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Spatial interpolation of traffic counts based on origin–destination centrality

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  • Lowry, Michael

Abstract

This paper presents a new method to estimate Annual Average Daily Traffic. Often traffic volumes are estimated based on roadway characteristics, such as number of lanes, speed limit, and adjacent land use. However, for many communities, especially small communities, these attributes are uniform across roadway types and therefore unable to adequately explain observed variation in traffic volumes. The new method uses novel explanatory variables that are intrinsically derived through a modified form of centrality, a network analysis metric that quantifies the topological importance of a link in a network. The new approach requires minimal data collection and is easily executed using a geographic information system. The case study showed high quality results (out-of-sample validation R2=0.95). The new approach can be used for various activities related to transportation planning and investment decision making.

Suggested Citation

  • Lowry, Michael, 2014. "Spatial interpolation of traffic counts based on origin–destination centrality," Journal of Transport Geography, Elsevier, vol. 36(C), pages 98-105.
    • Handle: RePEc:eee:jotrge:v:36:y:2014:i:c:p:98-105
    DOI: 10.1016/j.jtrangeo.2014.03.007
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    Cited by:

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    2. Hyun-ho Chang & Seung-hoon Cheon, 2019. "The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments," Transportation, Springer, vol. 46(3), pages 1011-1032, June.
    3. Kristin Carlson & Alireza Ermagun & Brendan Murphy & Andrew Owen & David Levinson, 2017. "Safety in Numbers and Safety in Congestion for Bicyclists and Motorists at Urban Intersections," Working Papers 000165, University of Minnesota: Nexus Research Group.
    4. Fu, Miao & Kelly, J. Andrew & Clinch, J. Peter, 2017. "Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results," Journal of Transport Geography, Elsevier, vol. 58(C), pages 186-195.
    5. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    6. Brendan Murphy & David Levinson & Andrew Owen, 2015. "Accessibility and Centrality Based Estimation of Urban Pedestrian Activity," Working Papers 000143, University of Minnesota: Nexus Research Group.
    7. Sarlas, Georgios & Páez, Antonio & Axhausen, Kay W., 2020. "Betweenness-accessibility: Estimating impacts of accessibility on networks," Journal of Transport Geography, Elsevier, vol. 84(C).
    8. Crispin H. V. Cooper & Ian Harvey & Scott Orford & Alain J. F. Chiaradia, 2021. "Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows," Transportation, Springer, vol. 48(2), pages 643-672, April.
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