Measuring the Quality of Arterial Traffic Signal Timing – A Trajectory-based Methodology

Evaluating the benefits from traffic signal timing is of increasing interest to transportation policymakers, operators, and the public as integrating performance measurements with agencies’ daily signal timing management has become a top priority. This dissertation presents a trajectory-based methodology for evaluating the quality of arterial signal timing, a critical part of signal operations that promises reduced travel time and fewer vehicle stops along arterials as well as improved travelers’ perception of transportation services. The proposed methodology could significantly contribute to performance-oriented signal timing practices by addressing challenges regarding which performance measures should be selected, how performance measurements can be performed cost-effectively, and how to make performance measures accessible to people with limited knowledge of traffic engineering. A review of the current state of practice and research was conducted first, indicating an urgent research need for developing an arterial-level methodology for signal timing performance assessments as the established techniques are mostly based on by-link or by-movement metrics. The literature review also revealed deficiencies of existing performance measures pertaining to traffic signal timing. Accordingly, travel-run speed and stop characteristics, which can be extracted from vehicle GPS trajectories, were selected to measure the quality of arterial signal timing in this research.Two performance measures were then defined based on speed and stop characteristics: the attainability of ideal progression (AIP) and the attainability of user satisfaction (AUS). In order to determine AIP and AUS, a series of investigations and surveys were conducted to characterize the effects of non-signal-timing-related factors (e.g., arterial congestion level) on average travel speed as well as how stops may affect travelers’ perceived quality of signal timing. AIP was calculated considering the effects of non-signal-timing-related factors, and AUS accounted for the changes in the perceived quality of signal timing due to various stop circumstances.Based upon AIP and AUS, a grade-based performance measurement methodology was developed. The methodology included AIP scoring, AUS scoring, and two scoring adjustments. The two types of scoring adjustments further improved the performance measurement results considering factors such as cross-street delay, pedestrian delays, and arterial geometry. Furthermore, the research outlined the process for implementing the proposed methodology, including the necessary data collection and the preliminary examination of the applicable conditions. Case studies based on real-world signal re-timing projects were presented to demonstrate the effectiveness of the proposed methodology in enhancing agencies’ capabilities of cost-effectively monitoring the quality of arterial signal timing, actively addressing signal timing issues, and reporting the progress and outcomes in a concise and intuitive manner

Meta-Analytic Estimation Techniques for Non-Convergent Repeated-Measure Clustered Data

Clustered data often feature nested structures and repeated measures. If coupled with binary outcomes and large samples (\u3e10,000), this complexity can lead to non-convergence problems for the desired model especially if random effects are used to account for the clustering. One way to bypass the convergence problem is to split the dataset into small enough sub-samples for which the desired model convergences, and then recombine results from those sub-samples through meta-analysis. We consider two ways to generate sub-samples: the K independent samples approach where the data are split into k mutually-exclusive sub-samples, and the cluster-based approach where naturally existing clusters serve as sub-samples. Estimates or test statistics from either of these sub-sampling approaches can then be recombined using a univariate or multivariate meta-analytic approach. We also provide an innovative approach for simulating clustered and dependent binary data by simulating parameter templates that yield the desired cluster behavior. This approach is used to conduct simulation studies comparing the performance of the K independent samples and cluster-based approaches to generating sub-samples, the results from which are combined either with univariate and multivariate meta-analytic techniques. These studies show that using natural clusters leaded to lower biased test statistics when the number of clusters and treatment effect were large, as compared to the K independent samples approach for both the univariate and multivariate meta-analytic approaches. And the independent samples approach was preferred when the number of clusters and treatment effect were small. We also apply these methods to data on cancer screening behaviors obtained from electronic health records of n=15,652 individuals and showed that these estimated results support the conclusions from the simulation studies

Catchment Area Analysis Using Bayesian Regression Modeling

A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research, we constructed both diagnosis and diagnosis/treatment CAs for the Massey Cancer Center (MCC) at Virginia Commonwealth University. We constructed diagnosis CAs for all cancers based on Virginia state cancer registry data and Bayesian hierarchical logistic regression models. We constructed a diagnosis/treatment CA using billing data from MCC and a Bayesian hierarchical Poisson regression model. To define CAs, we used exceedance probabilities for county random effects to assess unusual spatial clustering of patients diagnosed or treated at MCC after adjusting for important demographic covariates. We used the MCC CAs to compare patient characteristics inside and outside the CAs. Among cancer patients living within the MCC CA, patients diagnosed at MCC were more likely to be minority, female, uninsured, or on Medicaid

