Author: Anthony Cyril Mbakwe
Publisher:
ISBN: 9781124756882
Category :
Languages : en
Pages : 213

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Author: Yaxin Bi
Publisher: Springer Nature
ISBN: 3030295133
Category : Technology & Engineering
Languages : en
Pages : 1312

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The book presents a remarkable collection of chapters covering a wide range of topics in the areas of intelligent systems and artificial intelligence, and their real-world applications. It gathers the proceedings of the Intelligent Systems Conference 2019, which attracted a total of 546 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process, after which 190 were selected for inclusion in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle a host of problems more effectively. This branching out of computational intelligence in several directions and use of intelligent systems in everyday applications have created the need for an international conference as a venue for reporting on the latest innovations and trends. This book collects both theory and application based chapters on virtually all aspects of artificial intelligence; presenting state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision for future research, it represents a unique and valuable asset.

Author: Dan Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :

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The highway network, as a critical infrastructure in our daily life, is an important component of the public transportation system. In the face of a continuously increasing highway accident rate, highway safety is certainly one of the greatest concerns for transportation departments worldwide. To better improve the current situation, several studies have been carried out on preventing the occurrence of highway accidents or reducing the severity level of highway accidents. The principal causes of highway accidents can be summarized into four categories: external environment conditions, operational environment conditions, driver conditions and vehicle conditions. This research proposes a representational Bayesian Networks (BNs) model which can predict and continuously update the likelihood of highway accidents, by considering a set of well-defined variables belonging to these principal causes, also named risk factors, which directly or indirectly contribute to the frequency and severity of highway accidents. This accident predictive BNs model is developed using accidents data from Transport Canada's National Collision Database (NCDB) during the period of 1999 to 2010. Model testing is provided with a case study of Highway #63 site, which is from 6 km southwest of Radway to 16 km north of Fort Mackay in north Alberta, Canada. The validity of this BNs model is established by comparing prediction results with relevant historical records. The positive outcome of this exercise presents great potential of the proposed model to real life applications. Furthermore, this predictive BNs accident model can be integrated with a Safety Instrumented System (SIS). This integration would assist in predicting the real-time probability of accident and would also help activating risk management actions in a timely fashion. This research also simulates 10 scenarios with different specific states of variables to predict the probability of fatal accident occurrence, which demonstrates how the BNs model is integrated with SIS. The major objective of this research is to introduce the predictive accident BNs model with the capabilities of inferring the dependent causal relations and predicting the probability of highway accidents. It is also believed that this BNs model would help developing efficient and effective transportation risk management strategies.

Author: Patrick Manu
Publisher: Routledge
ISBN: 0429848536
Category : Architecture
Languages : en
Pages : 266

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The global construction sector is infamous for high levels of injuries, accidents and fatalities, and poor health and well-being of its workforce. While this record appears in both developed and developing countries, the situation is worse in developing countries, where major spending on infrastructure development is expected. There is an urgent need to improve construction health and safety (H&S) in developing countries. The improvement calls for the development of context-specific solutions underpinned by research into challenges and related solutions. This edited volume advances the current understanding of construction H&S in developing countries by revealing context-specific issues and challenges that have hitherto not been well explored in the literature, and applying emergent H&S management approaches and practices in developing countries. Coverage includes countries from the regions of sub-Saharan Africa, Latin America, Asia and Europe. This book, which is the first compendium of research into construction H&S issues in developing countries, adds considerable insight into the field and presents innovative solutions to help address poor H&S in construction in developing nations. It is a must read for all construction professionals, researchers and practitioners interested in construction and occupational H&S, safety management, engineering management and development studies.

Author: Hoong Chor Chin
Publisher: Nova Science Publishers
ISBN: 9781606922705
Category : Bayesian statistical decision theory
Languages : en
Pages : 0

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Background: In the study of traffic system safety, statistical models have been broadly applied to establish the relationships between the traffic crash occurrence and various risk factors. Most of the existing methods, such as the generalised linear regression models, assume that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation. Hence, the residuals from the models exhibit independence. Problem: However, this "independence" assumption may often not hold true since multilevel data structures exist extensively because of the data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. Method: Following a literature review of crash prediction models, this book proposes a 5 T-level hierarchy, viz. (Geographic region level -- Traffic site level -- Traffic crash level -- Driver-vehicle unit level -- Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To model properly the potential between-group heterogeneity due to the multilevel data structure, a framework of hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is employed. Bayesian inference using Markov chain Monte Carlo algorithm is developed to calibrate the proposed hierarchical models. Two Bayesian measures, viz. the Deviance Information Criterion and Cross-Validation Predictive Densities, are adapted to establish the model suitability. Illustrations: The proposed method is illustrated using two case studies in Singapore: 1) a crash-frequency prediction model which takes into account Traffic site level and Time level; 2) a crash-severity prediction model which takes into account Traffic crash level and Driver-vehicle unit level. Conclusion: Comparing the predictive abilities of the proposed models against those of traditional methods, the study demonstrates the importance of accounting for the within-group correlations and illustrates the flexibilities and effectiveness of the Bayesian hierarchical approach in modelling multilevel structure of traffic safety data.

Author: Thobias M. Sando
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages :

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Author: Michael David Pawlovich
Publisher:
ISBN:
Category :
Languages : en
Pages : 622

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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: Mohammad Heydari
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Saeid Pourroostaei Ardakani
Publisher: Springer Nature
ISBN: 9819966205
Category : Computers
Languages : en
Pages : 197

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This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured.

Author: Md. Al-Amin Baksh
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 356

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