Paul Waddell - US grants

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9818378
Waddell
This Urban Research Initiative (URI) project develops a new approach to assess two particular linkages that are central to the development of more sophisticated and integrated urban dynamic models in the domain of interactions between the human, built, and natural environments. In particular, the linkages between land use and transportation, and between land use and land cover will be investigated, with focus on the development of reusable modeling components that provide flexibility in analyzing common problems. The specific research questions that will be probed using the modeling components address how resolution and scale of space, time, and behavior affect the result of the analysis.

Land use patterns interact strongly with many other properties of urban areas, including transportation patterns, social capital, air and water quality, and stresses on natural ecosystems. This interaction is complex, e.g., land use patterns strongly affect transportation patterns, which affect air quality and other environmental factors, which in turn affect land use patterns. Similarly, land use, land cover, and environmental stressors are strongly interdependent, and developing a land use-land cover model provides a foundation for modeling urban-environmental interactions. Despite these complex interactions, in most urban models land use is considered an exogenous variable, that is, the land use patterns are simply input into the model without accounting for feedback.

In this research, modeling components will be developed by extending the UrbanSim land use simulation model, and adding generalization of the underlying data structures and behavior using object oriented programming methods. The results of the project will be twofold. First, a set of reusable modeling components will be produced for the development of integrated models of the dynamic interactions between the human, build, and natural environments. The software and documentation will be freely available via the Internet. Second, these modeling components will be applied to develop integrated land use-transportation models and also land use-land cover models. A key aspect of this research will be the cultivation of partnerships with metropolitan Planning Organizations that will participate in the design and testing of the models developed within this project.
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The interactions between urban development and ecological processes are extraordinarily complex. Urban development evolves over time and space as the outcome of microscopic interactions of individual choices and actions taken by multiple agents. These decisions affect ecosystem structures and functions through the conversion of land, fragmentation of natural habitat use, disruption of hydrological systems, and modification of energy flow and nutrient cycles. Environmental changes at local and regional scales affect human well-being and preferences as well as the decisions people make. This project will develop an integrated model of urban development and land-cover change in the central Puget Sound region that can interface with models representing a large set of ecosystem processes. The focus of this project will be on linking urban development to bird diversity as a test case for an integrated modeling approach. This approach builds on model traditions in urban economics, landscape ecology, bird population dynamics, and complex system science, each of which offers different perspectives on modeling urban ecological interactions. The project will apply Bayesian networks and a multi-agent microsimulation approach because of the potential for those approaches to support complex inference modeling in problem domains with inherent uncertainty. Instead of separately simulating urban growth and its impacts on birds habitats, this project will develop a framework to simulate metropolitan areas as they evolve through the dynamic interactions between urban development and ecological processes and link them through a spatially explicit representation of the urban landscape.

Assessments of ecological impacts of urban growth that are timely, accurate, and transparent are crucial to sound policy and management decisions. Although extensive urban research has focused on the dynamics of urban systems and their ecological interactions, these diverse urban processes have yet to be synthesized into one coherent modeling framework. Simulation models of urban and ecological dynamics have evolved in separate knowledge domains. While both of these research areas deal with human-environmental interactions, they do so with very different emphases, scale, methodology and objectives. This research will investigate how best to model complexity and uncertainty of coupled socioeconomic and biophysical processes in metropolitan regions and their interactions with the policy domain. This project will emphasize the importance of explicitly representing human and ecological processes in modeling urban systems, including patterns, processes, and impacts. Ultimately, this project will assist in identifying answers to questions related to the potential use of public policy to intervene in urban ecological systems in ways that may reduce ecological damage from urban processes while sustaining economically and socially viable urban communities for people. The project therefore should help in the development of tools for policy makers to explore the links between human behaviors and environmental change. This project is an award emanating from the FY 2001 special competition in Biocomplexity in the Environment focusing on the Dynamics of Coupled Natural and Human Systems.

EIA-0090832
Alan Borning
University of Washington

TITLE: Software Architectures for Microsimulation of Urban Development, Transportation and Environmental Impact

Patterns of land use and available transportation systems play critical role in determining the economic vitality, livability, and sustainability of urban areas. Transportation interacts strongly with land use; different kinds of transportation systems induce different patterns of land use, while at the same time, different kinds of land use induce demands for different kinds of transportation systems. Both land use and transportation have substantial environmental effects, in particular on emissions, resource consumption and open space. Government policies and investments affect patterns of land use and transportation in many complex and sometimes unintended ways.

This proposal will develop a fully disaggregated Microsimulation system for modeling urban development and government investments and policies related to transportation, land use and environment. Technical support can play a critical role in fostering informed civic deliberation and debate on these issues by allowing urban planners and stakeholders to be able to consider different scenarios-packages of possible policies and investments-and then, based on these alternatives, model the effects of these scenarios on patterns of urban growth and redevelopment, of transportation usage, and resource consumption, over periods of twenty or more years. This proposal will concentrate on two related computer science areas; the software engineering issues that arise in the design and construction of such a large, complex model, and the human computer interaction issues that arise in using it.

