A Systematic Approach to Identifying Traffic Safety Needs and Intervention Programs for Indiana: Volume I—Research Report

This report presents the results of JTRP Project: “A Systematic Approach of Identifying Safety Intervention Programs for Indiana (SNIP2),” which aimed to develop SNIP2 to support identification of roads that have excessive crashes of the types defined by the user. In addition, this tool is capable of selecting the best combination of high-crash roads and relevant safety interventions that maximizes the safety benefits and keeps the total cost within the budget and other user-defined constraints. Unlike other studies considering the implementation time of safety projects, the optimization objective of SNIP2 is to identify an optimal combination of countermeasures renewable within a long time horizon. This simplification is accomplished by representing the projects through their annualized costs and benefits. It allows consideration of many projects for large road networks and it makes the SNIP2 suitable for identification of safety focus areas in strategic safety plans. The SNIP optimizer – a heuristic approximation of a large-size mixed integer knapsack problem based on a greedy search was extensively tested and evaluated. It was found producing optimal or near-optimal solutions in a sufficiently short time. Another research result is a comprehensive catalog of countermeasures for Indiana – a list of countermeasure names, road and crash conditions for the countermeasure relevance, corresponding crash modification factors, and countermeasure costs. The SNIP2 is computer software developed with close collaboration with the INDOT future users. It includes an updated crash and state road database. A user’s manual describes on the necessary details of the software and various aspects of its use. Two example studies are also included in the manual to illustrate its use and to better presents the SNIP2 features

Technology and Management for Sustainable Buildings and Infrastructures

A total of 30 articles have been published in this special issue, and it consists of 27 research papers, 2 technical notes, and 1 review paper. A total of 104 authors from 9 countries including Korea, Spain, Taiwan, USA, Finland, China, Slovenia, the Netherlands, and Germany participated in writing and submitting very excellent papers that were finally published after the review process had been conducted according to very strict standards. Among the published papers, 13 papers directly addressed words such as sustainable, life cycle assessment (LCA) and CO2, and 17 papers indirectly dealt with energy and CO2 reduction effects. Among the published papers, there are 6 papers dealing with construction technology, but a majority, 24 papers deal with management techniques. The authors of the published papers used various analysis techniques to obtain the suggested solutions for each topic. Listed by key techniques, various techniques such as Analytic Hierarchy Process (AHP), the Taguchi method, machine learning including Artificial Neural Networks (ANNs), Life Cycle Assessment (LCA), regression analysis, Strength–Weakness–Opportunity–Threat (SWOT), system dynamics, simulation and modeling, Building Information Model (BIM) with schedule, and graph and data analysis after experiments and observations are identified

Dagstuhl News January - December 2005

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Knowledge-centric autonomic systems

Autonomic computing revolutionised the commonplace understanding of proactiveness in the digital world by introducing self-managing systems. Built on top of IBM’s structural and functional recommendations for implementing intelligent control, autonomic systems are meant to pursue high level goals, while adequately responding to changes in the environment, with a minimum amount of human intervention. One of the lead challenges related to implementing this type of behaviour in practical situations stems from the way autonomic systems manage their inner representation of the world. Specifically, all the components involved in the control loop have shared access to the system’s knowledge, which, for a seamless cooperation, needs to be kept consistent at all times.A possible solution lies with another popular technology of the 21st century, the Semantic Web,and the knowledge representation media it fosters, ontologies. These formal yet flexible descriptions of the problem domain are equipped with reasoners, inference tools that, among other functions, check knowledge consistency. The immediate application of reasoners in an autonomic context is to ensure that all components share and operate on a logically correct and coherent “view” of the world. At the same time, ontology change management is a difficult task to complete with semantic technologies alone, especially if little to no human supervision is available. This invites the idea of delegating change management to an autonomic manager, as the intelligent control loop it implements is engineered specifically for that purpose.Despite the inherent compatibility between autonomic computing and semantic technologies,their integration is non-trivial and insufficiently investigated in the literature. This gap represents the main motivation for this thesis. Moreover, existing attempts at provisioning autonomic architectures with semantic engines represent bespoke solutions for specific problems (load balancing in autonomic networking, deconflicting high level policies, informing the process of correlating diverse enterprise data are just a few examples). The main drawback of these efforts is that they only provide limited scope for reuse and cross-domain analysis (design guidelines, useful architectural models that would scale well across different applications and modular components that could be integrated in other systems seem to be poorly represented). This work proposes KAS (Knowledge-centric Autonomic System), a hybrid architecture combining semantic tools such as: • an ontology to capture domain knowledge,• a reasoner to maintain domain knowledge consistent as well as infer new knowledge, • a semantic querying engine,• a tool for semantic annotation analysis with a customised autonomic control loop featuring: • a novel algorithm for extracting knowledge authored by the domain expert, • “software sensors” to monitor user requests and environment changes, • a new algorithm for analysing the monitored changes, matching them against known patterns and producing plans for taking the necessary actions, • “software effectors” to implement the planned changes and modify the ontology accordingly. The purpose of KAS is to act as a blueprint for the implementation of autonomic systems harvesting semantic power to improve self-management. To this end, two KAS instances were built and deployed in two different problem domains, namely self-adaptive document rendering and autonomic decision2support for career management. The former case study is intended as a desktop application, whereas the latter is a large scale, web-based system built to capture and manage knowledge sourced by an entire (relevant) community. The two problems are representative for their own application classes –namely desktop tools required to respond in real time and, respectively, online decision support platforms expected to process large volumes of data undergoing continuous transformation – therefore, they were selected to demonstrate the cross-domain applicability (that state of the art approaches tend to lack) of the proposed architecture. Moreover, analysing KAS behaviour in these two applications enabled the distillation of design guidelines and of lessons learnt from practical implementation experience while building on and adapting state of the art tools and methodologies from both fields.KAS is described and analysed from design through to implementation. The design is evaluated using ATAM (Architecture Trade off Analysis Method) whereas the performance of the two practical realisations is measured both globally as well as deconstructed in an attempt to isolate the impact of each autonomic and semantic component. This last type of evaluation employs state of the art metrics for each of the two domains. The experimental findings show that both instances of the proposed hybrid architecture successfully meet the prescribed high-level goals and that the semantic components have a positive influence on the system’s autonomic behaviour

