Enhanced Transit Ridership Forecasting Using Automatic Passenger Counting Data

Recent emphasis on sustainable development has carried over into the transportation sector, given the impacts of transportation behavior on environment and equity. Transit is widely recognized as a viable option supporting the sustainability issue providing benefits such as reducing air pollution, alleviating traffic congestion, enhancing mobility, and promoting social well-being (health through walk- and bike-access). An important tool in advancing sustainable transport is to generate more robust transit ridership models to evaluate the benefits of investments in these modes. In particular, this thesis concentrates on two sub-problems of (1) calibration procedures and (2) insufficient data for transit mode choice modules. The first purpose of this thesis is to improve the calibration procedures through better understanding of calibrated mode constants. First, the magnitude and relative importance of mode constants to measurable components are analyzed using representative data from six cities in North America. The mode constants (representing unmeasured inputs) in study cities account for 41% to 65% of total utilities. The results demonstrate that, in some cases, mode constants are large enough to render models insensitive to changes of important but omitted system factors such as reliability, comfort, convenience, visibility, access environment, and safety. The need to explicitly include mode constant endogenous to the model is verified. Second, this thesis introduces a framework to improve the utilization of new data sources such as automated vehicle location (AVL) and automated passenger counting (APC) systems in transit ridership forecasting models. The direct application of the AVL/APC data to travel forecasting requires an important intermediary step that links stops activities - boarding and alighting - to the actual location (at the TAZ level) that generated/attracted this trip. The GIS-based transit trip allocation methods are newly developed with focus on considering the case when the access shed spans multiple TAZs. The proposed methods improve practical applicability with easily obtained data in local contexts. The performance of the proposed allocation methods is further evaluated using transit on-board survey data. The results show that the buffer area ratio weighted by employment or population and footprint weighted method perform reasonably well in the study area and can effectively handle various conditions, particularly for major activity generators. The average errors between observed data and the proposed method are about 8% for alighting trips and 18% for boarding trips. Third, given the outputs from the previous research effort, the application framework of the AVL/APC data to travel forecasting model calibration is demonstrated. In the proposed framework, transit trip allocation methods are employed to identify prediction errors at finer geographic level (at TAZs). In turn, the approach makes it possible to evaluate the zonal characteristics that affect estimation accuracy. Developed multinomial regression models produce equations for the mode choice prediction errors as a function of (1) measurable but omitted market segmentation variables in current mode choice utility function including socio-economic and land use data; and (2) newly quantifiable attributes with new data source or techniques including quality of service variables. The proposed composite index can systematically evaluate and prioritize the major source of prediction errors by quantifying total magnitudes of prediction error and a possible error component. The outcomes of the research in this thesis can serve as foundation towards more reliable and accurate mode choice models and ultimately enhanced transit travel forecasting

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

High-resolution embedding extractor for speaker diarisation

Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each speech segment. However, because of the sliding window approach, a segment easily includes two or more speakers owing to speaker change points. This study proposes a novel embedding extractor architecture, referred to as a high-resolution embedding extractor (HEE), which extracts multiple high-resolution embeddings from each speech segment. Hee consists of a feature-map extractor and an enhancer, where the enhancer with the self-attention mechanism is the key to success. The enhancer of HEE replaces the aggregation process; instead of a global pooling layer, the enhancer combines relative information to each frame via attention leveraging the global context. Extracted dense frame-level embeddings can each represent a speaker. Thus, multiple speakers can be represented by different frame-level features in each segment. We also propose an artificially generating mixture data training framework to train the proposed HEE. Through experiments on five evaluation sets, including four public datasets, the proposed HEE demonstrates at least 10% improvement on each evaluation set, except for one dataset, which we analyse that rapid speaker changes less exist.Comment: 5pages, 2 figure, 3 tables, submitted to ICASS

Absolute decision corrupts absolutely: conservative online speaker diarisation

Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic results. We hypothesise that cautiously increasing the number of estimated speakers is of paramount importance among many other factors. Thus, our proposed framework includes decreasing the number of speakers by one when the system judges that an increase in the past was faulty. We also adopt dual buffers, checkpoints and centroids, where checkpoints are combined with silhouette coefficients to estimate the number of speakers and centroids represent speakers. Again, we believe that more than one centroid can be generated from one speaker. Thus we design a clustering-based label matching technique to assign labels in real-time. The resulting system is lightweight yet surprisingly effective. The system demonstrates state-of-the-art performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and VoxConverse test sets.Comment: 5pages, 2 figure, 4 tables, submitted to ICASS

