IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v50y2023i3p709-722.html
   My bibliography  Save this article

A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data

Author

Listed:
  • Kevin Credit
  • Zander Arnao

Abstract

This paper describes a fully customizable open source method to create linked origin-destination data on commuting flows by mode at the Census tract scale by combining LODES and ACS data from the US Census Bureau. With additional work, the method could be scaled to the entire US (with a small number of exceptions) for every year from 2002 to 2019. For demonstration purposes, the paper applies this method to 2015 commuting flows in Cook County, Illinois. At an aggregate scale, the results of this application show that commuting by all modes is dominated by travel to large regional employment centres. However, the pattern is more localised for the walking mode, and focused along corridors of mode-specific infrastructure investment for the cycling and transit modes, as might be expected. The auto and work from home modes demonstrate the most distributed pattern of travel, revealing more instances of commuting to regional sub-centres than the other modes.

Suggested Citation

  • Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:3:p:709-722
    DOI: 10.1177/23998083221129614
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/23998083221129614
    Download Restriction: no
    File URL: https://libkey.io/10.1177/23998083221129614?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vojnovic, Igor & Darden, Joe T., 2013. "Class/racial conflict, intolerance, and distortions in urban form: Lessons for sustainability from the Detroit region," Ecological Economics, Elsevier, vol. 96(C), pages 88-98.
    2. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    3. Chris Harding & Ahmadreza Faghih Imani & Siva Srikukenthiran & Eric J. Miller & Khandker Nurul Habib, 2021. "Are we there yet? Assessing smartphone apps as full-fledged tools for activity-travel surveys," Transportation, Springer, vol. 48(5), pages 2433-2460, October.
    4. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    5. Matthew R. Graham & Mark J. Kutzbach & Brian McKenzie, 2014. "Design Comparison of LODES and ACS Commuting Data Products," Working Papers 14-38, Center for Economic Studies, U.S. Census Bureau.
    6. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    7. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    8. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    9. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    10. Yang, Yuan & Wang, Can & Liu, Wenling & Zhou, Peng, 2018. "Understanding the determinants of travel mode choice of residents and its carbon mitigation potential," Energy Policy, Elsevier, vol. 115(C), pages 486-493.
    Full references (including those not matched with items on IDEAS)

    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. Nadav Shalit & Michael Fire & Eran Ben-Elia, 2023. "A supervised machine learning model for imputing missing boarding stops in smart card data," Public Transport, Springer, vol. 15(2), pages 287-319, June.
    2. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    3. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).
    4. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    5. Christian Werner & Martin Loidl, 2021. "Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals," Data, MDPI, vol. 6(11), pages 1-11, November.
    6. F. Crawford & D. P. Watling & R. D. Connors, 2023. "Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data," Networks and Spatial Economics, Springer, vol. 23(2), pages 373-406, June.
    7. Rawad Choubassi & Lamia Abdelfattah, 2020. "How Big Data is Transforming the Way We Plan Our Cities," Briefs, Fondazione Eni Enrico Mattei, December.
    8. Freedman, Matthew & Khanna, Shantanu & Neumark, David, 2023. "Combining rules and discretion in economic development policy: Evidence on the impacts of the California Competes Tax Credit," Journal of Public Economics, Elsevier, vol. 217(C).
    9. Matthew R. Graham & Mark J. Kutzbach & Danielle H. Sandler, 2017. "Developing a Residence Candidate File for Use With Employer-Employee Matched Data," Working Papers 17-40, Center for Economic Studies, U.S. Census Bureau.
    10. Steven Dieterle & Otávio Bartalotti & Quentin Brummet, 2020. "Revisiting the Effects of Unemployment Insurance Extensions on Unemployment: A Measurement-Error-Corrected Regression Discontinuity Approach," American Economic Journal: Economic Policy, American Economic Association, vol. 12(2), pages 84-114, May.
    11. Laila Oubahman & Szabolcs Duleba, 2022. "A Comparative Analysis of Homogenous Groups’ Preferences by Using AIP and AIJ Group AHP-PROMETHEE Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    12. Jonathan I. Dingel & Felix Tintelnot, 2020. "Spatial Economics for Granular Settings," NBER Working Papers 27287, National Bureau of Economic Research, Inc.
    13. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    14. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    15. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    16. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    17. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
    18. Dieterle, Steven & Bartalotti, Otávio C. & Brummet, Quentin O., 2016. "Revisiting the Effects of Unemployment Insurance Extensions on Unemployment: A Measurement Error-Corrected RD Approach," Staff General Research Papers Archive 3392, Iowa State University, Department of Economics.
    19. Perez Perez, Jorge, 2020. "City Minimum Wages and Spatial Equilibrium Effects," SocArXiv fpx9e, Center for Open Science.
    20. Ying Ni & Jiaqi Chen, 2020. "Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis," Sustainability, MDPI, vol. 12(5), pages 1-20, March.

    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:sae:envirb:v:50:y:2023:i:3:p:709-722. 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: SAGE Publications (email available below). General contact details of provider: .

    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.