Interaction of transport and land use: framework for an integrated urban model

The growing general concern on limited resources (e.g. space) have led to the conviction that policy makers, that deal with urban development, need to consider their choices carefully, with respect to the effects on urban development on the long term. Models that describe the urban development provide quantitative insight in the effects of possible government policy and are a useful tool for policy makers. In the last decade new generation computers have drastically increased data handling capacity and graphically possibilities, allowing much more detail in spatial modeling. This has resulted in research efforts into urban models to quantify the effects of spatial policy. The research, which will be described in this paper, aims for an integrated approach to spatial modeling with special attention on the influence of transport networks and the role of the government. Main objective of the research is the development and application of an urban model to quantify the effects of planning policies on the spatial development. At micro level this urban model simulates the reaction of actors to changes in the urban system: the development or renewal of new urban areas and new infrastructure. These changes are imposed on the urban system by government and developers on macro and meso level. The paper will present the theoretical framework for the proposed urban model and the objectives of the research. This will be complemented with a description of spatial planning issues in the Netherlands. In the proposed urban model the spatial system (urban region) is represented by multiple linked sub-systems. Individual sub-systems are: the housing market, public facilities, the market for business real estate and the transport system. Each sub-system is represented as a market with a supply and demand side. The government and (project) developers define the supply side through spatial policy and investments. On the demand side, agents (households and companies) react at changes in these subsystems. These reactions expose themselves as individual decisions whether to move to other dwellings or to relocate businesses. Development and application of modeling techniques for the choice behavior of households and companies as entities, are main objectives in the research. The urban markets have a strong coherence for the spatial relations of each agent. Quantification of these relationships, by analyzing the transportation facilities, is important in analyzing the choice behavior of households and companies. This is why the transport system plays a central role in the urban model. For each subsystem an appropriate modeling technique has been selected, based on an exploration of available approaches in the literature and other research programs. Efforts are under way to collect and operationalize the extensive data necessary for the modeling task.

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/87286/201

Sustainable operations of industrial symbiosis: an enterprise input-output model integrated by agent-based simulation

Industrial symbiosis (IS) is a key for implementing circular economy. Through IS, wastes produced by one company are used as inputs by other companies. The operations of IS suffers from uncertainty barriers since wastes are not produced upon demand but emerge as secondary outputs. Such an uncertainty, triggered by waste supply-demand quantity mismatch, influences IS business dynamics. Accordingly, companies have difficulty to foresee potential costs and benefits of implementing IS. The paper adopts an enterprise input-output model providing a cost–benefit analysis of IS integrated to an agent-based model to simulate how companies share the total economic benefits stemming from IS. The proposed model allows to explore the space of cooperation, defined as the operationally favourable conditions to operate IS in an economically win-win manner. This approach, as a decision-support tool, allows the user to understand whether the IS relationship is created and how should the cost-sharing policy be. The proposed model is applied to a numerical example. Findings show that cost-sharing strategies are dramatically affected by waste supply-demand mismatch and by the relationship between saved and additional costs to run IS. Apart from methodological and theoretical contributions, the paper proposes managerial and practical implications for business strategy development in IS

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach

Agent-based model of broadband adoption in unserved and underserved areas

In the last two decades, demand for broadband internet has far outpaced its availability. The Federal Communications Commission’s (FCC) 2020 Broadband Deployment report suggests that at least 22 million Americans living in rural areas lack access to broadband internet. With the COVID-19 pandemic affecting normal life, there is an overwhelming need to enable unserved and underserved communities to adapt to the “new normal”. To address this challenge, federal and state agencies are funding internet service providers (ISPs) to deploy infrastructure in rural communities. However, policymakers and ISPs need open-source tools to predict take-rates of broadband service and formulate effective strategies to increase the adoption of high-speed internet. We propose using an agent-based model grounded in “The Theory of Planned Behavior” -- a long-established behavioral theory that explains the consumer’s decision-making process. The model simulates residential broadband adoption by capturing the interaction of a broadband service’s attributes with consumer preferences. We demonstrate the model’s performance, present a case study of an unserved area, and perform a sensitivity analysis. The major findings support the appropriateness of using theoretically based agent-based models to predict take-rates of broadband service. We also find that the take-rates are highly influenced by presence of existing internet users in the area as well as affordable or subsidized prices. In the future, this model can be extended to study the impact of online education, telecommuting, telemedicine, and precision agriculture on a rural economy. This type of simulation can guide evidence-based decision-making for infrastructure investment based on demand as well as influence the design of market subsidies that aim to reduce the digital divide --Abstract, page iii

