Modelling factors affecting the severities of injury from motor vehicle traffic crashes in Namibia using the 2008-2016 National road safety data PDF Download

Author: Maano Nalikolekwe Shimanda
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 168

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Author: National Highway Traffic Safety Administration
Publisher: CreateSpace
ISBN: 9781492399919
Category : Transportation
Languages : en
Pages : 50

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One of the most important factors that affects a person's risk of injury in a motor vehicle crash is the age of the person. This study investigates patterns of injury severity, location of injuries, and contact sources for the driver injuries by driver age. Based on the data from NHTSA's National Automotive Sampling System — Crashworthiness Data System (NASS-CDS) from 1993 through 2004, this study examines in great detail the driver injury severity, injured body regions, and injury contact sources by driver age in rollover and non-rollover real-world traffic crashes. The effect of seat belt use on injury patterns is also investigated.

Author: United States. General Accounting Office
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 84

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Author: Jeffrey Cailis Bullard
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 0

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Road traffic crashes are a leading cause of serious injuries and fatalities globally. They also place unnecessary developmental and economic burdens on low- and middle-income countries (LMICs) as they account for most of the world's road related deaths. This is typically due to both the increased frequency of dangerous crash types and the increased severity of said crash types. Rollover crashes while quite rare are a particularly dangerous crash type among other various crash types. In the case of Namibia, rollover crashes reportedly accounted for 34% of both road related injuries and fatalities in Namibia for 2020. Therefore, it crucial to understand the contributing factors and their associated effects on rollover crash severities in these countries. This thesis aims to investigate the significant factors influencing crash severities and their associated impact magnitudes on single vehicle rollover crashes in Namibia by adopting a mixed logit with heterogeneity in means and variances approach for 2014-2016 historical crash records. Additionally, roadside safety features such as paved road shoulder are a crucial component in the mitigation of these rollover crash types. While the lack of road safety features is an issue in LMICs, so is the ability to map the presence of said features. This a case study on the B2 highway in Namibia, aiming to classify images of the road according to the size of the road shoulder is also conducted. Google Street View images are labeled based to their road shoulder quality (none, up to two feet, larger than two feet). Based on the labeled images, we train a deep-artificial-neural-network to classify images according to those labels. Results indicate that rollover crashes in Namibia are influenced by several explanatory variables. Additionally, results from the deep-learning model have the potential to lower costs of mapping road safety features in LMIC.

Author: World Health Organization
Publisher: World Health Organization
ISBN: 9241565063
Category : Business & Economics
Languages : en
Pages : 338

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"The Global status report on road safety 2015, reflecting information from 180 countries, indicates that worldwide the total number of road traffic deaths has plateaued at 1.25 million per year, with the highest road traffic fatality rates in low-income countries. In the last three years, 17 countries have aligned at least one of their laws with best practice on seat-belts, drink-driving, speed, motorcycle helmets or child restraints. While there has been progress towards improving road safety legislation and in making vehicles safer, the report shows that the pace of change is too slow. Urgent action is needed to achieve the ambitious target for road safety reflected in the newly adopted 2030 Agenda for Sustainable Development: halving the global number of deaths and injuries from road traffic crashes by 2020. Made possible through funding from Bloomberg Philanthropies, this report is the third in the series, and provides a snapshot of the road safety situation globally, highlighting the gaps and the measures needed to best drive progress."--Publisher's description.

Author: Louis V. Lombardo
Publisher:
ISBN:
Category : Crash injuries
Languages : en
Pages : 116

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Author: Marjorie Peden
Publisher: DIANE Publishing
ISBN: 1437904068
Category : Transportation
Languages : en
Pages : 67

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Book Description
Every day thousands of people are killed and injured on our roads. Millions of people each year will spend long weeks in the hospital after severe crashes and many will never be able to live, work or play as they used to do. Current efforts to address road safety are minimal in comparison to this growing human suffering. This report presents a comprehensive overview of what is known about the magnitude, risk factors and impact of road traffic injuries, and about ways to prevent and lessen the impact of road crashes. Over 100 experts, from all continents and different sectors -- including transport, engineering, health, police, education and civil society -- have worked to produce the report. Charts and tables.

