Real-Time Estimation of Origin–Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data

2005 ◽  
Vol 1923 (1) ◽  
pp. 119-126 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Pravin Varaiya
Keyword(s):  
Statistical Model ◽  
Real Time ◽  
San Francisco ◽  
Time Estimation ◽  
Sample Survey ◽  
Accurate Estimation ◽  
Electronic Toll Collection ◽  
Matrix Estimation ◽  
Bay Area ◽  
Toll Collection

The origin–destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The relationship of the proposed estimator with similar methods in the literature was also studied and extension of the methods to general, more complex networks is discussed.

Evaluating Interior and Door Configurations of Rail Vehicles by Using Variable Loading Densities

2005 ◽  
Vol 1927 (1) ◽  
pp. 268-276 ◽  
Author(s):  
Samuel W. Lau
Keyword(s):  
San Francisco ◽  
Dwell Time ◽  
Time Estimation ◽  
Rapid Transit ◽  
Variable Loading ◽  
Uniform Loading ◽  
Transit System ◽  
Bay Area ◽  
Transit Agencies ◽  
Rail Rapid Transit

As many U.S. metropolitan areas expect unprecedented growth in population and travel in the next 20 to 30 years, rail transit agencies are faced with the challenges of replacing their aging fleets and procuring new vehicles to keep up with ridership increases. As funds become increasingly scarce, many operators are exploring ways of increasing car capacity by considering interior configurations (to maximize loading efficiency) and door configurations (to minimize the effect of increased loads on station dwell times). Few studies address the design and evaluation of interior and door configurations as a system. Typically, seating configurations are designed separately from door configurations. Furthermore, interior configuration evaluations or maximum vehicle loading quoted by car manufacturers assume a uniform loading density applied throughout the car. Loading on transit vehicles, however, varies greatly within a car. This affects practical vehicle capacity and its impact controlling dwell time at the busiest door. The San Francisco Bay Area Rapid Transit District, a heavy rail rapid transit system in California, recently conducted an evaluation of interior and door configurations based on a methodology that used variable loading densities and resulting impact on door loads for dwell time estimation. Variable loading density is more realistic in simulating actual passenger loading experience. This research shows that depending on the interior and door configuration, applying uniform loading density may misrepresent actual car capacity and door loads and thus waste valuable resources or underestimate actual needs.

scholarly journals Real-Time Sensing of Output Polymer Flow Temperature and Volumetric Flowrate in Fused Filament Fabrication Process

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 618
Author(s):  
Rakshith Badarinath ◽  
Vittaldas Prabhu
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Deposition Process ◽  
Fabrication Process ◽  
Accurate Estimation ◽  
Main Process ◽  
Monitoring And Control ◽  
Fused Filament Fabrication ◽  
Geometry Model ◽  
Width Measurements

In this paper we addressed key challenges in engineering an instrumentation system for sensing and signal processing for real-time estimation of two main process variables in the Fused-Filament-Fabrication process: (i) temperature of the polymer melt exiting the nozzle using a thermocouple; and (ii) polymer flowrate using extrusion width measurements in real-time, in-situ, using a microscope camera. We used a design of experiments approach to develop response surface models for two materials that enable accurate estimation of the polymer exit temperature as a function of polymer flowrate and liquefier temperature with a fit of 𝑅2=99.96% and 99.39%. The live video stream of the deposition process was used to compute the flowrate based on a road geometry model. Specifically, a robust extrusion width recognizer algorithm was developed to identify edges of the deposited road and for real-time computation of extrusion width, which was found to be robust to filament colors and materials. The extrusion width measurement was found to be within 0.08 mm of caliper measurements with an 𝑅2 value of 99.91% and was found to closely track the requested flowrate from the slicer. This opens new avenues for advancing the engineering science for process monitoring and control of FFF.

