Multi-City, National-Scale Direct-Demand Models of Peak-Period Bicycle and Pedestrian Traffic

Direct-demand models are potentially useful tools for generating spatial estimates of pedestrian and bicycle traffic volumes to help plan for active transport facilities and target infrastructure investments. To date, most direct-demand models are city-specific; lack of spatial and temporal coverage of traffic counts on a national scale has precluded generalizability and transferability of city-specific models. This project aims to address this limitation by sourcing peak-period non-motorized traffic counts at 6,342 locations across 20 U.S. metropolitan statistical areas (MSAs) to estimate spatial patterns of bicycle and pedestrian traffic. The authors developed models to estimate bicycle and pedestrians traffic at intersections and segments during two-hour morning and afternoon peak periods. The models have reasonable goodness of fit for both bicycle traffic (adjusted R²: 0.19 to 0.56) and pedestrian traffic (adjusted R²: 0.45 to 0.72). The authors found a number of land-use and network variables that were correlated with bicycle and pedestrian traffic, for example, multimodal network density, presence of water bodies, nearby offices, industrial area, zero-car households, as well as bicycle and walking commuting mode shares. Intersection density is also a strong predictor for pedestrian volume; off-street and on-street bicycle facilities are strong predictors of bicycle volume. The count data have good spatial and temporal coverage across a variety of cities and regions in the US. Estimating models across cities allows for estimating non-motorized traffic in cities where counts are inadequate or unavailable with higher reliability. The models could be used to inform decisions on where to locate non-motorized transportation facilities and to assess exposure to accidents with motor vehicles or other environmental hazards.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Maps; References; Tables;
  • Pagination: 35p

Subject/Index Terms

Filing Info

  • Accession Number: 01663155
  • Record Type: Publication
  • Contract Numbers: DTRT13-G-UTC33
  • Created Date: Mar 20 2018 5:09PM