Do Denser Neighborhoods Have Safer Streets? Population Density and Traffic Safety in the Philadelphia Region

2019 ◽  
pp. 0739456X1984504 ◽  
Author(s):  
Erick Guerra ◽  
Xiaoxia Dong ◽  
Michelle Kondo
Keyword(s):  
Population Density ◽  
Traffic Safety ◽  
Urban Form ◽  
Urban Areas ◽  
Negative Binomial ◽  
Pedestrian Safety ◽  
Negative Binomial Models ◽  
The Relationship ◽  
Binomial Models ◽  
Density Results

This study uses multilevel negative binomial models to investigate relationships between neighborhood socio-demographics, urban form, roadway characteristics, traffic collisions, injuries, and fatalities on the Philadelphia region’s streets from 2010 to 2014. We pay particular attention to neighborhood population density. Results indicate that streets in denser neighborhoods have fewer overall collisions, injuries, and fatalities. The association with pedestrian safety is mixed and somewhat uncertain across urban areas and model specifications. This study highlights the importance of population density in traffic safety and helps explain some of the variation in findings across studies examining the relationship between urban form and pedestrian safety.

Analysis of Toll Lanes Access Violation Behavior: Example of Express Toll Lanes on I-405

2018 ◽  
Vol 2672 (14) ◽  
pp. 1-9
Author(s):  
Hiroyuki Hinohara ◽  
Yinhai Wang
Keyword(s):  
Negative Binomial ◽  
General Purpose ◽  
Washington State ◽  
Entry And Exit ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Toll Lanes ◽  
Negative Binomial Models ◽  
The Relationship ◽  
Binomial Models

Washington State has introduced express toll lanes (ETLs) on I-405 as a managed lane project. One of problems in the operation of ETLs operation is the access violation in which vehicles cross the double white lines, which separate ETLs from general purpose lanes. The objective of this paper is to identify access violation patterns and quantitatively describe the relationship between access violation frequency and traffic condition, user type and road segment characteristics. Finally, this paper develops negative binomial models to predict access violation frequency. The analysis results show that that traffic conditions factors influence violation behavior differently depending on whether the violation occurred when entering or exiting ETLs, which suggests that entry and exit violation behaviors should be analyzed separately. In addition, the finding that casual users commit violations more frequently than frequent users implies the possibility of unintentional violations. It is expected that these results support enforcement against violations and improve ETLs operation in the future.

Relating Household Consumption Expenditures to Road Traffic Fatalities: A Rural–Urban Study

2021 ◽  
pp. 036119812110332
Author(s):  
Ruchika Agarwala ◽  
Vinod Vasudevan
Keyword(s):  
Developing Countries ◽  
Traffic Safety ◽  
Rural Areas ◽  
Urban Areas ◽  
Household Consumption ◽  
Household Expenditure ◽  
Traffic Fatality ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
The Relationship

Research shows that traffic fatality risk is generally higher in rural areas than in urban areas. In developing countries, vehicle ownership and investments in public transportation typically increase with economic growth. These two factors together increase the vehicle population, which in turn affects traffic safety. This paper presents a study focused on the relationship of various factors—including household consumption expenditure data—with traffic fatality in rural and urban areas and thereby aims to fill some of the gaps in the literature. One such gap is the impacts of personal and non-personal modes of travel on traffic safety in rural versus urban areas in developing countries which remains unexplored. An exhaustive panel data modeling approach is adopted. One important finding of this study is that evidence exists of a contrasting relationship between household expenditure and traffic fatality in rural and urban areas. The relationship between household expenditure and traffic fatality is observed to be positive in rural areas and a negative in urban areas. Increases in most expenditure variables, such as fuel, non-personal modes of travel, and two-wheeler expenditures, are found to be associated with an increase in traffic fatality in rural areas.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

scholarly journals An Application Comparison of Two Negative Binomial Models on Rainfall Count Data

2021 ◽  
Vol 1818 (1) ◽  
pp. 012100
Author(s):  
L. H. Hashim ◽  
N. K. Dreeb ◽  
K. H. Hashim ◽  
Mushtak A. K. Shiker
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Models ◽  
Binomial Models

scholarly journals Criminal Justice Debt During the Prisoner Reintegration Process: Who Has It and How Much?