An agent-based simulation model for business reopenings in New Orleans post Hurricane Katrina

The empirical study of business responses to disasters is relatively scarce, despite that they are a fundamental part of the cities, providing services, jobs and, taxes that are essential for urban sustainability. This dissertation develops an agent-based simulation model to represent and understand the businesses reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The objectives are two-fold: 1) To identify the main reopening predictors involved and estimate their relative importance through time, using an empirical data set collected from another study; 2) To represent the business reopening process through a computer simulation model, using the parameters derived from the first objective. The results show that businesses located in flooded areas had lower reopening probabilities, however the effect was significant only in the first nine months after the disaster. Larger businesses had better reopening probabilities than smaller ones, although this variable stopped being significant after six months. Variables associated with higher social vulnerability, such as percent non-white population and percent population under 18, had a negative effect on the business reopening probabilities at different points of time. The influence of neighboring firms using 1-km buffer was found significantly positive only immediately after the disaster; it became significantly negative one year after the disaster. The simulation model developed proved to mimic the reopening process at a suitable level. The model was used to simulate two scenarios: 1) First, the flood depth was reduced by 1 meter as a way to represent the implementation of measures designed to increase the buildings and infrastructure resistance to floods. The simulation results indicate that there are specific areas that would obtain greater benefit from these measures, however ten months after the disaster the effect of the measures tends to diminish. 2) Second, the spatial effects of aids were simulated by making a limited number of businesses in specific locations totally resilient to the disaster. The results indicate that the beneficial effect is influenced by variables such as business density and socio-economic conditions of the area. The positive effect is perceivable until four months after the disaster, after this point it diminishes

Agent-based modelling and inundation prediction to enable the identification of businesses affected by flooding

Flooding continues to cause significant disruption to individuals, organisations and communities in many parts of the world. In terms of the impact on businesses in the United Kingdom (UK), flooding is responsible for the loss of millions of pounds to the economy. As part of a UK Engineering and Physical Sciences Research Council funded project on flood risk management, SESAME, research is being carried out with the aim of improving business response to and preparedness for flood events. To achieve this aim, one strand of the research is focused on establishing how agent-based modelling and simulation can be used to evaluate and improve business continuity. This paper reports on the development of the virtual geographic environment (VGE) component of an agent-based model and how this has been combined with inundation prediction to enable the identification of businesses affected by flooding in any urban area of the UK. The VGE has been developed to use layers from Ordnance Survey’s MasterMap, namely the Topography Layer, Integrated Transport Network Layer and Address Layer 2. Coupling the VGE with inundation prediction provides credibility in modelling flood events in any area of the UK. An initial case study is presented focusing on the Lower Don Valley region of Sheffield leading to the identification of businesses impacted by flooding based on a predicted inundation. Further work will focus on the development of agents to model and simulate businesses during and in the aftermath of flood events such that changes in their behaviours can be investigated leading to improved operational response and business continuity

Modelling an End to End Supply Chain system Using Simulation

Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

Automatic ontology mapping for agent communication

Agent communication languages such as ACL and KQML provide a standard for agent communication. These languages enable an agent to specify the intention and the content of a message as well as the protocol, the language, and the ontology that are used. For the protocol and the language some standards are available and should be known by the communicating agents. The ontology used in a communication depends on the subject of the communication. Since the number of subjects is almost infinite and since the concepts used for a subject can be described by different ontologies, the development of generally accepted standards will take a long time. This lack of standardization, which hampers communication and collaboration between agents, is known as the interoperability problem. To overcome the interoperability problem, agents must be able to establish a mapping between their ontologies. This paper investigates a new approach to the interoperability problem. The proposed approach requires neither a correspondence between concepts used in the ontologies nor a correspondence between the structure of the ontologies. It only requires that some instances of the subject about which the agents try to communicate are known by both agents.economics of technology ;