Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

2021 ◽  
Vol 14 (1) ◽  
pp. 1149-1164
Author(s):  
Tao Tao ◽  
Jason Cao
Keyword(s):  
Low Income ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Twin Cities ◽  
Traffic Data ◽  
Highway Traffic ◽  
Transit Service ◽  
Poverty Concentration ◽  
Transit Agencies ◽  
Transportation Agencies

During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.

The Youth Pass

2017 ◽  
Vol 2652 (1) ◽  
pp. 116-123 ◽  
Author(s):  
Ian Thistle ◽  
Laurel Paget-Seekins
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Attrition Rate ◽  
Massachusetts Bay ◽  
Local Municipalities ◽  
Pass Program ◽  
Transit Agencies ◽  
To Receive ◽  
Transportation Agencies ◽  
Adequate Data

Public transportation agencies provide reduced fares to seniors, students, and disabled people, but only infrequently provide discounts to low-income members of the general population. A major reason for this is that it is difficult and labor-intensive for transit agencies to determine who is of low income. To address societal need and pilot the feasibility of such a program, the Massachusetts Bay Transportation Authority (MBTA) piloted a program for young people who were unable to receive reduced fares in another way. The MBTA partnered with local municipalities, and applicants proved their eligibility for the program through partner offices. The research requirements in the program provided adequate data to evaluate the effects of the program, but the requirements themselves negatively affected participation and attrition in the pilot. The ways the research affected participation are explored in detail, particularly the attrition rate of subjects throughout the study. It was found that the research requirements disproportionately affected those of very low income as well as African-American and Hispanic participants. Using the data from the pilot, the MBTA decided to implement a full youth pass program benefiting those populations without the barriers of the pilot.

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For

scholarly journals Deoxygenation impacts on Baltic Sea cod: Dramatic declines in ecosystem services of an iconic keystone predator

AMBIO ◽  
2021 ◽  
Author(s):  
Alessandro Orio ◽  
Yvette Heimbrand ◽  
Karin Limburg
Keyword(s):  
Ecosystem Services ◽  
Baltic Sea ◽  
Gadus Morhua ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Maximum Length ◽  
The Baltic Sea ◽  
Hypoxic Zone ◽  
The Baltic ◽  
Baltic Cod

AbstractThe intensified expansion of the Baltic Sea’s hypoxic zone has been proposed as one reason for the current poor status of cod (Gadus morhua) in the Baltic Sea, with repercussions throughout the food web and on ecosystem services. We examined the links between increased hypoxic areas and the decline in maximum length of Baltic cod, a demographic proxy for services generation. We analysed the effect of different predictors on maximum length of Baltic cod during 1978–2014 using a generalized additive model. The extent of minimally suitable areas for cod (oxygen concentration ≥ 1 ml l−1) is the most important predictor of decreased cod maximum length. We also show, with simulations, the potential for Baltic cod to increase its maximum length if hypoxic areal extent is reduced to levels comparable to the beginning of the 1990s. We discuss our findings in relation to ecosystem services affected by the decrease of cod maximum length.

A Scientometric Study of Foraminiferal Research in Brazil

2021 ◽  
Vol 51 (4) ◽  
pp. 267-285
Author(s):  
Beatriz Lima Vieira ◽  
Letícia Rizzetto Patrocínio ◽  
Douglas Villela de Oliveira Lessa ◽  
Doriedson Ferreira Gomes
Keyword(s):  
Continental Margin ◽  
Generalized Additive Model ◽  
Scientific Literature ◽  
Additive Model ◽  
Scientific Production ◽  
Field Of Study ◽  
The North ◽  
Marine Realm ◽  
Scientometric Study ◽  
Brazilian Science

ABSTRACT Scientometrics is a field of study that involves measuring and analyzing scientific literature and can be a valuable tool to assess and reveal major gaps in national scientific production. Among the major challenges for Brazilian science is the development of research in the extensive national marine realm. This paper provides a scientometric survey of papers involving foraminiferal research in Brazil. The metrics utilized were papers listed in “Capes Portal” and “Scopus” databases up to the year of 2019. A total of 324 papers were found and 177 were selected based upon criteria established. A generalized additive model (GAM) was used to establish a relationship between publications and time. Studies involving foraminifera increased in Brazil from 1952 to 2019. Most studies have been conducted in the southeast region. We identified the need for more research on foraminifera to be carried out in the Brazilian continental margin, especially in the north and northeast regions of the country.

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