Citation

Park Y, Akar G. J. Transp. Geogr. 2019; 74: 191-200.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.jtrangeo.2018.11.013

PMID

unavailable

Abstract

Bicyclists often deviate from the shortest possible routes and take detours in search of more pleasant riding conditions. The extent of detours and the factors affecting bicyclists to ride excess distances have not yet been fully explored. This study aims to measure and analyze the detour extent of utilitarian bicycle trips and their relationships with the route-level environmental components using data collected from individual bicyclists' smartphone GPS in Columbus, Ohio. Comparing the chosen routes with their shortest counterparts, we calculate two detour indices (a distanced-based index and an area-based index) and provide a comparative analysis of built environment attributes for low, moderate, and high levels of detours. We then estimate multilevel mixed-effect generalized linear regression models to identify the contribution of built-environment characteristics to such detours while accounting for individual heterogeneity. We find that most bicycle trips (91.1%) include a detour and are 13.5% longer on average than their shortest alternatives with large variations. Detour degrees are higher for long-distance trips and for peak-period trips. We find that bicyclists choose routes with smaller shares of commercial and single-family land-uses and low levels of land-use diversity. Longer detours are positively associated with street greenery. We find that sparse bicycle facilities and high-speed limits are strong contributors to bicyclists' detour decisions, while multilevel mixed-effect linear regression models further present significant heterogeneity in bicyclists' responses to some environmental attributes. The area-based detour index performs better in explaining the relationships between land-use features and detour degrees.

Language: en

Keywords

Bicycle detouring; Bicycle route preference; Environmental correlates; Multilevel mixed-effect generalized linear regression; Smartphone GPS application