2018 ◽  
Vol 46 (1) ◽  
pp. 154-172 ◽  
Author(s):  
Nathan W. Link
Keyword(s):  
Criminal Justice ◽  
Negative Binomial ◽  
Policy Implications ◽  
Community Supervision ◽  
Individual Level ◽  
Prisoner Reintegration ◽  
Negative Binomial Models ◽  
The Individual ◽  
Binomial Models ◽  
Transition Home

Much recent, national attention has centered on financial sanctions and associated debt burdens related to criminal justice. Scholars and practitioners alike have argued that financial debt among the incarcerated, in particular, exacerbates a transition home already defined by difficulties. This article takes a step back and assesses who is at risk of these adverse consequences in reentry by examining the extent of debt burdens that resulted from financial sanctions, its sources, and the individual-level factors that are associated with owing criminal justice debt. Relying on the Returning Home data ( N = 740), results from descriptive analyses, logistic regression, and negative binomial models show that a large proportion of respondents owed debts and that debt was strongly linked with being mandated to community supervision. In addition, debt amount was predicted by employment, income, and race. Policy implications in the realm of financial sanctioning by courts and correctional agencies are discussed.

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.

scholarly journals The Relationship Between Social Vulnerability and COVID-19 Incidence Among Louisiana Census Tracts

2021 ◽  
Vol 8 ◽  
Author(s):  
Erin N. Biggs ◽  
Patrick M. Maloney ◽  
Ariane L. Rung ◽  
Edward S. Peters ◽  
William T. Robinson
Keyword(s):  
Population Density ◽  
Social Vulnerability ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
Census Tracts ◽  
Control And Prevention ◽  
Choropleth Maps ◽  
The Relationship

Objective: To examine the association between the Centers for Disease Control and Prevention (CDC)'s Social Vulnerability Index (SVI) and COVID-19 incidence among Louisiana census tracts.Methods: An ecological study comparing the CDC SVI and census tract-level COVID-19 case counts was conducted. Choropleth maps were used to identify census tracts with high levels of both social vulnerability and COVID-19 incidence. Negative binomial regression with random intercepts was used to compare the relationship between overall CDC SVI percentile and its four sub-themes and COVID-19 incidence, adjusting for population density.Results: In a crude stratified analysis, all four CDC SVI sub-themes were significantly associated with COVID-19 incidence. Census tracts with higher levels of social vulnerability were associated with higher COVID-19 incidence after adjusting for population density (adjusted RR: 1.52, 95% CI: 1.41-1.65).Conclusions: The results of this study indicate that increased social vulnerability is linked with COVID-19 incidence. Additional resources should be allocated to areas of increased social disadvantage to reduce the incidence of COVID-19 in vulnerable populations.

scholarly journals The association between self-harm and area-level characteristics in Northern Ireland: an ecological study

2019 ◽  
Vol 29 (5) ◽  
pp. 948-953 ◽  
Author(s):  
Eve Griffin ◽  
Brendan Bonner ◽  
Christina B Dillon ◽  
Denise O’Hagan ◽  
Paul Corcoran
Keyword(s):  
Northern Ireland ◽  
Population Density ◽  
Suicidal Behaviour ◽  
Urban Areas ◽  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Binomial Regression ◽  
Self Harm ◽  
Deprived Areas

Abstract Background Factors contributing to suicidal behaviour are complex and multi-faceted. This study took an ecological approach to examine the association between area-level factors and rates of self-harm in Northern Ireland. Methods Data on self-harm presentations to emergency departments (EDs) were obtained from the Northern Ireland Self-harm Registry. The study included residents of Northern Ireland aged 16–64 years. Deprivation was measured using the Northern Ireland Multiple Deprivation Measure 2017. Population density and social fragmentation were calculated using measures from the 2011 census. Associations between area-level factors and self-harm rates were explored using negative binomial regression. Results Between 2013 and 2015, 14 477 individuals aged 16–64 years presented to EDs in Northern Ireland following self-harm. The rate of self-harm was 472 per 100 000 and was higher for male residents (478 vs. 467). Self-harm rates were highest in urban areas—680 per 100 000 in Belfast City and 751 per 100 000 in Derry City. Rates of self-harm in Northern Ireland were more than four times higher in the most deprived areas. A positive association with rates of self-harm held for the deprivation domains of employment, crime, education, health and income. There was a moderate association with population density. Some gender differences emerged, with associations with male rates of self-harm more pronounced. Conclusion These findings indicate that self-harm rates are highest for those residing in highly deprived areas, where unemployment, crime and low level of education are challenges. Community interventions tailored to meet the needs of specific areas may be effective in reducing suicidal behaviour.

scholarly journals An efficient EM algorithm for the mixture of negative binomial models

2019 ◽  
Vol 1324 ◽  
pp. 012093
Author(s):  
Chunmao Huang ◽  
Xingwang Liu ◽  
Tianyuan Yao ◽  
Xiaoqiang Wang
Keyword(s):  
Em Algorithm ◽  
Negative Binomial ◽  
Negative Binomial Models ◽  
Binomial Models
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