scholarly journals Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

2021 ◽  
Vol 14 (1) ◽  
pp. 1129-1148
Author(s):  
Jixiang Liu ◽  
Jiangping Zhou ◽  
Longzhu Xiao
Keyword(s):  
Built Environment ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Demographic Variables ◽  
Tobit Regression ◽  
Environment Variables ◽  
Binomial Regression ◽  
Health Related ◽  
Walking For Transportation

As a sustainable mode of travel, walking for transportation has multiple environmental, social, and health-related benefits. In existing studies, however, such walking has rarely been differentiated between commuting and non-commuting trips. Using multilevel zero-inflated negative binomial regression and multilevel Tobit regression models, this study empirically examines the frequency and duration of commuting and non-commuting walking and their correlates in Xiamen, China. It finds that (1) non-commuting walking, on average, has a higher frequency and longer duration than commuting walking; (2) most socio-demographic variables are significant predictors, and age, occupation, and family size have opposite-direction effects on commuting and non-commuting walking; and (3) different sets of built environment variables are correlated with commuting and non-commuting walking, and the built environment collectively influences the latter more significantly than the former. The findings provide useful references for customized interventions concerning promoting commuting and non-commuting walking.

Household Trip Generation and the Built Environment: Does More Density Mean More Trips?

2019 ◽  
Vol 2673 (5) ◽  
pp. 596-606
Author(s):  
Qin Zhang ◽  
Kelly J. Clifton ◽  
Rolf Moeckel ◽  
Jaime Orrego-Oñate
Keyword(s):  
Built Environment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Trip Generation ◽  
Transportation Demand ◽  
Home Based ◽  
Environment Variables ◽  
Binomial Regression ◽  
Demand Models ◽  
New Development

Trip generation is the first step in the traditional four-step trip-based transportation model and an important transport outcome used in evaluating the impacts of new development. There has been a long debate on the association between trip generation and the built environment, with mixed results. This paper contributes to this debate and approaches the problem with two hypotheses: 1) built environment variables have significant impacts on household total trip generation; and 2) built environment variables have different impacts on trip generation by purpose. This study relied on data from the Portland, Oregon, metropolitan area to estimate negative binomial regression models of household trip generation rates across all modes. Results show that the built environment does have significant and positive influences on trip generation, especially for total number of trips, total number of tours, and home-based shopping-related trips. Moreover, log likelihood ratio tests implied that adding built environment to the base model contributed significantly to improving model explanatory and predictability. These findings suggest that transportation demand models should be more sensitive to the effects of the built environment to better reflect the variations in trip making across regions.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Locally D- and c-optimal designs for Poisson and negative binomial regression models

Metrika ◽  
2006 ◽  
Vol 66 (2) ◽  
pp. 161-172 ◽  
Author(s):  
C. Rodríguez-Torreblanca ◽  
J. M. Rodríguez-Díaz
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Optimal Designs ◽  
Binomial Regression

Intuition and mood of investors in financial markets

2017 ◽  
Vol 30 (2) ◽  
pp. 231-248
Author(s):  
Robson Braga ◽  
Luiz Paulo Lopes Fávero ◽  
Renata Turola Takamatsu
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Real Data ◽  
Financial Assets ◽  
Risky Assets ◽  
Content Type ◽  
Binomial Regression ◽  
Investor Behaviour ◽  
Investment Portfolios

Purpose The purpose of this paper is to evaluate investor behaviour related to the timing of selling financial assets based on an intuitive evaluation of the current market trend and growth expectation. Design/methodology/approach The experiment involved 1,052 volunteer participants who made decisions about stock sales in an environment that simulated a home broker platform to negotiate stocks. Zero-inflated regression models were used. Findings The results show that investors’ attitudes, or beliefs, determine whether they will buy or keep risky assets in their investment portfolios; they may decide to sell such assets, even though market shows an upward trend. Such results make a new contribution to behavioural finance within the context of prospect theory and the disposition effect. Originality/value The originality of this paper lies in the use of new and innovative techniques (zero-inflated Poisson and negative binomial regression models) applied to real data obtained experimentally.

Sign in / Sign up

Export Citation Format

Share Document