Yilin Huang

A dynamic data-driven approach for rail transport system simulation

Public rail transport systems concern infrastructure and control strategies with long life spans. While many rail system simulations aim at planning and design, this paper proposes a dynamic data-driven approach to improve the adaptability of the model, hence promoting an extended use of the simulation model. In the proposed approach, the simulation study uses real data streams for automatic model calibration at run-time. For situations that cannot be automated, expert interference can be supported by interactive processes. Different model calibration schemes can be applied to several replications simultaneously to assess the schemes and to determine the parameter values that best match the most recent situation. The model can be fed with data derived from different scenarios, from decision variations or from real-time measurements to accomplish accurate and automated model calibration. This provides a foundation for the use of simulation for railway controller training tools and real-time rail monitoring systems.

From data to simulation models: component-based model generation with a data-driven approach

Model building is time-consuming and requires expertise in different areas. In this paper, we propose a data-driven approach for automatic model generation using pre-built and validated model components (or building blocks). We view this approach as an automated reuse of model components. Issues such as modularity and composability of model components are addressed. Models can be generated by automatically selecting, structuring and configuring the model components. The formulated rules can be structural and behavioral, by which a relational representation of the desired model composite structure is incrementally constructed. An example of generating a rail network model is given to demonstrate the steps.

Libros-II: railway modeling with devs

The increasing complexity of railway systems and the high costs incurred by design and operational errors make modeling and simulation a popular methodology in the domain of railway transportation. To successfully support detailed design and operation, a microscopic rail network model is often deemed not only suitable but also mandatory. However, the simulation of large-scale microscopic models is computationally intensive, making it unsuitable for real-time applications. In this paper, a railway simulation library, LIBROS-II, is introduced which offers high performance rail simulation at the microscopic level. The library is specified with the DEVS formalism. Its major components and their specifications are presented. Its performance is assessed through a simple example and contrasted with a typical model using a continuous modeling abstraction of train movement. The result shows that with comparable model detail and accuracy the LIBROS-II model yields a higher performance than the model using differential equations.

Social networking for Smart Grid users

Emerging smart grids have promising potentials to make energy management more efficient than currently possible in todays power grids. Integration of small scale renewables, distributed charging of electrical vehicles and virtual power stations are some of the technological innovations made possible by smart grids. Besides these technological aspects, smart grids also have a clear social component: consumers and small producers can together form energy communities. Such communities can be based on shared geographical location. They can also form based on shared values. This paper assumes that online social networks can be used to form virtual energy communities with shared values such as sustainability and social cohesion, sharing energy. We present an exploratory study on the creation and evolution of Smart Grid Social Networks using an agent-based simulation model. Initial simulation experiments show that in this context a large community with members that are occasionally active forms a better predictor for successful energy communities than a smaller community of very active users.

Towards automated model calibration and validation in rail transit simulation

The benefit of modeling and simulation in rail transit operations has been demonstrated in various studies. However, the complex dynamics involved and the ever-changing environment in which rail systems evolve expose the limits of classical simulation. Changing environmental conditions and second order dynamics challenge the validity of the models and seriously reduce model (re-)usability. This paper discusses the potential benefits and requirements of dynamic data-driven simulation in rail systems. The emphasis is placed on automated model reconfiguration, calibration, and validation through the use of data analysis methods. The rationale and requirements are discussed and a process model for data driven calibration and validation is proposed.

YouPower: An open source platform for community-oriented smart grid user engagement

This paper presents YouPower, an open source platform designed to make people more aware of their energy consumption and encourage sustainable consumption with local communities. The platform is designed iteratively in collaboration with users in the Swedish and Italian test sites of the project to improve the design and increase active user participation. The community-oriented design is composed of parts that link energy data to energy actions, provide comparisons at different levels, generate dynamic time-of-use signals, offer energy conservation suggestions, and support social sharing. The goal is to bridge peoples attitude-behavior gap in energy consumption and to facilitate the behavior change process towards sustainable energy consumption that is implementable in peoples daily life. Preliminary results show that community-oriented energy intervention has the potential to improve user engagement significantly.

Graph transformation based simulation model generation

The graph transformation based method presented in this paper can automatically generate simulation models assuming that the models are intended for a certain domain. The method differs from other methods in that: the data used for model generation does not contain specifications of the model structures to be generated; the generated simulation models have structures that are dynamically constructed during the model generation process. Existing data typically has quality issues and does not contain all types of information, particularly in terms of model structure, that are required for modelling. To solve the problem, transformation rules are designed to infer the required model selection and structure information from the data. The rules are specified on meta-models of the original data structure, of intermediate structures and of the simulation model. Graph patterns, pattern composites and graph pattern matching algorithms are used to define and identify potential model components. Model composite structures are represented by hypergraphs according to which simulation models are generated using model components as building blocks. The method has been applied practically in the domain of light-rail transport.

Embedding internet-of-things in large-scale socio-technical systems: A community-oriented design in future smart grids

In traditional engineering, technologies are viewed as the core of the engineering design, in a physical world with a large number of diverse technological artefacts. The real world, however, also includes a huge number of social components—people, communities, institutions, regulations and everything that exists in the human mind—that have shaped and been shaped by the technological components. Smart urban ecosystems are examples of large-scale Socio-Technical Systems (STS) that rely on technologies, in particular on the Internet-of-Things (IoT), within a complex social context where the technologies are embedded. Designing applications that embed both social complexity and IoT in large-scale STS requires a Socio-Technical (ST) approach, which has not yet entered the mainstream of design practice. This chapter reviews the literature and presents our experience of adopting an ST approach to the design of a community-oriented smart grid application. It discusses the challenges, process and outcomes of this apporach, and provides a set of lessons learned derived from this experience that are also deemed relevant to the design of other smart urban ecosystems.

Component-based light-rail modeling in discrete event systems specification DEVS

Rail modeling and simulation is an effective decision support instrument for planning and designing complex rail infrastructures and operations. To successfully support these activities at a large scale, the simulation model should be sufficiently detailed and yet be computationally efficient. This poses a set of challenges pertaining to the design of the models. In this paper, we propose a component-based light-rail modeling and simulation library in the discrete event system specification (DEVS) formalism. The proposed library is described in detail and is shown to be efficient and scalable. We conclude the paper by offering a set of good design principles derived from this experience, which are also relevant to other types of large-scale infrastructure system simulation model design.