scholarly journals Literature Review and Data Analysis on the Impacts of Prolonged Events on Transit System Ridership

2021 ◽  
Author(s):  
Yining Liu ◽  
◽  
Jesus Osorio ◽  
Yanfeng Ouyang ◽  
◽  
...  
Keyword(s):  
Media Coverage ◽  
Research Team ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Long Term Effects ◽  
Modeling Framework ◽  
Transit System ◽  
Executive Orders ◽  
Transit Ridership ◽  
Duration Magnitude

The COVID-19 pandemic has drastically disrupted transit operations and induced significant transit ridership losses worldwide. Given its unprecedented duration, magnitude, and scale, the long-term effects are still unclear. Despite the differences, we can learn from previous disruptive events, such as terrorist attacks and epidemics, in the past 30 years and draw qualitative and quantitative insights about public reactions, ridership recovery periods, and transit agency responses during and after those events. This study sought to understand ridership variations during the current COVID-19 pandemic and inform transit agencies’ future decisions. This project’s research team therefore reviewed the impacts of selected historical events. They observed the following: (i) that most of the reviewed incidents (except for the 9/11 attacks) did not impose prolonged post-event effects on transit ridership for more than one year; (ii) that executive orders (e.g., school closures), transportation services (e.g., intensified airport safety screening and rail station closures), public fear, media reports, and reduced tourism were frequently mentioned as key factors that impacted transit ridership; and (iii) that measures, such as sanitizing vehicles and facilities, improving communications with the public, and promotions and advertisements, can potentially help restore transit ridership. The research team also developed a modeling framework that integrated a Bayesian structural time-series model, a dynamics model for daily transit ridership loss, a prediction module, and ordinary least squares regression to study COVID-19’s effects on the Chicago Transit Authority’s rail ridership. The researchers undertook a model of ridership on the CTA rail system as a potential first step to modeling COVID-19’s effects on transit ridership in northeastern Illinois. The researchers have not modeled ridership on any other transportation mode in northeastern Illinois at this time. The statistical analysis showed that remote learning/work policies and executive orders had answered for most of the ridership loss. Socioeconomic and land-use characteristics could effectively capture their effects. However, these characteristics could not explain people’s different reactions to reported deaths and media attention. Different population groups may have reacted differently to policy decisions, but their responses to reported deaths and media coverage seem random and independent of sociodemographic factors.

Simulation of the Effects of Intermodal Transfer Penalties on Transit Use

1998 ◽  
Vol 1623 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Rongfang Liu ◽  
Ram M. Pendyala ◽  
Steven Polzin
Keyword(s):  
Public Transportation ◽  
System Performance ◽  
Network Performance ◽  
Transportation Systems ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Transit System ◽  
Analysis And Design ◽  
Transit Ridership ◽  
Study Results

In recent times, the planning, analysis, and design of intermodal transfer facilities have been receiving increasing attention as planners attempt to overhaul public transportation systems that are losing ground to the ubiquitous automobile. However, recent research indicates that modeling tools currently used in practice do not adequately account for the effects of transfer penalties on transit ridership and network performance. In an attempt to fill this research need, transit system performance is simulated under different scenarios of intermodal and intramodal transfers. Using a controlled experimental design, transit ridership and system performance are simulated within a traditional four-step travel modeling framework assuming a variety of network configurations characterized by different transfer scenarios. Results show that the presence of a transfer on a transit line can substantially reduce transit ridership and that the extent of this reduction is highly dependent on the type of transfer encountered, that is, whether the transfer is intermodal (across different modes) or intramodal (within the same mode). The implications of the study results on the planning of intermodal transit systems are discussed in detail.

scholarly journals A Novel Method to Model Spatial Distribution of Population

2020 ◽  
Vol 9 (3S) ◽  
pp. 34-40
Keyword(s):  
Spatial Distribution ◽  
Robust Regression ◽  
Open Data ◽  
Fractal Dimensions ◽  
Ordinary Least Squares ◽  
Network Centrality ◽  
Least Squares Regression ◽  
Modeling Framework ◽  
Gis Tools ◽  
Open Source Gis

