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Effects of data aggregation (buffer) techniques on bicycle volume estimation

Author

Listed:
  • Md Mintu Miah

    (University of California, Berkeley)

  • Stephen P. Mattingly

    (The University of Texas at Arlington)

  • Kate Kyung Hyun

    (The University of Texas at Arlington)

  • Joseph Broach

    (Portland State University)

  • Nathan McNeil

    (Portland State University)

  • Sirisha Kothuri

    (Portland State University)

Abstract

Researchers and practitioners commonly use a Direct Demand Model (DDM), which uses link, distance, and buffered variables (e.g., land use) to predict Annual Average Daily Bicycle Traffic (AADBT). Past studies deploy random buffer size combinations to find the best-fit variables for their specific DDMs. However, none of these studies seek to identify the best buffer types and sizes, and only a few past studies investigate the impacts of local characteristics on buffer type and size selection. Therefore, this study aims to determine the best buffer types and sizes and evaluate the impact of local characteristics on buffer type and size selection. To select the preferred buffer type and size, this study tests two types (Network and Euclidean) of buffers with seven unique sizes (0.1, 0.25, 0.50, 0.75, 1.0, 1.50, and 2.0 miles) and their combination for six different geographies (Portland, Eugene, Bend, Boulder, Charlotte, and Dallas). This study develops a total of 168 cross-validated (10 folds 5 repeats) generalized and city-specific Poisson regression models using emerging data sources (i.e., Strava, StreetLight) and contextual variables. Results recommend that a generalized model with the combination of Network and Euclidean buffers of multiple sizes provide the best prediction of AADBT, and Network buffers outperform Euclidean buffers. However, city-specific models with a single type and size of buffer sometimes outperform the generalized model. Network density determines the types and sizes of buffers. This research will help policymakers and modelers understand the sizes and types of buffers required to extract the variables to construct a DDM for AADBT estimations.

Suggested Citation

  • Md Mintu Miah & Stephen P. Mattingly & Kate Kyung Hyun & Joseph Broach & Nathan McNeil & Sirisha Kothuri, 2025. "Effects of data aggregation (buffer) techniques on bicycle volume estimation," Transportation, Springer, vol. 52(3), pages 1147-1190, June.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:3:d:10.1007_s11116-023-10452-7
    DOI: 10.1007/s11116-023-10452-7
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    References listed on IDEAS

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    1. Higuera-Mendieta, Diana & Uriza, Pablo Andrés & Cabrales, Sergio A. & Medaglia, Andrés L. & Guzman, Luis A. & Sarmiento, Olga L., 2021. "Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach," Journal of Transport Geography, Elsevier, vol. 94(C).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    4. repec:osf:socarx:q86sd_v1 is not listed on IDEAS
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