Robust and Timely Estimation of Excess Mortality: Statistical Analysis of Cremation Data During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Gemma Postill ◽  
Regan Murray ◽  
Andrew S Wilton ◽  
Richard A Wells ◽  
Renee Sirbu ◽  
...  
Keyword(s):  
Real Time ◽  
Excess Mortality ◽  
Age Groups ◽  
Time Estimation ◽  
Vital Statistics ◽  
Accurate Estimation ◽  
Mortality Data ◽  
Mortality Estimation ◽  
Statistics Canada ◽  
The Impact

BACKGROUND Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating timely mortality data that encompasses the majority of deaths within the province. OBJECTIVE Our objectives were to (1) validate the ability of cremation data in permitting real-time estimation of excess all-cause mortality, interim of vital statistics data, and (2) describe the patterns of excess mortality. METHODS Cremation records from January 2020 until April 2021 were compared to the historical records from 2017-2019, grouped according to week, age, sex, and COVID-19 status. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years to determine whether there was excess mortality and if so, which age groups had the greatest number of excess deaths during the COVID Pandemic, and whether deaths attributed to COVID-19 account for the entirety of the excess mortality. RESULTS Between 2017-2019, cremations were performed for 67.4% (95% CI: 67.3–67.5%) of deaths; the proportion of cremated deaths remained stable throughout 2020, establishing that the COVID-19 pandemic did not significantly alter cremation practices, even within age and sex categories. During the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI: 14.6–19.3%) in mortality. The accuracy of this excess mortality estimation was later confirmed by vital statistics data. CONCLUSIONS The stability in the percent of Ontarians cremated and the completion of cremation data several months before vital statistics data, enables accurate estimation of all-causes mortality in near real-time with cremation data. These findings demonstrate the utility of cremation data to provide timely mortality information during public health emergencies.

scholarly journals Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5606
Author(s):  
Yung-Hui Li ◽  
Latifa Nabila Harfiya ◽  
Kartika Purwandari ◽  
Yue-Der Lin
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Blood Pressure Monitoring ◽  
Time Estimation ◽  
Learning Model ◽  
Accurate Estimation ◽  
Arterial Blood ◽  
Deep Learning Model

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection

2017 ◽  
Vol 22 (17) ◽  
pp. 5707-5718 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Chuan-Jun Su ◽  
Han-Tang Nien ◽  
Pei-Fang Tsai ◽  
Chen-Yang Cheng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Travel Time ◽  
Real Time ◽  
Time Data ◽  
Electronic Toll Collection ◽  
Real Time Data ◽  
Toll Collection

A Qualitative Study of Ecstasy Sellers in the San Francisco Bay Area

2004 ◽  
Author(s):  
Sheigla Murphy ◽  
Paloma Sales ◽  
Micheline Duterte ◽  
Camille Jacinto
Keyword(s):  
Qualitative Study ◽  
San Francisco ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Bay Area

The “Japanese Turn” in Fine Dining in the United States, 1980–2020

2020 ◽  
Vol 20 (2) ◽  
pp. 45-54
Author(s):  
Samuel H. Yamashita
Keyword(s):  
United States ◽  
New York ◽  
Los Angeles ◽  
San Francisco ◽  
San Francisco Bay Area ◽  
The United States ◽  
First Century ◽  
Further Training ◽  
Bay Area ◽  
Wide Acceptance

In the 1970s, Japanese cooks began to appear in the kitchens of nouvelle cuisine chefs in France for further training, with scores more arriving in the next decades. Paul Bocuse, Alain Chapel, Joël Robuchon, and other leading French chefs started visiting Japan to teach, cook, and sample Japanese cuisine, and ten of them eventually opened restaurants there. In the 1980s and 1990s, these chefs' frequent visits to Japan and the steady flow of Japanese stagiaires to French restaurants in Europe and the United States encouraged a series of changes that I am calling the “Japanese turn,” which found chefs at fine-dining establishments in Los Angeles, New York City, and later the San Francisco Bay Area using an ever-widening array of Japanese ingredients, employing Japanese culinary techniques, and adding Japanese dishes to their menus. By the second decade of the twenty-first century, the wide acceptance of not only Japanese ingredients and techniques but also concepts like umami (savory tastiness) and shun (seasonality) suggest that Japanese cuisine is now well known to many American chefs.

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