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Author: Michael David Pawlovich
Publisher:
ISBN:
Category :
Languages : en
Pages : 622
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Book Description
Highway crashes result in over 40,000 deaths per year (500,000 worldwide). Their impact on the national economy is estimated at more than 230 billion dollars. Highway safety is the top priority of the United States Department of Transportation (US DOT). Funds dedicated to the problem are expected to increase substantially. Highway safety is a multidisciplinary issue. An important tool is the safety improvement candidate location (SICL) list. SICL lists list high crash locations for potential mitigation. SICL lists are developed using crash data. Crash frequency, rate, or loss is used to rank the worst locations. Classical statistical techniques are applied. In some cases, simple frequency analyses are used to draw attention to "problem" locations. Simple ranked lists suffer from methodological and practical limitations. Chief among these is the inability to identify "sites with promise", sites where mitigation has the best chance of success. Agencies representing engineering and enforcement generally examine top sites prior to resource dedication. This is resource intensive and efforts of different safety interests are often not well coordinated. For over 20 years, empirical Bayesian (EB) has been proposed to address these limitations. EB identifies sites where mitigation might be most effective, increases estimate confidence, and provides information on relative site safety. EB is being widely implemented at the national level. State and local agencies continue SICL development based on long-standing procedures. EB allows decision makers to more reliably estimate the crash reduction potential at specific sites. However, EB requires development of safety performance functions for road type classes. The technique also requires a priori development of accident modification factors. These requirements add significant expense. Powerful computers and advanced statistical sampling techniques allow hierarchical Bayesian statistics to be applied to highway safety. Hierarchical Bayesian eliminates the need for a priori functions and factors. This approach can readily incorporate additional information. It can also explicitly identify important relationships between causal factors and safety performance. The approach uses data to define results, based on an analyst-specified level of uncertainty. This dissertation discusses SICL list development and evaluates the potential of Bayesian statistics to improve their utility.
Author: Michael David Pawlovich
Publisher:
ISBN:
Category :
Languages : en
Pages : 622
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Book Description
Highway crashes result in over 40,000 deaths per year (500,000 worldwide). Their impact on the national economy is estimated at more than 230 billion dollars. Highway safety is the top priority of the United States Department of Transportation (US DOT). Funds dedicated to the problem are expected to increase substantially. Highway safety is a multidisciplinary issue. An important tool is the safety improvement candidate location (SICL) list. SICL lists list high crash locations for potential mitigation. SICL lists are developed using crash data. Crash frequency, rate, or loss is used to rank the worst locations. Classical statistical techniques are applied. In some cases, simple frequency analyses are used to draw attention to "problem" locations. Simple ranked lists suffer from methodological and practical limitations. Chief among these is the inability to identify "sites with promise", sites where mitigation has the best chance of success. Agencies representing engineering and enforcement generally examine top sites prior to resource dedication. This is resource intensive and efforts of different safety interests are often not well coordinated. For over 20 years, empirical Bayesian (EB) has been proposed to address these limitations. EB identifies sites where mitigation might be most effective, increases estimate confidence, and provides information on relative site safety. EB is being widely implemented at the national level. State and local agencies continue SICL development based on long-standing procedures. EB allows decision makers to more reliably estimate the crash reduction potential at specific sites. However, EB requires development of safety performance functions for road type classes. The technique also requires a priori development of accident modification factors. These requirements add significant expense. Powerful computers and advanced statistical sampling techniques allow hierarchical Bayesian statistics to be applied to highway safety. Hierarchical Bayesian eliminates the need for a priori functions and factors. This approach can readily incorporate additional information. It can also explicitly identify important relationships between causal factors and safety performance. The approach uses data to define results, based on an analyst-specified level of uncertainty. This dissertation discusses SICL list development and evaluates the potential of Bayesian statistics to improve their utility.
Author: United States. National Highway Traffic Safety Administration
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 272
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Book Description
Author:
Publisher:
ISBN:
Category : Traffic safety
Languages : en
Pages : 210
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Book Description
Author: Andrew P. Tarko
Publisher: Purdue University Press
ISBN: 9781622600212
Category :
Languages : en
Pages : 128
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Book Description
The Indiana Department of Transportation (INDOT) manages road safety in Indiana through safety emphasis areas, identification of safety needs within these areas, and development and implementation of transportation interventions that address the safety needs. Indiana applies system-wide performance screening to state roads using the somewhat limited, non-integrated tools available to date. A site-specific countermeasure development and evaluation process (scoring) is being used to determine essential project intent and merit or cost-effectiveness. This project addresses the need for a unifying approach to data-driven identification of safety emphasis areas and safety improvement projects via a systematic evaluation of safety needs in the Indiana road network. Such a method must utilize a database that integrates the Indiana state network, traffic, road inventory, and crash data. A method for safety screening of state roads in Indiana was developed with the following screening tasks in mind: (1) Identifying high crash locations (segments, intersection, ramps and bridges), corridors, and areas in terms of high crash frequency, crash rate, or proportion of a particular crash type. (2) Facilitating program-based screening (e.g., shoulder widening, median improvement). (3) Facilitating special programs and projects (e.g., five percent report). In order to ensure the versatility of screening tasks and the data maintenance, four components were included in the screening tool: (1) data management, (2) standard screening, (3) roads clustering and special studies, (4) results presentation.
Author:
Publisher:
ISBN:
Category : Traffic safety
Languages : en
Pages : 70
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Book Description
Author: American Association of State Highway and Transportation Officials. Task Force for Roadside Safety
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 560
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Book Description
Author: Jake Kononov
Publisher:
ISBN:
Category : Pattern recognition systems
Languages : en
Pages : 103
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Book Description
Since 2000 CDOT has made effective use of Safety Performance Functions (SPF), Level of Service of Safety (LOSS) concept and diagnostic norms to prioritize, plan and scope safety improvements on all projects. A recent trend in road safety research, however, is to develop SPFs for each crash type. This approach is significantly more costly and labor intensive than the one used by CDOT, the benefits of this approach, however, were not well understood. It was not known if having crash type-specific SPFs will improve effectiveness of safety management, or if it will simply make the process more labor-intensive and less accessible to practicing engineers and planners. The intent of this project is to compare the effectiveness of network screening and diagnostic methods using aggregate SPFs and Test of Proportions with crash type SPFs.
Author: William Eugene Tarrants
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
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Book Description
Author:
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 17
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Book Description
Road safety management activities include screening the network for sites with a potential for safety improvement (Network Screening), diagnosing safety problems at specific sites, and evaluating the safety effectiveness of implemented countermeasures. It is important that these activities be both efficient and methodologically sound, since resources would otherwise be wasted on unnecessary treatments for safe elements and elements deserving of treatment would be left untreated. The state-of-the-art methodologies for conducting these activities make use of statistical models to predict expected accident frequencies using traffic volumes and other site characteristics as the input to the models (known as Safety Performance Functions or SPFs). CDOT's research and safety engineers are in the forefront of national efforts to develop methods using SPFs to screen large networks to find sites with a potential for safety improvement. CDOT has previously developed SPFs to identify freeway and rural roadway segments that have the potential for accident reduction. This report documents the data collection, modeling efforts, and findings of a research project to develop SPFs for ten categories of intersections.
Author: Yue Zhao
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 288
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Book Description
Both federal and state policy makers increasingly emphasize the need to reduce traffic fatalities and serious injuries. Finding improved methods to enhance roadway safety has become a top priority. In an attempt to reduce traffic crashes, crash-prone locations should be identified for increased law enforcement activities, education programs, and engineering improvements. This dissertation addresses the critical issue in traffic safety research, methods for corridor-based screening for safety improvement. The role of corridor level screening is to periodically examine the entire urban roadway network in order to generate a list of corridors ranked in order of priority by which detailed engineering studies should be conducted. Ongoing debates in regards to corridor level network screening include what should constitute a corridor for the purpose of network screening, and how a local agency should perform a corridor screening. This research provides answers to these questions. Firstly, a comprehensive literature review was conducted to summarize current practices across the nation pertaining to corridor level network screening. No consensus was found in terms of corridor definitions or screening methodologies. Observed traffic crashes are generally used in evaluating the safety of urban facilities in state departments of transportation although model based evaluation is highly recommended because of its exceptional merits. Secondly, this research proposes a spatial clustering based approach to define urban corridor boundaries. The idea is to group signalized intersections along urban arterial roads based on their spatial auto-correlations. Local spatial measurements (local Moran's I and Getis-Ord index) are adopted to cluster multivariate intersection data. The analyses of arterials in the Reno-Sparks area indicate that the proposed approach provides reasonable corridor definitions. The next section of the dissertation proposes a model-based scheme for screening urban corridors. Significant efforts were made to collect crash, traffic and road inventory data at intersection level, segment level and corridor level. Data assembling and processing were conducted in ArcGIS. Statistical models, such as negative binomial regression models, have been widely used in developing crash prediction models over the past decades. This research investigated other models including the Poisson-Inverse Gaussian and Poisson-Lognormal models. Analysis results imply that for a certain data set different model assumptions will generate quite different results. Overall, the Poisson-Inverse Gaussian model and the Poisson-Lognormal model perform better than the negative binomial model in terms of goodness-of-fit statistics. Due to the high flexibility of Inverse Gaussian and Lognormal distributions, such models can be adopted as alternatives to the negative binomial models in developing crash prediction models. Furthermore, this research explores the effect of spatial correlations in crash prediction modeling. Significant spatial correlations were found not only within different intersection data sets and segment data set, but also in the model residuals and fitted values. The spatial eigenvectors are introduced into the developed models to supplement the spatial effects. Different neighboring proximity structures are tested for assembled data sets to establish the configuration that results in the optimal performance of the prediction models. The comparisons of model goodness-of-fit statistics indicate that the spatial correlations contribute significantly to model heterogeneity. Ignoring spatial impacts may result in biased estimates of model parameters and incorrect inferences. Combining safety performance of intersections and segments, a Corridor Safety Measurement (CSM) is proposed as the performance measure for corridor screening. The measurement is used to identify corridors in the Reno-Sparks area that have promise as locations where improvements will result in substantial crash reduction. The findings from this research will assist engineers to proactively identify and analyze high crash locations from a corridor perspective and detect potential problematic locations not identified through the traditional hot spot analysis.