A set of government partnerships is an integral part of this research. At the federal level, there are commitments from the Federal Highway Administration and the Federal Transit Administration (both units in the Department of Transportation), and at the local level, from the Puget Sound Regional Council, the governmental organization charged with land use and transportation planning.

EIA-0121326
Borning, Alan
University of Washington

ITR/PE: Interaction and Participation in Integrated Urban Land Use, Transportation, and Environmental Modeling

Patterns of land use and transportation play a critical role in determining the economic vitality, livability, and sustainability of urban areas. Transportation interacts strongly with land use: different kinds of transportation systems induce different patterns of land use, while at the same time, different kinds of land use induce demands for different kinds of transportation systems. Both have significant environmental effects. This integrated research program will support the construction and deployment of sophisticated models of land use, transportation, and environmental impact. The goal is to provide tools for stakeholders, such as urban planners, government staff, and citizens' groups, to help predict future patterns of urban development under different possible scenarios over periods of twenty or more years, allowing them to make more informed choices. Anticipated scientific advances include: in human-computer interaction, more effective ways of understanding the results from and interacting with complex simulations, and ways of linking stakeholder values with design choices in simulations and their interfaces; in graphics, capabilities for producing simulated street-level animations of urban environments from a policy-driven simulation; and in software engineering, new software structures that allow us to design, integrate, and evolve complex and diverse urban submodels.

Patterns of land use and available transportation systems play a critical role in determining the economic vitality, livability, and sustainability of urban areas. Transportation interacts strongly with land use, and both land use and transportation has substantial environmental effects, in particular on emissions, resource consumption, and open space. Government policies and investments affect patterns of land use and transportation in many, complex, and sometimes unintended ways. With prior supportthe research team has developed UrbanSim, a sophisticated simulation system to model urban development. The goal is to provide strong technical support to help government agencies and citizens make more informed decisions and to allow stakeholders to be able to consider different scenarios, packages of possible policies and investments, and then, based on these alternatives, model the effects of these scenarios over periods of twenty or more years. However, major challenges remain. Two significant ones are addressed in an integrated fashion in the research proposed : first, assessing and representing uncertainty; and second, supporting presentation of results and interaction with the simulation in an appropriate way for a wide range of stakeholders. Predicting the future is a risky business. There are numerous, complex, and interacting sources of uncertainty in urban simulations of the sort we are developing. These include measurement errors (such as missing or incorrect information in the input data), uncertainty regarding exogenous data and other input parameters (for example, regarding a macroeconomic forecast), systematic errors (for example, due to problems with sampling), and uncertainty arising from the model structure and from the stochastic nature of the simulation. Nevertheless, citizens and governments do have to make decisions, using the best available information. At the same time, it is necessary to represent the uncertainty in conclusions as well as possible, both for truthfulness and as important data to assist in selecting among alternatives. For example, iti may be desirable to select an apparently slightly less desirable alternative, if it substantially reduced uncertainty, or provided more flexibility to address situations with particularly high uncertainty. To play a useful and legitimate role in the political process, the results from modeling alternate scenarios must be presented in useful ways to elected officials and citizens in the region, in ways that let them understand the alternatives in light of what is important to them. Our primary tool for presenting these results are indicator; numeric quantities
that distill attributes of interest from the voluminous output of the simulation. These presentations should include clear and useful representations of uncertainty, as it propagates through the model and to the indicator values. In addition, the research may go beyond simply presenting the results to support stakeholders in being able to explore and test alternate scenarios (and the uncertainties around them), via a web interface.

Intellectual Merit
This proposal is grounded in the research challenges in the two principal areas of research. 1) In computational statistics these are developing, analyzing, and validating techniques for representing and propagating uncertainty through a sophisticated modeling system. The research approach uses promising but preliminary results in Bayesian melding. New statistical methods may be needed which are adapted to the challenges posed by UrbanSim, including model stochasticity, large effects of measurement and systematic errors, high dimension of model inputs and outputs, and significant running time for the underlying model. 2) In addition to the statistical challenges, however, undertaking this approach makes extreme computational demands; and achieving acceptable performance will require algorithmic advances, as well as sound software engineering. In human computer interaction, among the research challenges are supporting meaningful stakeholder access to and interaction with complex simulations, including representations of uncertainty. Finally, in the emerging area of science of design, an important question is how to design and evaluate the system overall, in a principled way, to support such basic values as accurate presentation of results (including limitations and uncertainties) and transparency.

Broader Impacts
If this work succeeds, UrbanSim has the potential to significantly aid in public deliberation over major decisions regarding urban sprawl, economic health, sustainability, and other issues. Urban Sim is Open Source and freely available, and has attracted considerable interest and use. Further, the results in computational statistics should be applicable to a broad range of simulations of economic or environmental processes to inform public policy development and deliberation. Finally, the interaction techniques and findings should be applicable to a range of other stakeholder interactions with complex models and sources of
information.