Effects of aiming lines and visual function on the golf putting alignment

Background: In golf, a player hits a ball with a club, aiming to transfer the ball successively into a series of hole cups in a course consisting of 18 (or fewer) holes. This study aimed to compare the impact of visual function and the presence and number of aiming lines on golf putting alignment between beginner and expert golfers. Methods: In this prospective comparative study, 43 participants with a mean ± standard deviation (SD) of corrected distance binocular visual acuity of –0.07 ± 0.74 logarithm of the minimum angle of resolution, who knew their average golf scores, were divided into beginner and expert golfers. Six visual function tests were conducted to assess heterotropia, dominant eye, verification of current spectacles, static visual acuity, stereopsis, and fixation disparity. At the putting distances of 1.5 m and 3 m, alignment errors were measured five times each, using golf balls with 1 and 3 aiming line(s) and putters with 1 and 3 aiming line(s). Results: The mean ± SD of age was 48.33 ± 10.07 years for study participants overall. The accuracy of ball alignment was not affected by the career or number of aiming lines, but the putter alignment was higher for the 3-lines putter than for the 1-line putter (P < 0.05). When the number and shape of the aiming line were the same for both the ball and putter, the aiming accuracy was found to be higher. In both stereopsis and fixation disparity, the combination of putting distance and a 3-lines ball showed negative values; all other combinations showed positive values, but no statistically significant correlation was detected (all P > 0.05). Conclusions: The accuracy of golf ball alignment did not depend on the number of aiming lines and the golfer’s career. However, the predicted putting success rate and subjective satisfaction were increased when three-line golf balls and putters were used, as compared to when one-line golf balls and putters were used. How to cite this article: Kim YJ, Jin YG, Koo BY, Jang JU, Mah KC. Effects of aiming lines and visual function on the golf putting alignment. Med Hypothesis Discov Innov Optom.2021 Spring; 2(1): 41-49. DOI: https://doi.org/10.51329/mehdioptometry12

Continuum understanding of twin formation near grain boundaries of FCC metals with low stacking fault energy

Deformation twinning from grain boundaries is often observed in face-centered cubic metals with low stacking fault energy. One of the possible factors that contribute to twinning origination from grain boundaries is the intergranular interactions during deformation. Nonetheless, the influence of mechanical interaction among grains on twin evolution has not been fully understood. In spite of extensive experimental and modeling efforts on correlating microstructural features with their twinning behavior, a clear relation among the large aggregate of grains is still lacking. In this work, we characterize the micromechanics of grain-to-grain interactions that contribute to twin evolution by investigating the mechanical twins near grain boundaries using a full-field crystal plasticity simulation of a twinning-induced plasticity steel deformed in uniaxial tension at room temperature. Microstructures are first observed through electron backscatter diffraction technique to obtain data to reconstruct a statistically equivalent microstructure through synthetic microstructure building. Grain-to-grain micromechanical response is analyzed to assess the collective twinning behavior of the microstructural volume element under tensile deformation. Examination of the simulated results reveal that grain interactions are capable of changing the local mechanical behavior near grain boundaries by transferring strain across grain boundary or localizing strain near grain boundary.116Ysciescopu

Disentangled dimensionality reduction for noise-robust speaker diarisation

The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation systems, but they often capture spurious information such as noise and reverberation, adversely affecting performance. Our previous work has proposed an auto-encoder-based dimensionality reduction module to help remove the redundant information. However, they do not explicitly separate such information and have also been found to be sensitive to hyper-parameter values. To this end, we propose two contributions to overcome these issues: (i) a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings; (ii) the use of a speech/non-speech indicator to prevent the speaker code from representing the background noise. Through a range of experiments conducted on four different datasets, our approach consistently demonstrates the state-of-the-art performance among models without system fusion.Comment: This paper was submitted to Interspeech202