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The search for sustainable transportation strategies hinges on the availability of transportation modeling frameworks that are sensitive enough to reflect the impact of proposed strategies and policies, and to evaluate new and emerging ideas and applications designed to make the system more sustainable. Motivated by this urgent need, this doctoral study aims at utilizing agent-based modeling and simulation (ABMS) principles to develop advanced transportation modeling frameworks that can support sustainable transportation planning and applications. Within this broader context, the study focuses on two major case studies. The first study involves developing an agent-based transportation model of a university campus capable of accurately modeling the parking search process. A unique feature of the model is that it includes a sequential game-theoretic neo-additive capacity model to model parking behavior under uncertainty. The model accounts for drivers' psychological characteristics (i.e. optimistic and pessimistic attitudes) regarding parking availability in their most desirable lot, traits that are shown to play a major role in determining drivers' parking choices. To demonstrate the model's validity and potential applications, it is used to model north campus transportation system of University at Buffalo (UB), The State University of New York and to quantify the environmental cost of the parking search process, a cost that turns out to be substantial. The second case study considered in this dissertation focuses on evaluating the likely environmental benefits of an Intelligent Transportation Systems (ITS) that involves providing dynamic route guidance to travelers based on their lowest emissions or fuel consumption route. To do this, an integrated microscopic traffic-emissions model is developed by interfacing the Transportation ANalysis and SIMulation System (TRANSIMS) with the Motor Vehicle Emission Simulator (MOVES) emissions model developed by the Environmental Protection Agency (EPA). The integrated model is then used to evaluate the benefits of "green" routing in the Greater Buffalo-Niagara metropolitan area. Results indicate that green routing could result in significant reductions in emissions, but that this naturally comes at the expense of an increased travel time. Assuming a traffic stream of only passenger cars, green routing is shown to result in an almost 13% reduction in Carbon Monoxide (CO) emissions, and a corresponding 8% increase in travel time for the case study.
This instructional book showcases techniques to parameterise human agents in empirical agent-based models (ABM). In doing so, it provides a timely overview of key ABM methodologies and the most innovative approaches through a variety of empirical applications. It features cutting-edge research from leading academics and practitioners, and will provide a guide for characterising and parameterising human agents in empirical ABM. In order to facilitate learning, this text shares the valuable experiences of other modellers in particular modelling situations. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as topics related to sustainability. In a similar vein to the instruction found in a cookbook, this text provides the empirical modeller with a set of 'recipes' ready to be implemented. Agent-based modeling (ABM) is a powerful, simulation-modeling technique that has seen a dramatic increase in real-world applications in recent years. In ABM, a system is modeled as a collection of autonomous decision-making entities called “agents.” Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. ABM is increasingly used for simulating real-world systems, such as natural resource use, transportation, public health, and conflict. Decision makers increasingly demand support that covers a multitude of indicators that can be effectively addressed using ABM. This is especially the case in situations where human behavior is identified as a critical element. As a result, ABM will only continue its rapid growth. This is the first volume in a series of books that aims to contribute to a cultural change in the community of empirical agent-based modelling. This series will bring together representational experiences and solutions in empirical agent-based modelling. Creating a platform to exchange such experiences allows comparison of solutions and facilitates learning in the empirical agent-based modelling community. Ultimately, the community requires such exchange and learning to test approaches and, thereby, to develop a robust set of techniques within the domain of empirical agent-based modelling. Based on robust and defendable methods, agent-based modelling will become a critical tool for research agencies, decision making and decision supporting agencies, and funding agencies. This series will contribute to more robust and defendable empirical agent-based modelling.
Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
This will be the first textbook on the integration of food, energy and water systems (FEWS). In recent years, the world has seen a dramatic rise in interdisciplinary energy and environmental courses and degrees at the undergraduate and graduate levels. In the US for instance, the number and variety of such programs has increased significantly over the past decade, Simultaneously, national and international initiatives that integrate food, energy and water systems have been launched. This textbook provides a substantive introduction to the food-energy-water nexus suitable for use in higher level undergraduate and graduate level courses and for scholars moving into the field of nexus studies without a strong background in all three areas and the many aspects of nexus studies.
Innovative and smart mobility systems are expected to make transportation systems more sustainable, inclusive, and safe. Because of changing mobility paradigms, transport planning and design require different methodological approaches. Over twelve chapters, this book examines and analyzes Mobility as a Service (MaaS), travel behavior, traffic control, intelligent transportation system design, electric, connected, and automated vehicles, and much more.
Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.
This book introduces a planning support system called Strategic Spatial Plan Support System (SSP-SS) to visualize population growth and predict energy demand, land use, and waste discharge resulting from urbanization. By analyzing policy interactions between household agents, the book uses SSP-SS to visualize policy effects on urban areas during stages of growth and decline. Simulations are created based on these policy outcome assessments, taking into account the influences of energy and resource consumption on sustainable development in urban environments. The book is geared towards researchers, universities, and urban policy makers. The book begins by presenting a framework of urban growth simulation, and introducing SSP-SS. Then, household lifecycle and relocation models are employed for simulating policy impacts on urbanization, and investigating the impacts of spatial strategic planning. Several projects are assessed using agent-based modeling including shopping centre construction, day-care service for aging populations, and shelter accommodation capacities for earthquakes and other disasters. The final chapters discuss water and energy management, the environmental impacts of demand and consumption, and future recommendations for sustainable development and policy implementation. Introduces Strategic Spatial Plan Support System (SSP-SS) to visualize population growth and predict energy demand, land use, and waste discharge resulting from urbanization. Analyzes policy effects on urban areas during stages of growth and decline. Discusses the influences of water and gas consumption on environmental issues in urban areas for sustainable development.
The overall purpose of this evaluation was to understand the effect of the Federal Highway Administration’s (FHWA’s) Research and Technology Program activities on the implementation of agent-based approaches to transportation-related projects and activities. Agent-based modeling and simulation (ABMS) uses individual “agents,” typically drivers and agencies, to model changes in transportation networks and systems. Researchers and industry stakeholders view ABMS and the data-collection and validation processes that ABMS requires as a valuable, emerging practice that can be used to advance existing transportation-modeling and simulation techniques. ABMS can also be used for various transportation applications, including planning, operations, and safety countermeasures. As a result, the discipline and community are growing, and usage of ABMS approaches is expanding. The evaluation team assessed the role the FHWA Exploratory Advanced Research (EAR) Program played in this growth and how the EAR Program–funded research led to further developments and advancements. Beginning in 2009, the FHWA EAR Program began investigating the use of agent-based modeling techniques for characterizing driver and traveler behavior. The EAR Program sought to address technological advancements being applied to vehicles within the transportation network.