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Citation |
Hankey S, Lindsey G. Transp. Res. Rec. 2016; 2586: 48-58. |
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Copyright |
(Copyright © 2016, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
Transportation planners and engineers need better spatial estimates of walking and cycling traffic to assess exposure to hazards, evaluate infrastructure investments, and locate facilities. Facility-demand models are potentially useful for generating spatial estimates of traffic volumes. Few facility-demand models have explored trade-offs between fully specified (i.e., exploratory) models and reduced-form models easily applied in the field. Presented are facility-demand models based on peak period (4 to 6 p.m.) counts of pedestrian and bicycle traffic in Minneapolis, Minnesota. The count database (n = 954 observations; 471 locations) has sufficient spatial density (~3 locations km−2) to develop spatially resolved models (i.e., ~100-m resolution). The modeling approach employs a stepwise linear regression method allowing for varying the spatial scale of independent (land use and transportation) variables. Compared were fully specified (statistically optimal) models and supervised, reduced-form models that included fewer variables based on theoretical validity. Reduced-form core models had modest goodness of fit (adjusted R2: ~.5) and included independent variables with large (industrial area and population density) and small (bicycle facilities, retail area, open space, transit stops) spatial scales. Also developed were reduced-form, time-averaged models for a subset of count sites having multiple observations (n = 84). With the use of reduced-form models (independent variables ranged from four to nine among models), block-level traffic estimates were generated (n = 13,886). Language: en |