Comparing the cycling behaviours of Australian, Chinese and Colombian cyclists using a behavioural questionnaire paradigm

Cross-culturally approaching the cycling behaviour questionnaire (CBQ): Evidence from 19 countries

“…On the other, self-reported cycling crashes, even though previously argued as a multivariate outcome (e.g., a result of many factors), have been shown to be predictable by risky road behaviours of bicycle riders to a considerable extent (Li et al, 2021;Useche, Alonso, Sanmartin, Montoro, & Cendales, 2019b;Zheng, Ma, Li, & Cheng, 2019). Coherently, comparative tests have proved that those individuals who self-report having suffered at least one cycling crash during the last five years, also report significantly greater scores on the traffic violations (F1) and errors (F2) scales, as well as tending to be low-scorers on positive behaviours (F3).…”

“…Previous evidence on the CBQ factor structure is relatively consistent. In particular, violation (8 items) and error (15 items) dimensions have been endorsed by both exploratory (EFA), and confirmatory (CFA) factor analyses with relatively high factor loadings and percentages of variance explained (Li et al, 2021;Useche, Esteban, Alonso, & Montoro, 2021a;Useche, Montoro, Tomas, & Cendales, 2018a), while the positive behaviour scale can slightly vary in accordance with the context where it is applied (Useche, Alonso, Montoro, & Esteban, 2019a;Useche, Philippot, Ampe, Llamazares, & de Geus, 2021b).…”

“…Regarding the concurrent validity of the scale, the CBQ-based literature has found that risky riding behaviours are consistently associated with each other in a positive and significant way (Li et al, 2021;Zheng, Ma, Li, & Cheng, 2019). In addition, both errors and violations (i.e., regardless of behavioural intentionality) have a negative and also significant relationship to protective (or 'positive') road behaviours, which is the third factor of the scale (O'Hern, Estgfaeller, Stephens, & Useche, 2021; Useche, Alonso, Montoro, & Esteban, 2019a).…”

“…Also, evidence on demographic (mainly gender-and age-based) differences in both risky and positive cycling behaviours, has been coherently gathered in previous studies applying the CBQ. These studies have found that, overall, male cyclists of younger age (especially in the segment between 18 and 25 years) are those who usually report greater risky riding patterns and scarcer protective behavioural habits, as well as higher self-reported cycling crash rates (Li et al, 2021;O'Hern, Estgfaeller, Stephens, & Useche, 2021;Useche, Alonso, Sanmartin, Montoro, & Cendales, 2019b;Zheng, Ma, Li, & Cheng, 2019).…”

Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach

“…To fill the aforementioned research gaps, a novel GC-LSTM model with the attention mechanism has been proposed to predict short-run bike-sharing demand based on multi-source data. The station-level shared-bike demand is influenced by multiple complex factors [32,[38][39][40]. The data sets considered the multi-source heterogeneous information, including weather conditions, land-use data around the bike station, and users' personal information.…”