Project Summary
The goals of the proposed research are twofold: first, to advance the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques; and second, to use these techniques in building better transportation models, which can then be used to help inform public deliberation regarding major infrastructure decisions. Problems of maintaining or replacing aging infrastructure, or adding new infrastructure to meet the needs of population growth and urban expansion of metropolitan areas, are becoming increasingly difficult to solve, in part because the cost is extremely large, and in part because the political discourse over alternative solutions is contentious and reflects divergent assumptions and values. Often, a major source of disagreement is cost; but another is rooted in differing assumptions about how people would adjust their travel in response to changed circumstances in both the short and long term, and how much congestion would result. Current transportation models used in operational analysis and planning are too behaviorally simple to be very useful in addressing these questions. Recent research advances have provided improvements in behavioral representation in these kinds of choice situations, but to date these nnovations are not integrated and are computationally not feasible for large-scale application. During the last decade, the artificial intelligence community has developed a set of techniques that enable fine-grained activity recognition from sensor data; among the most advanced and successful are approaches based on Dynamic Bayesian networks and statistical relational learning. The research team will build on this foundation, integrating these AI techniques with the Discrete Choice Models used in econometric approaches, to yield a new, hybrid reasoning system: Dynamic Discrete Choice Networks. This technique will be applied to the challenging domain of modeling dynamic travel choices of individuals, such as the number of trips, scheduled time of departure, destinations, modes, and routes and to predict how these choices change under dynamically updated travel conditions.

Intellectual Merit

The merit of this proposal is grounded in the research challenges in the artificial intelligence and urban modeling areas. This project advances the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques that are well suited for modeling the complex combinations of factors involved in human decision making in the commonsense domain of daily travel. By integrating this modeling power into probabilistic temporal models, Dynamic Discrete Choice Networks will provide an extremely general and flexible framework for learning and recognizing human activities from sensor data and for understanding how everyday human decision making adapts to a constantly changing environment.

Broader Impacts

UrbanSim has the potential to significantly aid in public deliberation over major decisions regarding transportation replacement or expansion of transportation infrastructure, managing urban development, planning for response to mitigate the effects of events such as hurricane Katrina or a major earthquake, and other issues. UrbanSim is Open Source and freely available, and has already attracted considerable interest and use. Because of their improved ability to recognize and analyze human activities from raw sensor data, Dynamic Discrete Choice Networks will have applications to other significant domains as well, such as eldercare and long term health monitoring.

In this project, the PI and his team will develop a new simulation framework to interactively model and visualize socio-economic and geometric characteristics of urban areas. The framework will consist of a synergistic collaboration of three different areas: behavioral urban modeling, probabilistic graphical modeling, and visualization and computer graphics. In machine learning and statistics, the area of probabilistic graphical modeling offers a flexible framework to build, estimate and simulate from models of substantial complexity and scale, with partially observed data. By accounting for uncertainty and interdependencies, including aspects of dynamic equilibrium that arise in modeling the complex spatio-temporal dynamics of urban areas, the PI argues there is significant potential for breakthroughs in modeling large-scale urban systems. Similarly, by integrating behavioral and geometrical dimensions of urban areas, he expects to exploit the power of behavioral simulations more effectively by filling in geometric details that behavioral models are not well suited to manage, and at the same time provide a powerful framework to generate 2D and 3D geometric representations of urban areas that are behaviorally and geometrically consistent. The PI will take advantage of massive datasets available for urban areas, including parcel and building inventories, business establishment inventories, census data, household surveys, and GIS data on physical and political features, and will fuse these data into a coherent and consistent database to support his modeling objectives. This data fusion will address imputation of missing data, accounting for complex spatial and relational connections among the data sources. The PI will evaluate the accuracy and usability of his system through several deployments in diverse contexts. The PI has elicited engagement from the Urban Land Institute, the European Research Council, and the Council for Scientific and Industrial Research. Several organizations in the San Francisco Bay Area in California and the Puget Sound region in Washington will serve as testbeds for the research. Finally, the PI will collaborate with other NSF-funded research projects, such as the Drought Research Initiative Network, in order to investigate correlations between urban development and water/drought.

Broader Impacts: The results of this multidisciplinary project will have a transformative effect on the area of urban simulation, in that they will enable non-professionals as well as the general public to better understand urban phenomena. City planners, researchers, students, and citizens will be able to efficiently simulate urban processes not previously possible, and to visualize the effects of adopting different urban policies on urban livability and sustainability outcomes, and to address local and global concerns regarding equity, infrastructure, and economic development. The framework will provide interactive desktop and web-based interfaces for configuring urban scenario inputs to a simulation that may reach petabytes in data size, and to visualize the simulation results using 2D aerial views, 3D city walkthroughs, and choroplethic maps and tables of indicators portraying the simulated area. Thus, the work will also advance the fields of visualization and computer graphics, through development of new techniques for large-scale urban modeling and rendering. The PI will develop an open-source system to make the results of this research widely available.