Multi-Criteria Decision Making in Complex Decision Environments

In the future, many decisions will either be fully automated or supported by autonomous system. Consequently, it is of high importance that we understand how to integrate human preferences correctly. This dissertation dives into the research field of multi-criteria decision making and investigates the satellite image acquisition scheduling problem and the unmanned aerial vehicle routing problem to further the research on a priori preference integration frameworks. The work will aid in the transition towards autonomous decision making in complex decision environments. A discussion on the future of pairwise and setwise preference articulation methods is also undertaken. "Simply put, a direct consequence of the improved decision-making methods is,that bad decisions more clearly will stand out as what they are - bad decisions.

Civil and Military Airworthiness

Effective safety management has always been a key objective for the broader airworthiness sector. This book is focused on safety themes with implications on airworthiness management. It offers a diverse set of analyses on aircraft maintenance accidents, empirical and systematic investigations on important continuing airworthiness matters and research studies on methodologies for the risk and safety assessment in continuing and initial airworthiness. Overall, this collection of research and review papers is a valuable addition to the published literature, useful for the community of aviation professionals and researchers

An Evaluation of Railroad Safety

A report by the Office of Technology Assessment (OTA) evaluating the efforts made to improve the safety of the nation's railroads. The report gives "a comprehensive and systematic review of railroad safety" and assists "in current and future legislative deliberations on railroad safety" (p. v")

Advanced risk and maintenance modelling in LNG carrier operations

High demand of Liquefied Natural Gas (LNG) in recent time requires LNG carriers in more frequent operations in order to meet customers' needs. To ensure that the LNG carriers are always reliable in service, it has become necessary to adopt various advanced modelling techniques such as Genetic Algorithm (GA), fuzzy logic and Evidential Reasoning (ER) for risk/safety assessment and maintenance modelling of LNG carrier operations. These advanced computational techniques can help to overcome challenges posed by uncertainties associated with the LNG carrier operations. Their usefulness is demonstrated using case studies in this research. Firstly, two major hazards of LNG carrier operations such as "failure of LNG containment system" and "LNG spill from transfer arm" are identified and estimated as high risk ones using a risk matrix technique and expert judgement. The causes (failure modeslbasic events) of these high risk hazards are analysed using a Fault Tree Analysis (FTA). The failure logics of their failure modes are established and Boolean algebra is applied to facilitate the evaluation of the failure probabilities and frequencies. Secondly, a GA model is developed to improve the safety levels of the LNG containment system and transfer arm, to minimise their maintenance costs and to realise optimal resource management. The GA is used to optimise a risk model that is developed with exponential distribution and parameters such as failure frequencies, unit costs of maintenance and new maintenance costs of the LNG containment system and transfer arm. Thirdly, the uncertainties of some parameters in the GA model such as unit costs of maintenance are subdued using the strength of Fuzzy Rule Base (FRB) in combination with GA. 125 fuzzy rules of LNG carrier system maintenance cost are developed, which makes it possible to facilitate the evaluation of maintenance cost in any specific LNG risk-based operation. The outcomes of unit costs of maintenance are used in the GA based risk model to update the optimal management of maintenance cost. Finally, the uncertainties of failure modes of the LNG containment system and transfer arm are investigated and treated based on the Formal Safety Assessment (FSA) principle using a Fuzzy ER (FER) approach. The fuzzy logic is used to estimate the safety/risk levels of those failure modes while the ER is used to synthesise them to facilitate the estimation of safety/risk levels of the top events. Risk Control Options (RCOs) are developed to manage high level risks. The costs for each of the RCOs are estimated and synthesised using ER, which facilitated the investigation of the best RCOs in risk-based decision making. There is no doubt that the methodologies proposed possess significant potential for use in improving safety and maintenance of LNG carrier operations based on the verifications of their corresponding test cases. Accordingly, the developed models can be integrated to formulate a platform to facilitate risk assessment and maintenance management of LNG carrier systems in situations where traditional techniques cannot be applied with confidence