Citation

Lanza K, Burford K, Ganzar LA. J. Transp. Geogr. 2022; 98: e103269.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.jtrangeo.2021.103269

PMID

unavailable

Abstract

Cities are investing in active transportation networks, yet little is known about travel behavior of different non-vehicle modes in the presence of multiple types of transportation infrastructure. At two sites in Austin, Texas, USA, with a cycle track, sidewalk, and street in parallel, we determined where different modes traveled and the likelihood of crossing from one infrastructure to another and of using "recommended" infrastructure--defined as sidewalk for walkers, dog walkers, and runners and bike lane with traffic for cyclists, e-scooter riders, and other wheeled micromobility users. We created the Mobility Behavior Tool to conduct observations of travelers on 50-m-long, straight segments of the parallel infrastructure at sites for one-hour sampling periods (n = 16 periods) in April-May 2021. In our sample (n = 2245 individuals), we observed that 20% of travelers crossed into other infrastructure and 35.8% used not recommended infrastructure. Using binomial logistic regression, we found statistically significant odds ratios for dog walkers (3.17), cyclists (0.56), e-scooter riders (1.85), and other micromobility users (3.06) crossing into other infrastructure compared to walkers (p < 0.001), and no significant differences between runners (0.50) and walkers (p > 0.05). A second model predicted statistically significant odds ratios for dog walkers (1.74), runners (3.88), e-scooter riders (1.38), and other micromobility users (2.74) using not recommended infrastructure compared to walkers (p < 0.001), and no significant differences between cyclists (0.86) and walkers (p > 0.05). Potential reasons for differences in travel behavior by mode include levels of understanding of local regulations and situational awareness, infrastructure preferences, frequency of passing, and propensity for weaving, swerving, and subversive behavior. Municipalities should consider how infrastructure design influences travel behavior and the travel efficiency, comfort, and safety of all modes.

Language: en

Keywords

Active travel; Cyclist; E-scooter; Mobility; Pedestrian; Safe design