Urban Public Transportation Planning with Endogenous Passenger Demand

An effective and efficient public transportation system is crucial to people\u27s mobility, economic production, and social activities. The Operations Research community has been studying transit system optimization for the past decades. With disruptions from the private sector, especially the parking operators, ride-sharing platforms, and micro-mobility services, new challenges and opportunities have emerged. This thesis contributes to investigating the interaction of the public transportation systems with significant private sector players considering endogenous passenger choice. To be more specific, this thesis aims to optimize public transportation systems considering the interaction with parking operators, competition and collaboration from ride-sharing platforms and micro-mobility platforms. Optimization models, algorithms and heuristic solution approaches are developed to design the transportation systems. Parking operator plays an important role in determining the passenger travel mode. The capacity and pricing decisions of parking and transit operators are investigated under a game-theoretic framework. A mixed-integer non-linear programming (MINLP) model is formulated to simulate the player\u27s strategy to maximize profits considering endogenous passenger mode choice. A three-step solution heuristic is developed to solve the large-scale MINLP problem. With emerging transportation modes like ride-sharing services and micro-mobility platforms, this thesis aims to co-optimize the integrated transportation system. To improve the mobility for residents in the transit desert regions, we co-optimize the public transit and ride-sharing services to provide a more environment-friendly and equitable system. Similarly, we design an integrated system of public transit and micro-mobility services to provide a more sustainable transportation system in the post-pandemic world

Dynamic scaling for the growth of non-equilibrium fluctuations during thermophoretic diffusion in microgravity

Diffusion processes are widespread in biological and chemical systems, where they play a fundamental role in the exchange of substances at the cellular level and in determining the rate of chemical reactions. Recently, the classical picture that portrays diffusion as random uncorrelated motion of molecules has been revised, when it was shown that giant non-equilibrium fluctuations develop during diffusion processes. Under microgravity conditions and at steady-state, non-equilibrium fluctuations exhibit scale invariance and their size is only limited by the boundaries of the system. In this work, we investigate the onset of non-equilibrium concentration fluctuations induced by thermophoretic diffusion in microgravity, a regime not accessible to analytical calculations but of great relevance for the understanding of several natural and technological processes. A combination of state of the art simulations and experiments allows us to attain a fully quantitative description of the development of fluctuations during transient diffusion in microgravity. Both experiments and simulations show that during the onset the fluctuations exhibit scale invariance at large wave vectors. In a broader range of wave vectors simulations predict a spinodal-like growth of fluctuations, where the amplitude and length-scale of the dominant mode are determined by the thickness of the diffuse layer.Comment: To appear in Scientific Report

Estimating Matching Affinity Matrix under Low-Rank Constraints

In this paper, we address the problem of estimating transport surplus (a.k.a. matching affinity) in high dimensional optimal transport problems. Classical optimal transport theory species the matching affinity and determines the optimal joint distribution. In contrast, we study the inverse problem of estimating matching affinity based on the observation of the joint distribution, using an entropic regularization of the problem. To accommodate high dimensionality of the data, we propose a novel method that incorporates a nuclear norm regularization which effectively enforces a rank constraint on the affinity matrix. The lowrank matrix estimated in this way reveals the main factors which are relevant for matching

THREE ESSAYS ON IMPLEMENTATION THEORY

Ph.DDOCTOR OF PHILOSOPH

Stimulus Control for Making Math Verbal

In three experiments, I first examined the correlation between the presence of transformation of stimulus function (TSF) across computation and the presence of TSF across saying and writing for spelling words, and then tested the effects of the establishment of TSF across saying and writing on the establishment of TSF across math operants. Eight middle school students with learning disabilities participated in experiments I and II. All participants demonstrated reader/writer and math skills such as textual responding and using counting strategies to solve one-step word problems. Four of the eight participants also demonstrated TSF across saying and writing for spelling. The dependent variables of Experiment I were the accuracy and fluency of solving word problems after receiving fluency training on math facts, as well as the number of counting strategies used when solving word problems. Results showed that all participants with TSF across saying and writing for spelling demonstrated significant increases in both their accuracy and fluency when responding to word problems (i.e., ES = 1) whereas participants who did not demonstrate TSF across saying and writing for spelling demonstrated minimal gain from accuracy and fluency training of math facts (i.e., mean ES = 0.3). Experiment II tested the effects of fluency and accuracy training of word problems on the accurate and fluent responding to math facts and other math operants. Results showed that accuracy and fluency training had large effects on all participants (i.e., ES = 1). Participants who did not demonstrate TSF also demonstrated larger improvement (i.e., ES > 0.67) compared to Experiment I. The results of Experiments I and II demonstrated an association between TSF across math operants and TSF across saying and writing for spelling. Experiment III further tested for a functional relation by examining the effects of the establishment of TSF across saying and writing for spelling on the establishment of TSF across math operants with three of the participants who did not demonstrate TSF across saying and writing for spelling in the first two experiments. Upon establishment of TSF across saying and writing for spelling words, all three participants demonstrated TSF across math operants (i.e., increased accuracy and fluency of word problems, extinction of counting strategies). The results of the three experiments suggest the importance of teaching math as a verbal behavior, more specifically, as a speaker-as-own-listener behavior instead of as visual match-to-sample repertoires. Future replication of the procedure is needed to extend the external validity of the current experiments

STATISTICAL METHODS FOR RECURRENT MARKER PROCESS IN THE PRESENCE OF TERMINAL EVENTS

Benefit-risk assessment is a crucial step in the medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefit-risk assessment. The effect of an intervention or a treatment on the longitudinal marker process, however, can be in conflict with its effect on the time to the terminal event. Thus questions arise on how to evaluate treatment effects based on the two endpoints, for the purpose of deciding on which treatment is most likely to benefit the patients. In this dissertation, we present a unified framework for benefit-risk assessment using the observed longitudinal markers and time to event data. We propose a cumulative weighted marker process to synthesize information from the two endpoints, and use its mean function at a pre-specified time point as a benefit-risk summary measure. We consider nonparametric estimation of the summary measure under two scenarios: (i) the longitudinal marker is measured intermittently during the study period, and (ii) the value of the longitudinal marker is observed throughout the entire follow-up period. The large-sample properties of the estimators are derived and compared. Simulation studies and the application to an AIDS clinical trial exhibit that the proposed methods are easy to implement and reliable for practical use. In many follow-up or surveillance studies, marker data are collected conditioning on the occurrence of recurrent events. In contrast with the above situation that the marker measurements exists at any time before the terminal event, sometimes marker measurements are triggered by the occurrence of recurrent events. Examples include the medical cost for inpatient or outpatient cares, length-of-stay for hospitalizations, and prognostic or quality-of-life measurement repeatedly measured at multiple infections related to a certain disease. A recurrent marker process, defined between a pre-specified time origin and a terminal event, is composed of recurrent events and repeatedly measured marker measurements. We consider nonparametric estimation of the mean recurrent marker process in the situation when the occurrence of terminal event is subject to competing risks. Statistical methods and inference are developed to address a variety of questions and applications, for the purposes of estimating and comparing the integrated risk in relation to recurrent events, marker measurements and time to the terminal event for different competing risk groups. A SEER-Medicare linked database is used to illustrate the proposed approaches