Estimation of Pre-COVID19 Daily Ridership Patterns From Paper and Electronic Ticket Sales Data With Origin-Destination, Time-Of-Day, and Train-Start Detail on a Commuter Railroad: Quick-Response Big Data Analytics in a World Steeped With Tradition

Abstract Our niche method independently estimates hourly commuter rail station-to-station origin-destination (OD) matrix data each day from ticket sales and activation data from four sales channels (paper/mobile tickets, mail order, and onboard sales) by extending well-established transportation modelling methodologies. This algorithm’s features include: (1) handles multi-pack pay-per-ride fare instruments not requiring electronic validation, like ten-trip paper tickets “punched” onboard by railroad conductors; (2) correctly infers directionality for direction-agnostic ticket-types; (3) estimates unlimited ride ticket utilization patterns sufficiently precisely to inform vehicle assignment/scheduling; (4) provides integer outputs without allowing rounding to affect control totals nor introduce artifacts; (5) deals gracefully with cliff-edge changes in demand, like the COVID19 related lockdown; and (6) allocates hourly traffic to each train-start based on passenger choice. Our core idea is that the time of ticket usage is ultimately a function of the time of sale and ticket type, and mutual transformation is made via probability density functions (“patterns”) given sufficient distribution data. We generated pre-COVID daily OD matrices and will eventually extend this work to post-COVID inputs. Results were provided to operations planners using visual and tabular interfaces. These matrices represent data never previously available by any method; prior OD surveys required 100,000 respondents, and even then could neither provide daily nor hourly levels of detail, and could not monitor special event ridership nor specific seasonal travel such as summer Friday afternoons.

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Smart Blockchain

Smart Systems Design, Applications, and Challenges - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2112-0.ch003 ◽

2020 ◽

pp. 43-62

Author(s):

Gutha Jaya Krishna ◽

Vadlamani Ravi ◽

Bheemidi Vikram Reddy ◽

Mohammad Zaheeruddin

Keyword(s):

Visual Analytics ◽

New Technology ◽

Big Data Analytics ◽

Left Behind ◽

The Core ◽

Diverse Field ◽

Comprehensive Survey ◽

Overall Performance ◽

Core Idea ◽

Analytical Approaches

A blockchain is a digitized, decentralized, open system of records. Of late, there is a phenomenal spurt in the research and application activities of the catch-all phrase analytics, which subsumes machine learning, text mining, classical optimization, artificial intelligence, evolutionary computing, visual analytics, big data analytics, etc. in many a diverse field. Consequently, even new technology like blockchain is not left behind. This chapter presents a comprehensive survey of 33 papers that appeared between 2016 and 2018 under the theme, ‘Analytics and Blockchain', which focuses on how analytical approaches and blockchain implementation are symbiotically related to improve their overall performance in solving various real-world problems. The core idea behind the survey is to facilitate the reader to appreciate the utility of analytical methods to the design, implementation, and application of blockchain and suggesting future directions for further research.

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Factors and outcomes associated with paediatric emergency department arrival patterns through the day

Paediatrics & Child Health ◽

10.1093/pch/pxy173 ◽

2018 ◽

Vol 24 (5) ◽

pp. 323-329

Author(s):

Hathami Almubarak ◽

Garth Meckler ◽

Quynh Doan

Keyword(s):

Emergency Department ◽

Resource Allocation ◽

Cross Sectional Study ◽

Time Of Day ◽

Minor Trauma ◽

Patient Acuity ◽

Cross Sectional ◽

Utilization Patterns ◽

Multivariable Regression ◽

Ed Visits

Abstract Introduction Steadily increasing emergency department (ED) utilization has prompted efforts to increase resource allocation to meet demand. Little is known about the distribution and characteristics of patient arrivals by time of day. This study describes the variability and patterns of ED resource utilization related to patient, acuity, clinical, and disposition characteristics over a 24-hour period. Methods Retrospective cross-sectional study of all visits to a tertiary children’s hospital over a 1-year period. We use descriptive statistics to present ED visit details stratified by shift of arrival, and multivariable regression to explore the association between shift of presentation and hospital admission at index and 7-day return ED visits. Results Of 46,942 visits during the study period, 12% arrived overnight, 42% during the day, and 45% during the evening with variability in pattern of shift arrival by day of week. Overnight arrivals had a higher acuity (Canadian Triage and Acuity Scale [CTAS]) and different presenting complaints (more viral infection, less minor trauma) than day and evening arrivals, but similar ED length of stay. Shift of arrival was not associated with admission to hospital, but age, gender, socioeconomic status (SES), and day of week were. Discussion ED utilization patterns vary by shift of arrival. Though overnight arrivals represent a smaller proportion of total daily arrivals, their acuity is higher, and the spectrum of disease differs from day or evening arrivals. Conclusions Understanding variations and patterns of ED utilization by shift of arrival and day of week may be helpful in tailoring resource allocation to more accurately and specifically meet demands.

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Stereological Analyses of Hepatocyte Ultrastructure Monitor Arsenic Liver Burdens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s1431927600001896 ◽

1980 ◽

Vol 38 ◽

pp. 442-443

Author(s):

E. M. B. Sorensen ◽

R. R. Mitchell ◽

L. L. Graham

Keyword(s):

Drainage System ◽

Water Levels ◽

Ultrastructural Changes ◽

Hepatocyte Ultrastructure ◽

Lepomis Cyanellus ◽

Large Numbers ◽

Activation Data ◽

Water Analyses ◽

Municipal Lake ◽

Green Sunfish

Endemic freshwater teleosts were collected from a portion of the Navosota River drainage system which had been inadvertently contaminated with arsenic wastes from a firm manufacturing arsenical pesticides and herbicides. At the time of collection these fish were exposed to a concentration of 13.6 ppm arsenic in the water; levels ranged from 1.0 to 20.0 ppm during the four-month period prior. Scale annuli counts and prior water analyses indicated that these fish had been exposed for a lifetime. Neutron activation data showed that Lepomis cyanellus (green sunfish) had accumulated from 6.1 to 64.2 ppm arsenic in the liver, which is the major detoxification organ in arsenic poisoning. Examination of livers for ultrastructural changes revealed the presence of electron dense bodies and large numbers of autophagic vacuoles (AV) and necrotic bodies (NB) (1), as previously observed in this same species following laboratory exposures to sodium arsenate (2). In addition, abnormal lysosomes (AL), necrotic areas (NA), proliferated rough endoplasmic reticulum (RER), and fibrous bodies (FB) were observed. In order to assess whether the extent of these cellular changes was related to the concentration of arsenic in the liver, stereological measurements of the volume and surface densities of changes were compared with levels of arsenic in the livers of fish from both Municipal Lake and an area known to contain no detectable level of arsenic.

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