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Steve Hankey

Publications and source records attributed to Steve Hankey.

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How Infrastructure and Streetscape Shape E-Scooter Route Choice: Evidence from Washington, DC

E-scooters have emerged as an important micromobility mode for short urban trips, yet evidence on route choice behavior remains limited. This study examines e-scooter route choice in Washington, DC using GPS trajectory data and a Path Size Logit model. In addition to roadway and infrastructure characteristics, the model incorporates visual streetscape features extracted from Google Street View imagery using computer vision techniques. The results show that the effectiveness of cycling infrastructure depends strongly on roadway context. On major roads, only protected bicycle facilities significantly increase route attractiveness, whereas on minor roads both protected and designated lanes provide utility gains. Sidewalks constitute the most frequently used riding environment, yet only asphalt-paved sidewalks are associated with positive utility, suggesting that sidewalk riding may reflect the absence of attractive on-street alternatives rather than a preference for pedestrian infrastructure. Tree coverage, particularly during summer, as well as building and wall coverage, are positively associated with route choice. Likelihood ratio tests and value-of-distance analysis indicate that roadway infrastructure exerts a stronger influence on route choice than visual streetscape features, although the latter provide additional explanatory power. These findings support targeted infrastructure investment and the integration of streetscape improvements as a complementary strategy for enhancing micromobility route attractiveness.

stat.AP

Estimating Mixed-Mode Urban Trail Traffic Using Negative Binomial Regression Models

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.

physics.soc-ph