Influence of pedestrianization on travel behavior changes: a case study of Montreal
Bibliographic record
Abstract
Introduction Pedestrianization promotes active modes of transportation and provides many benefits related to the environment, economy, health, and mobility. Nonetheless, it has often faced widespread opposition from residents and business owners. Therefore, it is essential to examine the effectiveness of pedestrianization programs (i.e., transforming streets into car-free zones). Objectives This study investigates the influence of different variables on pedestrianization effectiveness in Montreal, Canada. The effectiveness of pedestrianization is evaluated in terms of frequency and duration of walking trips, duration spent in street shops, and route change. Methods An online survey was distributed in Montreal. A powerful machine learning method (XGBoost) is used for modeling, and two interpretation techniques (SHapley Additive exPlanations and Partial Dependence Plots) are used to interpret the results of XGBoost. The performance of the developed interpretable machine learning approach is compared with Ordinal Logistic Regression. Results The top variables impacting the effectiveness of pedestrianization are the level of agreement in redoing pedestrianization projects every year, the opinion on the influence of pedestrianization on individual mobility, the level of satisfaction with urban furniture of pedestrian streets, the level of satisfaction with the attractiveness of pedestrian streets, and age. Conclusions Positive attitudes toward pedestrianization and active travel satisfaction are the top determining factors in supporting pedestrianization, and they play a vital role in the effectiveness of pedestrianization programs. Therefore, improving the urban furniture and the attractiveness of car-free streets increases the effectiveness of these projects. Among socio-demographics, age is the top variable, and pedestrianization is the most effective for individuals between 33 and 45 years. Accordingly, policymakers should prioritize the implementation of such projects in areas with the highest concentration of this age group to maximize their effectiveness. Further, travel behavior changes are highly dependent on the trip purpose and the built environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".