This study proposes a simulation approach to model the spatial distribution of population (i.e., population density) in a given regioninto a great deal of detail. The approach is based on two mathematical notions: graph theory and fractal geometry. Accordingly, the approach takesself-similarity of road accessibility into account when simulating the spatial distribution of population. The study has followed three steps; (a) formulatea conceptual framework to model the spatial-distribution of population in a given area (i.e. a city or a region) by employingthe network centrality-based fractal dimension, (b) develop a modeling framework, and (c) calibrate the model utilizing empirical data at for five-selected case study areas: Colombo-Sri Lanka, Hanoi-Vietnam, Kolkata -India, Wuhu-China and Singapore. Network centrality-based fractal dimensions were computed by employing open-data and open-source GIS tools. The study applied threetypes of regression techniques: Robust Regression (RR), Ordinary-Least-Squares Regression (OLSR) and Poisson Regression (PR) for derivingthe most appropriate mathematical model. Then, the study validated the model by testing its prediction accuracy. Results revealed that the developed model to simulate spatial distribution of populationin a given area recorded an accepted level of accuracy (R2 >0.75) and predictability (MdAPE< 10%) on a par with the internationalspatial modeling standards. The proposed approach can be adoptedto simulate the spatial-distribution ofpopulation, particularly as a decision-making aid in the domain of urban & regional planning

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

scholarly journals Predicting presenteeism using measures of health status

2021 ◽  
Author(s):  
Cheryl Jones ◽  
Katherine Payne ◽  
Alexander Thompson ◽  
Suzanne M. M. Verstappen
Keyword(s):  
Health Status ◽  
Online Survey ◽  
Activity Index ◽  
Work Productivity ◽  
Rank Correlation ◽  
Ordinary Least Squares ◽  
Statistical Correlation ◽  
Least Squares Regression ◽  
Mapping Algorithm ◽  
Spearman’S Rank Correlation

Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status. Methods Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE). Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = − 0.64); SF6D (r =− 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859). Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

scholarly journals Lack of Informations about COVID-19 Vaccine: From Implications to Intervention for Supporting Public Health Communications in COVID-19 Pandemic

2021 ◽  
Vol 18 (11) ◽  
pp. 6141
Author(s):  
Silva Guljaš ◽  
Zvonimir Bosnić ◽  
Tamer Salha ◽  
Monika Berecki ◽  
Zdravka Krivdić Dupan ◽  
...  
Keyword(s):  
Vaccine Efficacy ◽  
Health Belief ◽  
Ordinary Least Squares ◽  
Vaccine Hesitancy ◽  
Equal Proportion ◽  
Least Squares Regression ◽  
Online Questionnaire ◽  
Demographic Groups ◽  
Population Immunity ◽  
Wide Range

Lack of knowledge and mistrust towards vaccines represent a challenge in achieving the vaccination coverage required for population immunity. The aim of this study is to examine the opinion that specific demographic groups have about COVID-19 vaccination, in order to detect potential fears and reasons for negative attitudes towards vaccination, and to gain knowledge on how to prepare strategies to eliminate possible misinformation that could affect vaccine hesitancy. The data collection approach was based on online questionnaire surveys, divided into three groups of questions that followed the main postulates of the health belief theory—a theory that helps understanding a behaviour of the public in some concrete surrounding in receiving preventive measures. Ordinary least squares regression analyses were used to examine the influence of individual factors on refusing the vaccine, and to provide information on the perception of participants on the danger of COVID-19 infection, and on potential barriers that could retard the vaccine utility. There was an equal proportion of participants (total number 276) who planned on receiving the COVID-19 vaccine (37%), and of those who did not (36.3%). The rest (26.7%) of participants were still indecisive. Our results indicated that attitudes on whether to receive the vaccine, on how serious consequences might be if getting the infection, as well as a suspicious towards the vaccine efficacy and the fear of the vaccine potential side effects, may depend on participants’ age (<40 vs. >40 years) and on whether they are healthcare workers or not. The barriers that make participants‘ unsure about of receiving the vaccine, such as a distrust in the vaccine efficacy and safety, may vary in different socio-demographic groups and depending on which is the point of time in the course of the pandemic development, as well as on the vaccine availability and experience in using certain vaccine formulas. There is a pressing need for health services to continuously provide information to the general population, and to address the root causes of mistrust through improved communication, using a wide range of policies, interventions and technologies.

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

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