UrbanSim

Last updated
UrbanSim
Initial releaseMarch 7, 2014;9 years ago (2014-03-07) [1]
Stable release
3.1.1 / May 9, 2017;6 years ago (2017-05-09) [2]
Repository github.com/UDST/urbansim
Written in Python
Operating system MacOS, Linux, and Windows [3]
License New BSD License [4]
Website urbansim.com

UrbanSim is an open source urban simulation system designed by Paul Waddell of the University of California, Berkeley and developed with numerous collaborators to support metropolitan land use, transportation, and environmental planning. It has been distributed on the web since 1998, with regular revisions and updates, from www.urbansim.org. Synthicity Inc coordinates the development of UrbanSim and provides professional services to support its application. The development of UrbanSim has been funded by several grants from the National Science Foundation, the U.S. Environmental Protection Agency, the Federal Highway Administration, as well as support from states, metropolitan planning agencies and research councils in Europe and South Africa. Reviews of UrbanSim and comparison to other urban modeling platforms may be found in references. [5] [6] [7]

Contents

Applications

The first documented application of UrbanSim was a prototype application to the Eugene-Springfield, Oregon setting. [8] [9] Later applications of the system have been documented in several U.S. cities, including Detroit, Michigan, [10] Salt Lake City, Utah, [11] [12] San Francisco, California, [13] and Seattle, Washington. [14] In Europe, UrbanSim has been applied in Paris, France; [15] [16] [17] Brussels, Belgium; and Zurich, Switzerland with various other applications not yet documented in published papers.

Architecture

The initial implementation of UrbanSim was implemented in Java. [18] [19] The software architecture was modularized and reimplemented in Python beginning in 2005, making extensive use of the Numpy numerical library. The software has been generalized and abstracted from the UrbanSim model system, and is now referred to as the Open Platform for Urban Simulation (OPUS), in order to facilitate a plug-in architecture for models such as activity-based travel, dynamic traffic assignment, emissions, and land cover change. [20] OPUS includes a Graphical User Interface, and a concise expression language to facilitate access to complex internal operations by non-programmers. [21] Beginning in 2012, UrbanSim was re-implemented using current Scientific Python libraries such as Pandas. UrbanSim Inc. has developed the UrbanSim Cloud Platform that deploys simulations on the cloud for scalability, enabling hundreds or even thousands of simulations to be run simultaneously, and a web browser based User Interface that features a 3D web map view of inputs and outputs from the simulation. UrbanSim models have been pre-built for 400 metropolitan areas within the United States at a census block level of detail. Users anywhere in the world can also build UrbanSim models using zone and parcel templates, by uploading local data and using the cloud resources to auto-specify and calibrate the models using local data. Details are available at www.urbansim.com.

Design

Earlier urban model systems were generally based on deterministic solution algorithms such as Spatial Interaction or Spatial Input-Output, that emphasize repeatability and uniqueness of convergence to an equilibrium, but rest on strong assumptions about behavior, such as agents having perfect information of all the alternative locations in the metropolitan area, transactions being costless, and markets being perfectly competitive. Housing booms and busts, and the financial crisis, are relatively clear examples of market imperfections that motivate the use of less restrictive assumptions in UrbanSim. Rather than calibrating the model to a cross-sectional equilibrium, or base-year set of conditions, statistical methods have been developed to calibrate uncertainty in UrbanSim arising from its use of Monte Carlo methods and from uncertainty in data and models, against observed data over a longitudinal period, using a method known as Bayesian Melding. [22] In addition to its less strong assumptions about markets, UrbanSim departs from earlier model designs that used high levels of aggregation of geography into large zones, and agents such as households and jobs into large groups assumed to be homogeneous. Instead, UrbanSim adopts a microsimulation approach meaning that it represents individual agents within the simulation. This is an agent-level model system, but unlike most agent-based models, it does not focus exclusively on the interactions of adjacent agents. Households, businesses or jobs, buildings, and land areas represented alternatively by parcels, gridcells, or zones, are used to represent the agents and locations within a metropolitan area. The parcel level modeling applications allow for the first time the representation of accessibility at a walking scale, something that cannot be effectively done at high levels of spatial aggregation. [23]

