that high-speed rail can be a viable option for improving transportation infrastructure. The third chapter Displacement and Substitution from Physical Barriers for Suicide Attempts investigates substitution effects of installing physical barriers for suicide prevention. Physical barriers are considered as an effective way to reduce suicides at a given metro station. However, it remains unexplored whether such installation could eradicate the incentives to end life for those who are vulnerable to suicidal impulses. Using novel data on Metro in Seoul and suicide attempts at the municipality level, I find pioneering evidence showing that the physical barriers installation at metro stations can cause displacement to other stations without barriers or substitution into seeking another method of suicide. The installation substantially lowers the number of suicide attempts at the metro station but I reject the null hypothesis that all of the reduced suicides are prevented at the municipality level. Furthermore, taking types of physical barriers into account, I find an interesting pattern of substitution of each type based on economic theory. Search costs for alternative ways of suicide explains the substitution pattern of each type of physical barriers

HCLAS-X: Hierarchical and Cascaded Lyrics Alignment System Using Multimodal Cross-Correlation

In this work, we address the challenge of lyrics alignment, which involves aligning the lyrics and vocal components of songs. This problem requires the alignment of two distinct modalities, namely text and audio. To overcome this challenge, we propose a model that is trained in a supervised manner, utilizing the cross-correlation matrix of latent representations between vocals and lyrics. Our system is designed in a hierarchical and cascaded manner. It predicts synced time first on a sentence-level and subsequently on a word-level. This design enables the system to process long sequences, as the cross-correlation uses quadratic memory with respect to sequence length. In our experiments, we demonstrate that our proposed system achieves a significant improvement in mean average error, showcasing its robustness in comparison to the previous state-of-the-art model. Additionally, we conduct a qualitative analysis of the system after successfully deploying it in several music streaming services

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, is an indispensable task for a wide range of applications. Previous studies using large-scale trajectory datasets in a single server have achieved remarkable performance in UNLP task. However, in real-world applications, legal and ethical issues have been raised regarding privacy concerns leading to restrictions against sharing human trajectory datasets to any other server. In response, Federated Learning (FL) has emerged to address the personal privacy issue by collaboratively training multiple clients (i.e., users) and then aggregating them. While previous studies employed FL for UNLP, they are still unable to achieve reliable performance because of the heterogeneity of clients' mobility. To tackle this problem, we propose the Federated Learning for Geographic Information (FedGeo), a FL framework specialized for UNLP, which alleviates the heterogeneity of clients' mobility and guarantees personal privacy protection. Firstly, we incorporate prior global geographic adjacency information to the local client model, since the spatial correlation between locations is trained partially in each client who has only a heterogeneous subset of the overall trajectories in FL. We also introduce a novel aggregation method that minimizes the gap between client models to solve the problem of client drift caused by differences between client models when learning with their heterogeneous data. Lastly, we probabilistically exclude clients with extremely heterogeneous data from the FL process by focusing on clients who visit relatively diverse locations. We show that FedGeo is superior to other FL methods for model performance in UNLP task. We also validated our model in a real-world application using our own customers' mobile phones and the FL agent system.Comment: Accepted at 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023

Probing the Ion Transport Properties of Ultrashort Carbon Nanotubes Integrated with Supported Lipid Bilayers via Electrochemical Analysis.

Supported lipid bilayers (SLBs) are commonly used to investigate interactions between cell membranes and their environment. These model platforms can be formed on electrode surfaces and analyzed using electrochemical methods for bioapplications. Carbon nanotube porins (CNTPs) integrated with SLBs have emerged as promising artificial ion channel platforms. In this study, we present the integration and ion transport characterization of CNTPs in in vivo environments. We combine experimental and simulation data obtained from electrochemical analysis to analyze the membrane resistance of the equivalent circuits. Our results show that carrying CNTPs on a gold electrode results in high conductance for monovalent cations (K+ and Na+) and low conductance for divalent cations (Ca2+)

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.Comment: Accepted at 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023

Basic study on the oil recovery in a hybrid heat pump using ammonia/water solution

Paper presented at the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16-18 July, 2012.In an ammonia/water hybrid heat pump system which is a vapor compression cycle with a solution circuit, lubricating oil is commonly used for the compressor. Since Poly-Alpha-Olefin (PAO) oil which is commonly used for ammonia is immiscible with the ammonia/water solution, a proper oil recovery method is required for a smooth operation of the compressor. Although the oil separator installed at the outlet of the compressor removes most of the oil from the refrigerant vapor, some oil droplets are carried over and accumulated in the solution reservoir. Unlike the pure ammonia vapor compression system, the density of PAO oil is smaller than that of ammonia/water mixture which has the ammonia concentration of 30-40%, and the oil tends to rise and gather near the liquid/vapor interface. In this study, a method for oil recovery from the solution reservoir is suggested. In the present method, the mixture of the oil and the solution is drained into an oil separator having a narrow cylinder at the top, if the oil in the reservoir is greater than a certain amount. The oil droplets in the solution rise by buoyancy and gather at this upper narrow cylinder. The gathered oil is extracted and returned to the compressor by an oil recovery pump. Since the solution has to be returned to the reservoir as soon as the separation process is finished, the process time for the separation should be as short as possible. To predict the time for the separation, experiments and simulations have been carried out. The model using the multiphase segregated flow (MSF) showed that a proper choice of droplet diameter is necessary to predict a correct separation time. Also, a simulation model which is able to consider the effect of surface tension and droplet merging is needed to be developed.dc201

Association between Dietary Intake of Flavonoids and Cancer Recurrence among Breast Cancer Survivors

Intake of flavonoids is associated with the incidence of breast cancer, but the association between the intake of flavonoids and cancer recurrence is unclear. This study aimed to investigate the hypothesis that intake of flavonoids and flavonoid-rich foods is negatively associated with cancer recurrence. Among 572 women who underwent breast cancer surgery, 66 patients had a cancer recurrence. Dietary data were collected using a structured 24-h dietary recall, and intake of flavonoids was calculated based on the Korea Rural Development Administration flavonoid database. Among overweight and obese patients, disease-free survival was associated with intake of flavonoids (p = 0.004) and flavonoid-rich foods (p = 0.003). Intake of flavonoids (hazard ratio (HR) = 0.249, 95% confidence interval (CI): 0.09–0.64) and flavonoid-rich foods (HR = 0.244, 95% CI: 0.09–0.66) was negatively associated with cancer recurrence after adjusting for confounding factors in overweight and obese patients. Consumption of flavonoids and flavonoid-rich foods was lower in overweight and obese patients with cancer recurrence than those without recurrence and in normal-weight patients. This study suggests that intake of flavonoids and flavonoid-rich foods could have beneficial effects on cancer recurrence in overweight and obese breast cancer survivors