Understanding Electric Bikers’ Red-Light Running Behavior: Predictive Utility of Theory of Planned Behavior vs Prototype Willingness Model
Bibliographic record
Abstract
To date, electric bikers’ (e-bikers’) red-light running (RLR) behavior is often viewed as one of the main contributors to e-bike-related accidents, especially for traffic scenarios with high e-bike ridership. In this paper, we aim to understand e-bikers’ RLR behavior based on structural equation modeling. Specifically, the predictive utility of the theory of planned behavior (TPB), prototype willingness model (PWM), and their combined form, TPB-PWM model, is used to analyze e-bikers’ RLR behavior, and a comparison analysis is made. The analyses of the three modeling approaches are based on the survey data collected from two online questionnaires, where more than 1,035 participant-completed questionnaires are received. The main findings in this paper are as follows: (i) Both PWM and TPB-PWM models could work better in characterizing e-bikers’ RLR behavior than the TPB model. The former two models explain more than 80% (81.3% and 81.4%, respectively) of the variance in e-bikers’ RLR behavior, which is remarkably higher than that of the TPB model (only 74.3%). (ii) It is also revealed that RLR willingness contributes more on influencing the RLR behavior than RLR intention, which implies that such behavior is dominated by social reactive decision-making rather than the reasoned one. (iii) Among the examined psychological factors, attitude, perceived behavioral control, past behavior, prototype perceptions (favorability and similarity), RLR intention, and RLR willingness were the crucial predictors of e-bikers’ RLR behavior. Our findings also support designing of more effective behavior-change interventions to better target e-bikers’ RLR behavior by considering the influence of the identified psychological factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".