01663554

Component

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 study aims to address this limitation by sourcing peak-period non-motorized traffic counts at 6,342 locations across 20 U.S. metropolitan statistical areas to estimate spatial patterns of non-motorized traffic. Models were developed to estimate bicycle and pedestrian traffic during two-hour morning and afternoon peak periods at (1) intersections and (2) roadway, pedestrian, and bikeway network segments. The models have reasonable goodness of fit for both bicycle (adjusted R²= 0.46-0.61) and pedestrian (adjusted R²=0.47-0.74) traffic. A number of land-use and network variables were correlated with non-motorized traffic; for example, multimodal network density, presence of water bodies, offices, industrial areas, zero-car households, as well as bicycle and walking commute mode share. 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 numerous cities and regions in the U.S. Estimating models across cities allows for estimating non-motorized traffic in cities where counts are inadequate or unavailable. These 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.

This paper was sponsored by TRB committee ABJ35 Standing Committee on Highway Traffic Monitoring. Alternate title: Toward Generalizability in Direct-Demand Modeling: Exploratory Models from 20 U.S. Jurisdictions

18-02122

English

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

References

Cyclists; Peak periods; Pedestrians; Spatial analysis; Traffic surveillance; Traffic volume

Direct demand forecasting

Pedestrians and Bicyclists; Planning and Forecasting

Transportation Research Board Annual Meeting 2018 Paper #18-02122

TRIS, TRB, ATRI

Jan 8 2018 10:31AM