Evaluating traffic safety network screening: an initial framework utilizing the hierarchical Bayesian philosophy

Pawlovich, Michael

doi:10.31274/rtd-180813-97

Cited by 3 publications

(15 citation statements)

References 23 publications

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“…For example, the method recognizes that a road segment or an intersection with zero crash during a given time period is expected to experience some crashes, close to the average number of crashes in similar sites, in the long term. However, as the Institute of Transportation Engineers explains, the EB method is not a fully Bayesian model, and it is "complex and not ready for widespread implementation" (Pawlovich, 2003). Moreover, the development of an SPF requires a large database storing the characteristics of reference sites (e.g., AADT per road segment, segment length, lane width, shoulder width, road grade, presence of a horizontal curve).…”

Section: Statistical Methods Overviewmentioning

confidence: 99%

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Azimian¹

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BAYESIAN SPATIO-TEMPORAL ANALYSIS OF ROAD TRAFFIC CRASHES Amin Azimian Road traffic crashes are one of the major causes of death and serious injury in the US, leading to economic losses and human suffering. In recent years, several research efforts have been made to screen areas and locate hotspots/zones and to identify the factors contributing to traffic crashes of different severity levels. Such research typically aggregates crash locations into spatial units at the macro level, such as counties, or at the micro level, such as road segments or intersections. Many crash estimation methods have been proposed in the literature. These methods range from classical approaches, such as the linear, Poisson, negative binomial, and logistic regression methods, to more state-of-the-art approaches, such as the empirical Bayesian (EB), spatial autoregressive, and full Bayesian methods. A considerable drawback of classical methods is that they cannot account for the regression-to-the-mean bias and potential unobserved heterogeneity, which can result in unstable and biased parameter estimates. Additionally, EB and spatial autoregressive methods are unable to address multilevel data and group-level random effects. By contrast, the full Bayesian framework is more flexible and can be easily extended to include random effect terms that can act as proxies for unobserved or missing covariates that have a spatial or temporal structure. Although the full Bayesian framework appears to be a promising methodology for dealing with crash data, the few previous studies in the area of road traffic safety that used this framework had some major limitations. Some studies performed their analyses in DEDICATION This dissertation is lovingly dedicated to my family. Your support and encouragement mean so much to me, and I am forever grateful. Thank you! v ACKNOWLEDGMENTS First, I would like to thank my advisor, Dr. V. Dimitra Pyrialakou. I am deeply honored to be her first Ph.D. student. I appreciate all her contributions in terms of her time, ideas, and efforts to make my Ph.D. experience truly productive and stimulating. The joy and enthusiasm she has for research were contagious and motivational for me, especially during the tough times in my Ph.D. pursuit. I also thank my committee members, namely, Drs. David Martinelli, Kakan Dey, Sijin Wen, Ashish Nimbarte, and Steven Lavrenz, for all their advice and support. Finally, I thank my family for their love, care, and support. Specifically, I express my immense gratitude to my lovely wife, Hanieh, for her patience and unparalleled support. My family is my biggest support system, materially and morally. vi

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Section: Statistical Methods Overviewmentioning

confidence: 99%

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Azimian¹

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“…In contrast, the Bayesian methods allow for a statement concerning the probability of P falling in the interval to be made for credible sets (105). An additional difference is that, while Bayesian methods do rely on prior distributions, sometimes causing them to be regarded as subjective, they do not rely on mathematical idealizations of a quantity, as is the case with classical methods (96).…”

Section: Traffic Datamentioning

confidence: 99%

“…Conversely, most Bayesian problems rely on some form of scientific judgment to specify the "likelihood" (all relevant experimental information about the unknown parameter) and prior distribution portions of the model. This aspect of Bayesian statistics offers an advantage over classical methods in that whenever another analysis is conducted, the Bayesian-based database and knowledge grow (96).…”

Section: Traffic Datamentioning

confidence: 99%

Assessing tradeoffs between safety and operations: an analytical framework

Veneziano

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“…For an extensive discussion of these methods, see (Zegeer (1986); Ogden (1996); H.R. Green (2001);Pawlovich (2003)). Though all of these methods have proven useful, all can be improved at least with respect to the identification of high crash locations and each has limitations, as discussed in the documents which we cite above.…”

Section: Introductionmentioning

confidence: 99%

“…Many network screening methods have proven useful (see e.g. Zegeer (1986), Ogden (1996), Pawlovich (2003)). Many methods that are recommended for the construction of lists of SWiP's rely on the estimation of long-run crash frequencies at each site.…”

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confidence: 99%

Modeling crash frequency data

Schmidt

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Evaluating traffic safety network screening: an initial framework utilizing the hierarchical Bayesian philosophy

Cited by 3 publications

References 23 publications

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Assessing tradeoffs between safety and operations: an analytical framework

Modeling crash frequency data

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