01123269

Component

https://doi.org/10.3141/2103-13

http://scholar.google.com...Park&publication_year=2009

Until a few years ago, the dispersion parameter of Poisson–gamma models had been assumed to be invariant of the characteristics of the observations under study, but recent research in highway safety has shown that the dispersion parameter can depend on the covariates of the model. To account for this dependence, some researchers have reported that the dispersion parameter should be modeled solely as a function of segment length. The primary objective of this research was to examine empirically whether the dispersion parameter should be characterized using only the length of the segment. If not, the secondary objective consisted of determining alternative parameterizations using other covariates that would offer a better approach for characterizing the variance function of Poisson–gamma models. To accomplish the study objectives, 10 parameterizations describing the varying dispersion parameter were estimated with three different data sets collected in Texas, California, and Washington State. Flow-only models were used for comparing the parameterizations. The Akaike information criterion and other related goodness-of-fit (GOF) measures were used for evaluating and comparing the different models. The results of this study show that no single functional form or parameterization is suitable for all the data sets. Traffic flow was more significantly associated with the structured variation observed in the data than segment length. It is therefore recommended that transportation safety analysts evaluate different parameterizations and select the most appropriate one using a combination of GOF criteria, including the significance of the model’s coefficients.

01138796

09-0830

English

pp 108-118

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309126182

Print

Figures (3) ; References (27) ; Tables (7)

Data files; Dispersion (Statistics); Goodness of fit; Parametric analysis; Traffic flow; Traffic safety

Segment length

California; Texas; Washington (State)

Highways; Safety and Human Factors; I81: Accident Statistics

TRIS, TRB, ATRI

Jan 30 2009 5:04PM

• Analysis of Effects of Pavement Marking Retroreflectivity on Traffic Crash Frequency on Highways in North Carolina: Application of Artificial Neural Networks and Generalized Estimating Equations
• Calibration of the Highway Safety Manual's Accident Prediction Model for Italian Secondary Road Network
• Comparison of Simulated Freeway Safety Performance with Observed Crashes
• Developing Accident Modification Functions: Exploratory Study
• Elementary Units of Exposure
• Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots: Naive Ranking, Empirical Bayes, and Full Bayes Methods
• Incorporating Traffic Operation Measures in Safety Analysis at Signalized Intersections
• Road Data Aggregation and Sectioning Considerations for Crash Analysis
• Safety Effectiveness of Lane and Shoulder Width Combinations on Rural, Two-Lane, Undivided Roads
• Safety Evaluation of Curve Delineation Improvements: Empirical Bayes Observational Before-and-After Study
• Speed and Safety
• Traffic Microsimulation Modeling to Study a Traffic Signal Incident Reduction Function