ADDRESSING INFORMALITY IN PROCESSING CHINESE MICROTEXT

Ph.DDOCTOR OF PHILOSOPH

Bias Mitigation Framework for Intersectional Subgroups in Neural Networks

We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation

Enhancement of the superconductivity and quantum metallic state in the thin film of superconducting Kagome metal KV3_3Sb5_5

Recently V-based Kagome metal attracted intense attention due to the emergence of superconductivity in the low temperature. Here we report the fabrication and physical investigations of the high quality single-crystalline thin films of the Kagome metal KV$_3$Sb$_5$. For the sample with the thickness of about 15 nm, the temperature dependent resistance reveals a Berezinskii-Kosterlitz-Thouless (BKT) type behavior, indicating the presence of two-dimensional superconductivity. Compared with the bulk sample, the onset transition temperature $T^{onset}_{c}$ and the out-of-plane upper critical field $H_{c2}$ are enhanced by 15\% and more than 10 times respectively. Moreover, the zero-resistance state is destroyed by a magnetic field as low as 50 Oe. Meanwhile, the temperature-independent resistance is observed in a wide field region, which is the hallmark of quantum metallic state. Our results provide evidences for the existence of unconventional superconductivity in this material.Comment: 5 pages, 4 figure

Identification of preoperative radiological risk factors for reoperation following percutaneous endoscopic lumbar decompression to treat degenerative lumbar spinal stenosis

BackgroundThis study aimed to identify radiological risk factors associated with reoperation after percutaneous transforaminal endoscopic decompression (PTED) for degenerative lumbar spinal stenosis (DLSS).MethodsThe preoperative clinical data of 527 consecutive patients with DLSS who underwent PTED were retrospectively reviewed. Overall, 44 patients who underwent reoperation were matched for age, sex, body mass index, and surgical segment to 132 control patients with excellent or good clinical outcomes. Radiological characteristics were compared between the groups using independent sample t-tests and Pearson's chi-square tests. A predictive model was established based on multivariate logistic regression analysis.ResultsThe analyses revealed significant differences in the presence of lumbosacral transitional vertebra (LSTV, 43.2% vs. 17.4%, p = 0.001), the number of levels with senior-grade disc degeneration (2.57 vs. 1.96, p = 0.018) and facet degeneration (1.91 vs. 1.25 p = 0.002), and the skeletal muscle index (SMI, 849.7 mm2/m2 vs. 1008.7 mm2/m2, p < 0.001) between patients in the reoperation and control groups. The results of the logistic analysis demonstrated that LSTV (odds ratio [OR] = 2.734, 95% confidence interval [CI]:1.222–6.117, p < 0.014), number of levels with senior-grade facet degeneration (OR = 1.622, 95% CI:1.137–2.315, p = 0.008), and SMI (OR = 0.997, 95% CI:0.995–0.999, p = 0.001) were associated with reoperation after PTED. The application of the nomogram based on these three factors showed good discrimination (area under the receiver operating characteristic curve 0.754, 95% CI 0.670–0.837) and good calibration.ConclusionLSTV, more levels with senior-grade facet degeneration, and severe paraspinal muscle atrophy are independent risk factors for reoperation after PTED. These factors can thus be used to predict reoperation risk and to help tailor treatment plans for patients with DLSS

Synthesis and Characterization of CZTS Thin Films by Sol-Gel Method without Sulfurization

One process of layer-by-layer sol-gel deposition without sulfurization was developed. The CZTS films with 1.2 μm and the sulfur ratio of ~48% were prepared and their characteristics were measured. The as-deposited and annealed films are of Kesterite structure. The as-deposited films do not present obvious electric conduction type. However, the annealed 9-LAY-ANN film is p-type conduction and has sheet resistance of 4.08 kΩ/□ and resistivity of 4.896 × 10−1 Ω·cm. The optic energy gap is 1.50 eV for as-deposited films and is 1.46 eV after being annealed. The region deposited by using Lo-Con solution is more compact than that by the Hi-Con solution from SEM morphology images