Author: Yingge Xiong
Publisher:
ISBN:
Category : Traffic accident investigation
Languages : en
Pages : 107

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The American Association of State Highway Transportation Officials (AASHTO) has established a goal to halve the national number of highway fatalities by 2027. In order to fulfill the states' portion of the goal, efforts are needed on building sophisticated crash injury data analysis methodologies for reliable safety hazards identification in development of state and local safety programs. On account of the considerable amount of unobserved and omitted on-the-spot information in crash datasets used by agencies, the issue of unobserved heterogeneity in crash data modeling has been identified and has attracted growing attention in recent years. Prior studies on relationships between highway safety elements and crash injury severity outcomes have suggested that effects of contributing factors in different situations may be nonhomogeneous. However, little is understood about the dynamics. This dissertation aims to contribute to the literature by (a) investigating how effects of hazardous factors vary across road segments and over time periods and (b) how they would interact with the effects of other factors on crash injury severity outcomes, with accommodation of unobserved heterogeneity in different levels and without prespecified assumptions on probability distributions. The analysis went beyond the heterogeneous effects formulation and included the model estimation details based on Bayesian inference. This dissertation consists two studies: (1) A particular case of cross-sectional unobserved heterogeneity modeling for a safety intervention program was studied by using Indiana adolescent crash data. A Markov Chain Monte Carlo (MCMC) algorithm was developed for estimation and a permutation sampler was extended for model identification. (2) A general case of time-varying unobserved heterogeneity modeling was carried out based on Indiana rural interstate crash data. Reparameterization and partially marginalized conditional samplers techniques were designed to reduce autocorrelation between consecutive draws and to improve the convergence efficiency of chains in estimation simulation. The implications for implementation of regulation enforcement and highway infrastructure upgrade and maintenance were discussed. The empirical results can provide substantial insights to government agencies that are concerned about strategic programming of safety countermeasures to leverage safety intervention resources. The methodologies set forth herein should be of interest to individuals who are developing analysis tools for crash cause diagnosis in state and local transportation safety programs, and have the potential for valuable new insights into a wide variety of questions in discrete data modeling.

Author: Simon Washington
Publisher: CRC Press
ISBN: 0429520751
Category : Technology & Engineering
Languages : en
Pages : 496

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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: Shamsunnahar Yasmin
Publisher:
ISBN:
Category :
Languages : en
Pages :

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"The objective of the dissertation is to develop advanced econometric frameworks to address methodological gaps in safety literature while employing these models developed to study important empirical issues. Crash severity analysis has evolved on examining the influence of several factors, comprising of driver characteristics, vehicle characteristics, roadway attributes, environmental factors and crash characteristics on traffic crash related severities. These associated risk factors are critical to assist decision makers, transportation officials, insurance companies, and vehicle manufacturers to make informed decisions to improve road safety, thereby providing empirical evidence regarding the critical factors would allow us to suggest remedial measures to reduce the negative consequences of crash outcomes. To that extent, the current dissertation contributes to the severity analysis with a specific focus on driver injury severity analysis. Road safety researchers have employed several statistical formulations for analyzing the relationship between injury severity and crash related factors. However, there are still several methodological and empirical gaps in safety literature. The specific emphasis of the current dissertation is to contribute substantially towards methodological gaps in the state of the art for driver injury severity analysis along six directions: (1) appropriate model framework, (2) underreporting issue in severity analysis, (3) exogenous factor homogeneity assumption (4) multiple dependent variables in severity analysis, (5) continuum of fatal crashes and (6) data pooling from multiple data sources. In the dissertation, several econometric models are formulated, estimated and validated to address the aforementioned methodological issues through five different empirical studies. The econometric models developed in the dissertation are estimated using police reported crash databases at the regional and the national level from different industrialized countries. Specifically, the dissertation research is undertaken employing General Estimates System and Fatality Analysis Reporting System of the United States and the Victoria crash database of Australia. In addition to making the aforementioned methodological contributions, the dissertation also makes a substantial empirical contribution to the existing safety literature. Specifically, several policy measures in terms of engineering, enforcement, education and emergency response strategies are identified to improve safety situation and to reduce road crash related fatalities." --