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Author: Rajasekhar Basavaraju Publisher: ISBN: Category : Languages : en Pages : 314
Book Description
One goal of transportation safety engineers is to identify and prioritize safety deficient locations along roadways. The identification of such locations allows examination of possible engineering, enforcement, and/or educational measures to improve safety. In general, each state in the U.S. selects its own methodology for identification and prioritization of high crash locations. The Iowa Department of Transportation (DOT) annually identifies and then ranks the top 100 high crash locations using a combination of crash frequency, crash rate, and value loss. In the present methodology adopted by Iowa DOT for ranking of high crash locations, the crash database is filtered before the potential high crash locations are ranked. But, the currently used filter seems to inherently favor locations with fatalities. Hence, two new filters with a potential to reduce the bias have been analyzed and the sensitivity analysis of the two filters is explained as part of this thesis. Also, in the current methodology, once the crash database is filtered for the potential high crash locations, three ranking factors, crash frequency, crash rate, and value loss are used for ranking the locations. In the current process, all the three ranking factors are considered equal contributors to the final ranking position of a high crash location. This equal weightage does not seem to be correct. Hence, the impact of different weighting is considered and a sensitivity analysis of twenty-seven new combinations of ranking factors is performed as part of this research.
Author: Rajasekhar Basavaraju Publisher: ISBN: Category : Languages : en Pages : 314
Book Description
One goal of transportation safety engineers is to identify and prioritize safety deficient locations along roadways. The identification of such locations allows examination of possible engineering, enforcement, and/or educational measures to improve safety. In general, each state in the U.S. selects its own methodology for identification and prioritization of high crash locations. The Iowa Department of Transportation (DOT) annually identifies and then ranks the top 100 high crash locations using a combination of crash frequency, crash rate, and value loss. In the present methodology adopted by Iowa DOT for ranking of high crash locations, the crash database is filtered before the potential high crash locations are ranked. But, the currently used filter seems to inherently favor locations with fatalities. Hence, two new filters with a potential to reduce the bias have been analyzed and the sensitivity analysis of the two filters is explained as part of this thesis. Also, in the current methodology, once the crash database is filtered for the potential high crash locations, three ranking factors, crash frequency, crash rate, and value loss are used for ranking the locations. In the current process, all the three ranking factors are considered equal contributors to the final ranking position of a high crash location. This equal weightage does not seem to be correct. Hence, the impact of different weighting is considered and a sensitivity analysis of twenty-seven new combinations of ranking factors is performed as part of this research.
Author: Shenjun Yao Publisher: Open Dissertation Press ISBN: 9781361315767 Category : Languages : en Pages :
Book Description
This dissertation, "Advances in Spatial Analysis of Traffic Crashes: the Identification of Hazardous Road Locations" by Shenjun, Yao, 姚申君, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The identification of hazardous road locations is important to the improvement of road safety. However, there is still no consensus on the best method of identifying hazardous road locations. While traditional methods, such as the hot spot methodology, focus on the physical distances separating road crashes only, the hot zone methodology takes network contiguity into consideration and treats contiguous road segments as hazardous road locations. Compared with the hot spot method, hot zone methodology is a relatively new direction and there still remain a number of methodological issues in applying the method to the identification of hazardous road locations. Hence, this study aims to provide a GIS-based study on the identification of crash hot zones as hazardous road locations with both link-attribute and event-based approaches. It first explores the general procedures of the two approaches in identifying traffic crash hot zones, and then investigates the characteristics of the two approaches by conducting a range of sensitivity analysis on defining threshold value and crash intensity with both simulated and empirical data. The results suggest that it is better to use a dissolved road network instead of a raw-link-node road network. The segmentation length and the interval of reference points have great impacts on the identification of hot zones, and they are better defined as 100 meters considering the stabilities of the performance. While employing a numerical definition to identify hot zones is a simple and effort-saving approach, using the Monte Carlo method can avoid selection bias in choosing an appropriate number as the threshold value. If the two approaches are compared, it is observed that the link-attribute approach is more likely to cause false negative problem and the event-based approach is prone to false positive problem around road junctions. No matter which method is used, the link-attribute approach requires less computer time in identifying crash hot zones. When a range of environmental variables have to be taken into consideration, the link-attribute approach is superior to the event-based approach in that it is easier for the link-attribute approach to incorporate environmental variables with statistical models. By investigating the hot zone methodology, this research is expected to enrich the theoretical knowledge of the identification of hazardous road locations and to practically provide policy-makers with more information on identifying road hazards. Further research efforts have to be dedicated to the ranking of hot zones and the investigation of false positive and false negative problems. DOI: 10.5353/th_b5043444 Subjects: Traffic accidents - Statistical methods
Author: Simon Washington Publisher: CRC Press ISBN: 0429520751 Category : Technology & Engineering Languages : en Pages : 496
Book Description
The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.
Author: Andrea Saltelli Publisher: John Wiley & Sons ISBN: 9780470725177 Category : Mathematics Languages : en Pages : 304
Book Description
Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
Author: Rajasekhar Basavaraju
Author: Rajasekhar Basavaraju
Author: 
Author: Shenjun Yao
Author: Iowa State University
Author: D. Vangi
Author: Ghazan Khan
Author: Simon Washington
Author: Andrea Saltelli
Author: 
Author: