Application of the Poisson-Tweedie distribution in analyzing crash frequency data.

This paper describes a study that applies the Poisson-Tweedie distribution in developing crash frequency models. The Poisson-Tweedie distribution offers a unified framework to model overdispersed, underdispersed, zero-inflated, spatial, and longitudinal count data, as well as multiple response variables of similar or mixed types. The form of its variance function is simple, and can be specified as the mean added to the product of dispersion and mean raised to the power P.

Compliance with multidisciplinary team meeting management recommendations.

Objectives: The objective of this study was to evaluate patient compliance with management recommendations given by a breast cancer multidisciplinary team (MDT), assess for reasons for noncompliance, and perform an exploratory assessment on breast cancer outcomes in noncompliant patients.

Materials And Methods: A retrospective analysis of prospectively collected data was undertaken for patients selected by their primary clinician to be discussed at the MDT of Breast Cancer Research Centre-WA in Perth between 1st March 2011 and the 28th February 2016. The primary objective was the rate of compliance with MDT management recommendations.

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

The objective of this study was to develop crash modification factors (CMFs) for bicycle crashes for different roadway segment and intersection facility types in urban areas. The study used four years (2011-2014) of crash data from Florida to quantify the safety impacts of roadway and traffic characteristics, bicycle infrastructure, and bicycle activity data on bicycle crashes. A cross-sectional analysis using Generalized Linear Model (GLM) approach with Zero Inflated Negative Binomial (ZINB) distribution was adopted to develop the relevant regression models in this study.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications.

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S.

Safety performance evaluation of cable median barriers on freeways in Florida.

Objective: This article aims to evaluate the safety performance of cable median barriers on freeways in Florida.

Method: The safety performance evaluation was based on the percentages of barrier and median crossovers by vehicle type, crash severity, and cable median barrier type (Trinity Cable Safety System [CASS] and Gibraltar system). Twenty-three locations with cable median barriers totaling about 101 miles were identified.

Analyzing pedestrian crash injury severity at signalized and non-signalized locations.

This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections. The factors explored include geometric predictors (e.g.

Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S.

Introduction: The Moving Ahead for Progress in the 21st Century (MAP-21) includes a separate program that supports safety improvements to reduce the number of fatalities and injuries at public highway-railroad grade crossings (HRGCs). This study identifies the significant factors affecting crash injury severity at public HRGCs in the United States.

Method: Crashes from 2009 through 2013 on 5,528 public HRGCs, extracted from the Federal Railroad Administration database, were used in the analysis.

Prioritizing Highway Safety Manual's crash prediction variables using boosted regression trees.

The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency's safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states' databases.

Effect of driver's age and side of impact on crash severity along urban freeways: a mixed logit approach.

Introduction: This study identifies geometric, traffic, environmental, vehicle-related, and driver-related predictors of crash injury severity on urban freeways.

Method: The study takes advantage of the mixed logit model's ability to account for unobserved effects that are difficult to quantify and may affect the model estimation, such as the driver's reaction at the time of crash. Crashes of 5 years occurring on 89 urban freeway segments throughout the state of Florida in the United States were used.

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas.

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida.

Identifying traditional and nontraditional predictors of crash injury severity on major urban roadways.

Objective: This study identifies and compares the factors that contribute to injury severity on urban freeways and arterials and recommends potential countermeasures to enhance the safety of both facilities. The study makes use of an extensive data set from the State of Florida in the United States. To obtain a more complete picture, this study explores both traditional and nontraditional severity predictors.