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

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.

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

Journal of Planning Education and Research ◽

10.1177/0739456x19845043 ◽

2019 ◽

pp. 0739456X1984504 ◽

Cited By ~ 4

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.

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

Sustainability ◽

10.3390/su11236643 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6643 ◽

Cited By ~ 3

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.

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/1818/1/012100 ◽

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

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

Criminal Justice and Behavior ◽

10.1177/0093854818790291 ◽

2018 ◽

Vol 46 (1) ◽

pp. 154-172 ◽

Cited By ~ 13

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.

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Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models

Accident Analysis & Prevention ◽

10.1016/j.aap.2018.12.009 ◽

2019 ◽

Vol 123 ◽

pp. 303-313 ◽

Cited By ~ 15

Author(s):

Md Asif Raihan ◽

Priyanka Alluri ◽

Wensong Wu ◽

Albert Gan

Keyword(s):

Negative Binomial ◽

Crash Modification Factors ◽

Negative Binomial Models ◽

Binomial Models

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Managing Inflation

Crime & Delinquency ◽

10.1177/0011128716679796 ◽

2016 ◽

Vol 63 (1) ◽

pp. 77-87 ◽

Cited By ~ 5

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.

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