Engagement

One of the motivations for the UrbanSim project is to not only provide robust predictions of the potential outcomes of different transportation investments and land use policies, but also to facilitate more deliberative civic engagement in what are often contentious debates about transportation infrastructure, or land policies, with uneven distributions of benefits and costs. Initial work on this topic has adopted an approach called Value Sensitive Design. [24] [25] Recent work has also emerged to integrate new forms of visualization, including 3D simulated landscapes. [26] [27]

Related Research Articles

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Land-use forecasting undertakes to project the distribution and intensity of trip generating activities in the urban area. In practice, land-use models are demand-driven, using as inputs the aggregate information on growth produced by an aggregate economic forecasting activity. Land-use estimates are inputs to the transportation planning process.

<span class="mw-page-title-main">Geoinformatics</span> Application of information science methods in geography, cartography, and geosciences

Geoinformatics is a scientific field primarily within the domains of Computer Science and technical geography. It focuses on the programming of applications, spatial data structures, and the analysis of objects and space-time phenomena related to the surface and underneath of Earth and other celestial bodies. The field develops software and web services to model and analyse spatial data, serving the needs of geosciences and related scientific and engineering disciplines. The term is often used interchangeably with Geomatics, although the two have distinct focuses; Geomatics emphasizes acquiring spatial knowledge and leveraging information systems, not their development. At least one publication has claimed the discipline is pure computer science outside the realm of geography.

Articles in economics journals are usually classified according to JEL classification codes, which derive from the Journal of Economic Literature. The JEL is published quarterly by the American Economic Association (AEA) and contains survey articles and information on recently published books and dissertations. The AEA maintains EconLit, a searchable data base of citations for articles, books, reviews, dissertations, and working papers classified by JEL codes for the years from 1969. A recent addition to EconLit is indexing of economics journal articles from 1886 to 1968 parallel to the print series Index of Economic Articles.

Geovisualization or geovisualisation, also known as cartographic visualization, refers to a set of tools and techniques supporting the analysis of geospatial data through the use of interactive visualization.

<span class="mw-page-title-main">Spatial analysis</span> Formal techniques which study entities using their topological, geometric, or geographic properties

Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

<span class="mw-page-title-main">Modifiable areal unit problem</span> Source of statistical bias

The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. MAUP affects results when point-based measures of spatial phenomena are aggregated into spatial partitions or areal units as in, for example, population density or illness rates. The resulting summary values are influenced by both the shape and scale of the aggregation unit.

<span class="mw-page-title-main">Transportation forecasting</span>

Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation. Feeding it with predicted data for population, employment, etc. results in estimates of future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station. The current technologies facilitate the access to dynamic data, big data, etc., providing the opportunity to develop new algorithms to improve greatly the predictability and accuracy of the current estimations.

Urban, city, or town planning is the discipline of planning which explores several aspects of the built and social environments of municipalities and communities:

A spatial decision support system (SDSS) is an interactive, computer-based system designed to assist in decision making while solving a semi-structured spatial problem. It is designed to assist the spatial planner with guidance in making land use decisions. A system which models decisions could be used to help identify the most effective decision path.

The Land Use Evolution and Impact Assessment Model is a computer model developed at the University of Illinois at Urbana-Champaign. LEAM is designed to simulate future land use change as a result of alternative policies and development decisions. In recent years, LEAM has been used in combination with transportation and social cost models to better capture the effects land use has on transportation demand and social costs and vice versa.

Shiba Prasad Chatterjee was a Professor of Geography at the University of Calcutta, India. He served as President of the International Geographical Union from 1964 until 1968, Chatterjee received a Murchison Award from the Royal Geographical Society in 1959, and a Padma Bhushan from the Government of India in 1985. He coined the name 'Meghalaya' for one of India's states.

The Sustainable Communities and Climate Protection Act of 2008, also known as Senate Bill 375 or SB 375, is a State of California law targeting greenhouse gas emissions from passenger vehicles. The Global Warming Solutions Act of 2006 sets goals for the reduction of statewide greenhouse gas emissions. Passenger vehicles are the single largest source of greenhouse gas emissions statewide, accounting for 30% of total emissions. SB 375 therefore provides key support to achieve the goals of AB 32.

