  Data and models of non-motorized traffic on multiuse urban trails are needed
to improve planning and management of urban transportation systems. Negative
binomial regression models are appropriate and useful when dependent variables
are non-negative integers with over-dispersion like traffic counts. This paper
presents eight negative binomial models for estimating urban trail traffic
using 1,898 daily mixed-mode traffic counts from active infrared monitors at
six locations in Minneapolis, MN. Our models include up to 10 independent
variables that represent socio-demographic, built environment, weather, and
temporal characteristics. A general model can be used to estimate traffic at
locations where traffic has not been monitored. A six-location model with dummy
variables for each monitoring site rather than neighborhood specific variables
can be used to estimate traffic at existing locations when counts from monitors
are not available. Six trail-specific models are appropriate for estimating
variation in traffic in response to variations in weather and day of week.
Validation results indicate negative binomial models outperform models
estimated by ordinary least squares regression. These new models estimate
traffic within approximately 16.3% error, on average, which is reasonable for
planning and management purposes.
