Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation

Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO2 traffic emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active field of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approach and a possibility to reuse and integrate existing models. In order to solve this problem, a framework for electric vehicle modeling is presented, which provides strong capabilities for detailed electricity demand modeling. It is built on an agent-based travel demand and traffic simulation. A case study for the city of Zurich is presented, which highlights the capabilities of the framework to uncover possible bottlenecks in the electricity network and detailed fleet simulation for CO2 emission calculations, and thus its power to support policy makers in taking decisions.

Landslides

Landslide research is an interdisciplinary field that primarily encompasses scientists from geomorphology, engineering geology, and geotechnical engineering in collaboration with researchers from such fields as geodesy, hydrogeology, geophysics, and many others. This chapter is intended as a resource for researchers interested in landslide engineering and landslide science to acquire a summarized review of research subjects and the state-of-the-art literature. A wide range of landslide topics are presented in the following sections: landslide mapping, landslide investigation, landslide monitoring, landslide hazard and risk assessment, and landslide stabilization and remediation measures. The results of landslide studies have practical applications to society via the avoidance, prevention, and mitigation of landslide hazards and risks. Landslide avoidance and prevention are the primary interests for land-use policies based on landslide mapping, followed by the prediction of landslide processes and their consequences. Landslide mitigation includes the development of engineering technologies for landslide investigation, monitoring, and remediation.

The Integrative Time-Dependent Modeling of the Reliability and Failure of the Causes of Drivers' Error Leading to Road Accidents

Nowadays, the human error is usually identified as the conclusive cause of investigations in road accidents. The human although is the person in control of vehicle until the moment of crash but it has to be understood that the human is under continued impact by various factors including road environment, vehicle and human's state, abilities and conduct. The current advances in design of vehicle and roads have been intended to provide drivers with extra comfort with less physical and mental efforts, whereas the fatigue imposed on driver is just being transformed from over-load fatigue to under-load fatigue and boredom. A representational model to illustrate the relationships between design and condition of vehicle and road as well as driver's condition and state on fatigue and the human error leading to accidents has been developed. Thereafter, the stochastic mathematical models based on time-dependent failure rates were developed to make prediction on the road transportation reliability and failure probabilities due to each cause (vehicle, road environment, human due to fatigue, and human due to non fatigue factors). Furthermore, the supportive assessment methodology and models to assess and predict the failure rates of driver due to each category of causes were developed and proposed.

General Approach to Risk Analysis

Broadly accepted methodology that is implemented in the oil industry when dealing with risks includes as the first step the identification of possible hazards. That is done by gathering information about degree of risk according to working procedures, processes, and individuals involved in the operation of the process. That is the first step in risk management, an iterative process that must lead to the use of proper measurements in the way of protecting people, facilities and environment. The analysis is done based on the combination of probability and severity of undesirable events, and the final consequences. Explanation of basic terms, their interdependence, dilemmas, and methods of risk analysis are introduced. Each method is shortly described with main anteriority and shortcomings. Differences between quantitative methods, qualitative methods, and hybrid methods (the combination of qualitative-quantitative or semi-quantitative methods) are elaborated. The impact, occurrence, and the consequences are at the end compared to risk acceptance criteria concept. The ALARP (As Low as Reasonably Practicable) framework is explained with some observation on the quality and acceptance in petroleum industry. Finally, the human impact on the risk and consequences is analyzed.

Assessing Human Reliability Behaviour from Use of Technology for Ships Navigating within Coastal Water

The traditional approach to the study of human factors in the maritime field involves the analysis of accidents without considering human factor reliability analysis. The main approaches being used to analyze human errors are statistical approach and probability theory approach. Another suitable approach to the study of human factors in the maritime industry is the quasi-experimental field study where variations in performance (for example attention) can be observed as a function of natural variations in performance shaping factors. This chapter analyzes result of modelling for human error and human reliability emanating from the use of technology on board ship navigation in coastal water areas by using qualitative and quantitative tools. Accident reports from marine department are used as empirical material for quantitative analysis. The literature on safety is based on common themes of accidents, the influence of human error resulting from technology usage design, accident reports from MAIB, and interventions information are used for qualitative assessment. Human reliability assessment involves analysis of accidents in waterways emanating from human-technology factors. The chapter reports enhancement requirement of the methodological issues with previous research study, monitoring, and deduces recommendations for technology modification of the human factors necessary to improve maritime safety performance. The result presented can contribute to rule making and safety management leading to the development of guidelines and standards for human reliability risk management for ships navigating within inland and coastal waters.