The Centre for Advanced Spatial Analysis (CASA) is a research centre at University College London (UCL), which specialises in the application and visualisation of spatial analytic techniques and simulation models to cities and regions. It is a constituent department of The Bartlett Faculty of the Built-Environment.

Historical dynamics broadly includes the scientific modeling of history. This might also be termed computer modeling of history, historical simulation, or simulation of history - allowing for an extensive range of techniques in simulation and estimation. Historical dynamics does not exist as a separate science, but there are individual efforts such as long range planning, population modeling, economic forecasting, demographics, global modeling, country modeling, regional planning, urban planning and many others in the general categories of computer modeling, planning, forecasting, and simulations.

In public policy a polycentric network is a group of distinct local, regional, or national entities that work co-operatively towards a common goal. Proponents claim that such networks can better adapt to changing issues collectively than individually, thus providing network participants better results from relevant efforts.

<span class="mw-page-title-main">Land change modeling</span> Geographic and ecological field of study

Land change models (LCMs) describe, project, and explain changes in and the dynamics of land use and land-cover. LCMs are a means of understanding ways that humans change the Earth's surface in the past, present, and future.

<span class="mw-page-title-main">GAMA Platform</span> Simulation platform

GAMA is a simulation platform with a complete modelling and simulation integrated development environment (IDE) for building spatially explicit agent-based simulations.

CLUE model is a spatially explicit land-use change model developed to simulate future land-use and land-cover changes, including urban expansion, deforestation, land abandonment, and agricultural intensification. CLUE model is a dynamic modeling framework which simulates land-use change based on quantification of biophysical and human drivers of land-use conversion. The CLUE model can be applied at the national and continental scale, implemented in Central America, Ecuador, China, and Java, Indonesia. CLUE model cannot be employed at regional level. Different versions of CLUE model include CLUE-S, CLUE-Scanner, and Dyna-CLUE models.

