Software:UrbanSim

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Short description: Open source urban simulation system
UrbanSim
Initial releaseMarch 7, 2014; 10 years ago (2014-03-07)[1]
Stable release
3.1.1 / May 9, 2017; 7 years ago (2017-05-09)[2]
Repositorygithub.com/UDST/urbansim
Written inPython
Operating systemMacOS, Linux, and Windows[3]
LicenseNew BSD License[4]
Websiteurbansim.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]

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]

References

  1. ↑ "First GitHub Release". github.com/UDST. https://github.com/UDST/urbansim/releases. Retrieved 24 December 2018. 
  2. ↑ "GitHub Releases". github.com/UDST. https://github.com/UDST/urbansim/releases. Retrieved 24 December 2018. 
  3. ↑ "Getting Started". github.com/UDST. https://udst.github.io/urbansim/gettingstarted.html#installation. Retrieved 24 December 2018. 
  4. ↑ "License". github.com/UDST. https://github.com/UDST/urbansim/blob/master/LICENSE. 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. [1]
  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.

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