Citation

Piras F, Sottile E, Tuveri G, Meloni I. Transp. Res. A Policy Pract. 2021; 154: 108-128.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.tra.2021.10.003

PMID

unavailable

Abstract

Although recent studies have recognised that psychosocial factors could affect the choice to bike to work, most have tended to focus only on the statistical significance of psychosocial variables, often making no attempt to analyse the magnitude of their effect before suggesting policy strategies based on these variables in too general a manner. Additionally, different studies have failed to distinguish between the choice to commute by bike and cycle for non-commuting purposes, mixing their results. Given the above discussion, the current studyaims at understandingand interpreting the relationship between the psychosocial factors related to bike use, commute mode choice and cycling for non-commuting purposes. To analyse the relationship among all these choice dimensions, we specified and estimated an integrated choice and latent variable (ICLV) model using a dataset drawn from a survey conducted in Sardinia (Italy). The model estimation highlights several very interesting aspects, some of which confirm the findings of previous studies, while others are new contributions to the literature. First, we find that the perception of the benefits of cycling and that of bike comfort positively influence the probability of using a bike for commuting and non-commuting purposes, albeit in different ways. Another important point is how modelling results can be employed to develop effective strategies for promoting cycling. We show that the implementation of structural measures aimed at reducing travel time may only be effective for commuters who travel more than 5 km, while the success of behavioural measures seems to be independent of distance. At the same time, by running different test scenarios, we indicate how to increase the efficacy of behavioural measures depending on the target population.

Language: en

Keywords

Cycling behaviour; ICLV model; Policy scenarios; Psychological factors; Soft measures