IDEAS home Printed from https://ideas.repec.org/a/ris/jtralu/0031.html
   My bibliography  Save this article

Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco

Author

Listed:
  • Waddell, Paul

    (University of California, Berkeley; United States)

  • Wang, Liming

    (University of California, Berkeley)

  • Charlton, Billy

    (San Francisco County Transportation Authority)

  • Olsen, Aksel

    (San Francisco Planning Department)

Abstract

This paper develops a prototype of an integrated microsimulation model system combining land use at a parcel level with activity-based travel in San Francisco, California. The paper describes the motivation for the model system, its design, data development, and preliminary application and testing. The land use model is implemented using UrbanSim and the Open Platform for Urban Simulation (OPUS), using parcels and buildings rather than zones or grid cells as spatial units of analysis. Measures of accessibility are derived from the San Francisco SF-CHAMP activity-based travel model, and the predicted locations of households and business establishments are used to update the micro-level inputs needed for the activity-based travel model. Data used in the model include business establishments linked to parcels over several years, and a panel of parcels that allow modeling of parcel development over time. This paper describes several advances that have not been previously integrated in an operational model system, including the use of parcels and buildings as units of location for consumers and developers of real estate, the use of business establishments to represent economic activity, and the interfacing of this microsimulation land use model with a microsimulation activity-based travel model. Computational performance and development effort were found to be modest, with land use model run times averaging one minute per year on a current desktop computer, and two to three minutes on a current laptop. By contrast, long run times of the travel model suggest that there may be a need to reconsider the level of complexity in the travel model for an integrated land use and transportation model system application to be broadly usable. The land use model is currently in refinement, being used to identify input data and model specification adjustments needed in order to bring it into operational use, which is planned over the next several months.

Suggested Citation

  • Waddell, Paul & Wang, Liming & Charlton, Billy & Olsen, Aksel, 2010. "Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 65-84.
    • Handle: RePEc:ris:jtralu:0031
    as

    Download full text from publisher

    File URL: http://www.jtlu.org/index.php/jtlu/article/view/124/121
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ryuichi Kitamura & Cynthia Chen & Ram Pendyala & Ravi Narayanan, 2000. "Micro-simulation of daily activity-travel patterns for travel demand forecasting," Transportation, Springer, vol. 27(1), pages 25-51, February.
    2. David Levinson & Yuanlin Huang, 1997. "A Windowed Transportation Planning Model," Working Papers 199703, University of Minnesota: Nexus Research Group.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Levinson, David M., 2013. "Introduction: The Journal of Transport and Land Use enters year six," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 1-5.
    2. Marko Kryvobokov & Aurélie Mercier & Alain Bonnafous & Dominique Bouf, 2013. "Simulating housing prices with UrbanSim: predictive capacity and sensitivity analysis," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 31-44, March.
    3. Ravulaparthy, Srinath & Goulias, Konstadinos G., 2011. "Forecasting with Dynamic Microsimulation: Design, Implementation, and Demonstration," University of California Transportation Center, Working Papers qt2x12q5pv, University of California Transportation Center.
    4. Nadafianshahamabadi, Razieh & Tayarani, Mohammad & Rowangould, Gregory, 2021. "A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts," Journal of Transport Geography, Elsevier, vol. 94(C).
    5. Levinson, David, 2011. "Introduction," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 4(1), pages 1-3.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arun Kuppam & Ram Pendyala, 2001. "A structural equations analysis of commuters' activity and travel patterns," Transportation, Springer, vol. 28(1), pages 33-54, February.
    2. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    3. Gonzalo Suarez & Rachata Muneepeerakul, 2022. "Modeling human migration driven by changing mindset, agglomeration, social ties, and the environment," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-11, February.
    4. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    5. Yang, Zimo & Lian, Defu & Yuan, Nicholas Jing & Xie, Xing & Rui, Yong & Zhou, Tao, 2017. "Indigenization of urban mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 232-243.
    6. Yamaguchi, Hiromichi & Nakayama, Shoichiro, 2020. "Detection of base travel groups with different sensitivities to new high-speed rail services: Non-negative tensor decomposition approach," Transport Policy, Elsevier, vol. 97(C), pages 37-46.
    7. Fagiolo, Giorgio & Santoni, Gianluca, 2015. "Human-mobility networks, country income, and labor productivity," Network Science, Cambridge University Press, vol. 3(3), pages 377-407, September.
    8. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    9. Qi Xuan & Premkumar Devanbu & Vladimir Filkov, 2016. "Converging Work-Talk Patterns in Online Task-Oriented Communities," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-20, May.
    10. Ram Pendyala & Chandra Bhat, 2004. "An Exploration of the Relationship between Timing and Duration of Maintenance Activities," Transportation, Springer, vol. 31(4), pages 429-456, November.
    11. CHEN Xueming & CHEN Ying, 2009. "Examining an important urban transportation management tool: subarea modeling," Economia. Seria Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(2), pages 98-113, December.
    12. Christopher Upchurch & Michael Kuby, 2014. "Evaluating light rail sketch planning: actual versus predicted station boardings in Phoenix," Transportation, Springer, vol. 41(1), pages 173-192, January.
    13. Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
    14. Zhang, Xiaoning & Yang, Hai & Huang, Hai-Jun & Zhang, H. Michael, 2005. "Integrated scheduling of daily work activities and morning-evening commutes with bottleneck congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(1), pages 41-60, January.
    15. Siqin Wang & Mengxi Zhang & Tao Hu & Xiaokang Fu & Zhe Gao & Briana Halloran & Yan Liu, 2021. "A Bibliometric Analysis and Network Visualisation of Human Mobility Studies from 1990 to 2020: Emerging Trends and Future Research Directions," Sustainability, MDPI, vol. 13(10), pages 1-22, May.
    16. Sean Doherty, 2006. "Should we abandon activity type analysis? Redefining activities by their salient attributes," Transportation, Springer, vol. 33(6), pages 517-536, November.
    17. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 2021. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 48(3), pages 1481-1502, June.
    18. Alex A. Kurzhanskiy, 2022. "A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network," Mathematics, MDPI, vol. 10(3), pages 1-11, February.
    19. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 0. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 0, pages 1-22.
    20. Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.

    More about this item

    Keywords

    microsimulation; land use model; activity-based travel; integrated modeling; residential location choice; business location choice; real estate development; real estate prices; built environment; travel behavior;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:jtralu:0031. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Arlene Mathison (email available below). General contact details of provider: https://edirc.repec.org/data/ctumnus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.