References

  1. ↑ "First GitHub Release". github.com/UDST. Retrieved 24 December 2018.
  2. ↑ "GitHub Releases". github.com/UDST. Retrieved 24 December 2018.
  3. ↑ "Getting Started". github.com/UDST. Retrieved 24 December 2018.
  4. ↑ "License". github.com/UDST. Retrieved 24 December 2018.
  5. ↑ U.S. EPA (2000) Projecting Land-Use Change: A Summary of Models for Assessing the Effects of Community Growth and Change on Land-Use Patterns. EPA/600/R-00/098. U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH. 260 pp.
  6. ↑ Miller, E. J., D. S. Kriger and J. D. Hunt (1998). Integrated Urban Models for Simulation of Transit and Land-Use Policies, Transit Cooperative Research Project, National Academy of Sciences.
  7. ↑ Richard Dowling, Robert Ireson, Alexander Skabardonis, David Gillen, Peter Stopher, Alan Horowitz, John Bowman, Elizabeth Deakin, and Robert Dulla. Predicting short-term and long-term air quality effects of traffic-flow improvement projects: Interim report and Phase II work plan. Technical Report 25-21, National Cooperative Highway Research Program, Transportation Research Board, National Research Council, October 2000.
  8. ↑ Waddell, Paul (2000). A behavioral simulation model for metropolitan policy analysis and planning: residential location and housing market components of UrbanSim. Environment and Planning B: Planning and Design Vol 27, No 2 (247 – 263).
  9. ↑ Waddell, Paul (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation and Environmental Planning. Journal of the American Planning Association, Vol. 68, No. 3, (297-314).
  10. ↑ Waddell, Paul, Liming Wang and Xuan Liu (2008) UrbanSim: An Evolving Planning Support System for Evolving Communities. Planning Support Systems for Cities and Regions. Richard Brail, Editor. Cambridge, MA: Lincoln Institute for Land Policy. pp. 103-138.
  11. ↑ Waddell, P. and F. Nourzad. (2002). Incorporating Non-motorized Mode and Neighborhood Accessibility in an Integrated Land Use and Transportation Model System, Transportation Research Record No. 1805 (119-127).
  12. ↑ Waddell, Paul, Gudmundur Freyr Ulfarsson, Joel Franklin and John Lobb, (2007) Incorporating Land Use in Metropolitan Transportation Planning, Transportation Research Part A: Policy and Practice Vol. 41 (382-410).
  13. ↑ Waddell, P., L. Wang and B. Charlton (2007) Integration of a Parcel-Level Land Use Model and an Activity-Based Travel Model. World Conference on Transport Research, Berkeley, CA., June 2007.
  14. ↑ Waddell, P., C. Bhat, N. Eluru, L. Wang, R. Pendyala (2007) Modeling the Interdependence in Household Residence and Workplace Choices. Transportation Research Record Vol. 2003 (84-92).
  15. ↑ de Palma, A., K. Motamedi, N. Picard, P. Waddell (2007) Accessibility and Environmental Quality: Inequality in the Paris Housing Market. European Transport No. 36, (47-64).
  16. ↑ de Palma, A., N. Picard, P. Waddell (2007) Discrete Choice Models with Capacity Constraints: An Empirical Analysis of the Housing Market of the Greater Paris Region. Journal of Urban Economics Vol. 62 (204-230).
  17. ↑ de Palma, A., N. Picard, P. Waddell (2005) Residential Location Choice with Endogenous Prices and Traffic in the Paris Metropolitan Region. European Transport. No. 31 (67-82).
  18. ↑ Noth, M., A. Borning and P. Waddell. (2003) An Extensible, Modular Architecture for Simulating Urban Development, Transportation, and Environmental Impacts. Computers, Environment and Urban Systems Vol. 27, No. 2, (181-203).
  19. ↑ Waddell, P., A. Borning, M. Noth, N. Freier, M. Becke, G. Ulfarsson. (2003). UrbanSim: A Simulation System for Land Use and Transportation. Networks and Spatial Economics 3 (43-67).
  20. ↑ Paul Waddell, Hana Ševcíková, David Socha, Eric Miller, Kai Nagel, Opus: An Open Platform for Urban Simulation. Presented at the Computers in Urban Planning and Urban Management Conference, June, 2005, London, U.K.
  21. ↑ Borning, Alan, Hana Ševčíková, and Paul Waddell (2008) A Domain-Specific Language for Urban Simulation Variables, Proceedings of the 9th Annual International Conference on Digital Government Research, Montréal, Canada, May 2008.
  22. ↑ Sevcikova, H., A. Raftery and P. Waddell (2007) Assessing Uncertainty in Urban Simulations Using Bayesian Melding. Transportation Research Part B: Methodology Vol. 41, No. 6 (652-659).
  23. ↑ Lee, Brian, Paul Waddell, Liming Wang and Ram Pendyala (2010) Re-examining the Influence of Work and Non-work Accessibility on Residential Location Choices with a Micro-analytic Framework. Environment and Planning A Vol. 42 (913-930)
  24. ↑ Davis, J., P. Lin, A. Borning, B. Friedman, P. Kahn and P. Waddell. (2006) Value Sensitive Design of Interactions with UrbanSim Indicators. Computer, October, 2006.
  25. ↑ Borning, Alan, Paul Waddell and Ruth Förster (2008) UrbanSim: Using Simulation to Inform Public Deliberation and Decision-Making. In Digital Government: Advanced Research and Case Studies. Hsinchun Chen, Lawrence Brandt, Sharon Dawes, Valerie Gregg, Eduard Hovy, Ann Macintosh, Roland Traunmüller, and Catherine A. Larson, Eds. Springer. pp. 439 – 463.
  26. ↑ Aliaga, Daniel, Carlos Vanegas, Bedřich Beneš, Paul Waddell. (2009) Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery. IEEE Transactions on Visualization & Computer Graphics.
  27. ↑ Vanegas, Carlos, Daniel Aliaga, Bedrich Beneš, Paul Waddell (2009) Interactive Design of Urban Spaces using Geometrical and Behavioral Modeling. ACM Transactions on Graphics, also ACM SIGGRAPH Asia, 28(5): 10 pages, 2009.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.