IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v79y2019ic23.html
   My bibliography  Save this article

A cumulative gravity model for inter-urban spatial interaction at different scales

Author

Listed:
  • Thompson, C.A.
  • Saxberg, K.
  • Lega, J.
  • Tong, D.
  • Brown, H.E.

Abstract

The classical gravity model assumes the knowledge of origin-destination (OD) flows between places. While this is reasonable for some modes of transportation such as air travel, it presents challenges to many other applications such as road travel where the observed flow on road segments, such as annual average daily traffic (AADT), may involve traffic for multiple OD pairs. This article introduces a methodology to calibrate a cumulative gravity model on a scale-specific road network. The model infers the traffic on OD pairs from the knowledge of link flows and we document its effectiveness in predicting the bidirectional volume of traffic on highways in the United States. Census data and state AADT were used to calibrate both the noncumulative and cumulative gravity models in three different regions in the western US: Arizona only, 10 western US states, and 16 western US states. The road networks were built by defining nodes as population centers of >10,000 inhabitants for Arizona and >500,000 for the other two networks. A systematic method to remove junction nodes that do not satisfy population threshold requirements but are necessary to maintain network connectivity is presented. The cumulative gravity model performed better (with a R-squared value of 0.93 in Arizona) than the standard model, but the improvement, based on two goodness-of-fit metrics (Common Part of Commuters and least squares) was above 2% only in the Arizona network. Removal of commercial traffic from the data further improved the model's calibration in the 10-state network. A thresholding method that connects the cumulative to the standard gravity model reveals that most road trips in Arizona are within the 150-mile range and that this distance increases to 500 miles in both western networks. Potential future applications of the present work are discussed.

Suggested Citation

  • Thompson, C.A. & Saxberg, K. & Lega, J. & Tong, D. & Brown, H.E., 2019. "A cumulative gravity model for inter-urban spatial interaction at different scales," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    • Handle: RePEc:eee:jotrge:v:79:y:2019:i:c:23
    DOI: 10.1016/j.jtrangeo.2019.102461
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692318304289
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2019.102461?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. Oleguer Sagarra & Michael Szell & Paolo Santi & Albert Díaz-Guilera & Carlo Ratti, 2015. "Supersampling and Network Reconstruction of Urban Mobility," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    2. Lei Zhang & Frank Southworth & Chenfeng Xiong & Anthon Sonnenberg, 2012. "Methodological Options and Data Sources for the Development of Long-Distance Passenger Travel Demand Models: A Comprehensive Review," Transport Reviews, Taylor & Francis Journals, vol. 32(4), pages 399-433, April.
    3. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    4. Maxime Lenormand & Sylvie Huet & Floriana Gargiulo & Guillaume Deffuant, 2012. "A Universal Model of Commuting Networks," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-7, October.
    5. Floriana Gargiulo & Maxime Lenormand & Sylvie Huet & Omar Baqueiro Espinosa, 2012. "Commuting Network Models: Getting the Essentials," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-6.
    6. Gargiulo, Floriana & Lenormand, Maxime & Huet, Sylvie & Baqueiro Espinosa, Omar, 2012. "Commuting network models: Getting the essentials," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(2).
    7. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    8. Guoqiang Shen & Saniye Gizem Aydin, 2014. "Origin-destination missing data estimation for freight transportation planning: a gravity model-based regression approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(6), pages 505-524, August.
    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. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.
    2. Deng, Ping & Song, Lian & Xiao, Ruiqi & Huang, Chengfeng, 2022. "Evaluation of logistics and port connectivity in the Yangtze River Economic Belt of China," Transport Policy, Elsevier, vol. 126(C), pages 249-267.
    3. Chumki Shikary & Somnath Rudra, 2024. "Assessment of urban-to-urban interaction and its impact on urban development: a study on a backward district of Eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 16863-16886, July.

    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. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
    2. Rongrong Hong & Wenming Rao & Dong Zhou & Chengchuan An & Zhenbo Lu & Jingxin Xia, 2020. "Commuting Pattern Recognition Using a Systematic Cluster Framework," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    3. Lenormand, Maxime & Huet, Sylvie & Gargiulo, Floriana, 2014. "Generating French virtual commuting networks at the municipality level," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(1), pages 43-55.
    4. Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    5. Chaogui Kang & Yu Liu & Diansheng Guo & Kun Qin, 2015. "A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-11, November.
    6. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    7. Chen, Ya & Li, Xue & Zhang, Richong & Huang, Zi-Gang & Lai, Ying-Cheng, 2020. "Instantaneous success and influence promotion in cyberspace — how do they occur?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    8. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    9. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    10. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    11. Chen, Yong & Geng, Maosi & Zeng, Jiaqi & Yang, Di & Zhang, Lei & Chen, Xiqun (Michael), 2023. "A novel ensemble model with conditional intervening opportunities for ride-hailing travel mobility estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    12. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    13. Abderrahman Ait-Ali & Jonas Eliasson, 2022. "The value of additional data for public transport origin–destination matrix estimation," Public Transport, Springer, vol. 14(2), pages 419-439, June.
    14. Inho Hong & Woo-Sung Jung & Hang-Hyun Jo, 2019. "Gravity model explained by the radiation model on a population landscape," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    15. Maxime Lenormand & Sylvie Huet & Floriana Gargiulo & Guillaume Deffuant, 2012. "A Universal Model of Commuting Networks," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-7, October.
    16. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    17. Varga, Levente & Tóth, Géza & Néda, Zoltán, 2017. "An improved radiation model and its applicability for understanding commuting patterns in Hungary," MPRA Paper 76806, University Library of Munich, Germany.
    18. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    19. Shen, Wei & Wynter, Laura, 2012. "A new one-level convex optimization approach for estimating origin–destination demand," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1535-1555.
    20. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.

    More about this item

    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:eee:jotrge:v:79:y:2019:i:c:23. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.