Decision Support Model for Fire Insurance Risk Analysis in a Petrochemical Case Study

A decision support system was researched and applied to a case study in the petrochemical industry. The participants were an insurance company underwriting the policies of oil and gas refineries located in a major oil producing nation. The Chemical Process Quantitative Risk Analysis methodology was applied as a framework to implement uncertainty quantification and risk analysis using a specialized commercial DSS software product. A gas vapor explosion was simulated at an oil refinery, to predict the fire and radiation damage. Costs and risks were entered into the model based on historical data. Loss estimates were generated for equipment and buildings located various distances (pressures) from the explosion origin. Overall, the DSS model predicted an expected loss of over $14,000,000 USD for equipment located in the 50 meter explosion radius, which represented a loss ratio of almost 52%. The losses predicted from the DSS model were comparable to the literature and to experiences of the case study company. The margin of error from the DSS model was less than ±5% which made it very reliable according to benchmarks.

Cloud Computing for Global Software Development

The cloud computing paradigm offers an innovative and promising vision concerning Information and Communications Technology. Actually, it provides the possibility of improving IT systems management and is changing the way in which hardware and software are designed and purchased. This paper introduces challenges in Global Software Development (GSD) and application of cloud computing platforms as a solution to some problems. Even though cloud computing provides compelling benefits and cost-effective options for GSD, new risks and difficulties must be taken into account. Thus, the paper presents a study about the risk issues involved in cloud computing. It highlights the different types of risks and how their existence can affect GSD. It also proposes a new risk management process model. The risk model employs new processes for risk analysis and assessment. Its aim is to analyse cloud risks quantitatively and, consequently, prioritise them according to their impact on different GSD objectives.

Communication Networks to Connect Moving Vehicles to Transportation Systems to Infrastructure

Communication in transportation systems not only involves the communication inside a vehicle, train, or airplane but it also includes the transfer of data to and from the transportation system or between devices belonging to that system. This will be done using different types of wireless communication. Therefore in this chapter, first, the fundamentals of mobile communication networks are shortly described. Thereafter, possible candidate networks are discussed. Their suitability for a certain transportation system can be evaluated taking into consideration the system's requirements. Among the most prominent are the influence of speed and mobility, data rate and bit error rate constraints, reliability of the system and on-going connections. As in most of the cases, there will be no single best wireless communication network to fulfil all requirements, and in this chapter also hybrid networks are discussed. These are networks consisting of different (wireless) access networks. The devices may use the best suited network for a given situation but also change to another network while continuing the on-going connection or data transfer. Here the design of the handover or relocation plays a critical role as well as localization.

Risk Evaluation in the Insurance Company Using REFII Model

A business case describes a problem present in all insurance companies: portfolio risk evaluation. Such analysis deals with determining the risk level as well as main risk factors. In the specific case, an insurance company is faced with market share growth and profit decline. Discovered knowledge about the level of risk and main risk factors was not used to increase premium for the riskiest portfolio segments due to a specific market situation, which could lead to loss of clients in the long run. Instead, additional analysis was conducted using data mining methods resulting in a solution, which stopped further profit decline and lowered the risk level for the riskiest portfolio segments. The central role for the unexpected revealed knowledge in the chapter acts as the REFII model. The REFII model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series.

The Challenges of Obtaining Credible Data for Transportation Security Modeling

The National Transportation Security Center of Excellence (NTSCOE) was established in August 2007 to develop new approaches to defend, protect, and increase the resilience of the nation's multi-modal transportation infrastructure, and to create education and training programs for transportation security. The Center for Transportation Safety, Security, and Risk (CTSSR) at Rutgers University, an NTSCOE institution, developed models that address multi-modal resilience of freight and transit transportation networks. Data collection processes for each project presented significant hurdles for the research team in developing credible and accurate modeling tools. For any given data need, the potential exists for data gaps, collection, and processing errors, publication and use restrictions, and the need to obtain the most timely information. These challenges must be foreseen by researchers and practitioners in order to better accommodate potential restrictions on both data collection and dissemination while still providing users with a tool that improves decision making.