Assessing the Built Environment of Light Rail Transit Stations to Encourage Active Transportation: A Multi‐Criteria <scp>GIS</scp>‐Based Analysis
Bibliographic record
Abstract
ABSTRACT Active transportation (AT) has become increasingly important due to its positive impacts on public health, reducing emissions, and promoting sustainable urban environments. This study examines how Light Rail Transit (LRT) stations in Edmonton, Canada, contribute to encouraging AT by evaluating nine criteria within three key categories: infrastructure availability and connectivity, safety and security, and comfort and aesthetics. The Criteria Importance Through Inter‐criteria Correlation (CRITIC) method was used to determine the weight of each criterion, followed by the Weighted Sum Model (WSM) to rank stations, calculate scores, and identify areas in need of enhancement. An equity analysis was conducted to evaluate whether the distribution of LRT stations disproportionately benefits specific demographic groups. The findings reveal that 75% of sidewalks within a 400‐m buffer were of good quality, yet 12% of the area was unsuitable for walking. Moreover, 74% of streets within an 800‐m buffer lacked designated bike lanes. The distribution of intersection types within station areas included 46% cross intersections, 38% T‐intersections, and 16% cul‐de‐sacs. Furthermore, station scores, which range from 0 to 1, varied significantly, with Downtown stations such as Bay Enterprise Square (0.873) and Central (0.778) ranked highest, while suburban stations like NAIT Blatchford Market Station (0.044) and Davies (0.124) ranked much lower. This study highlights the necessity of focused improvements in AT infrastructure, particularly in outlying areas, to enhance the overall effectiveness of the LRT network. These insights are valuable for urban planners seeking to develop a more accessible and sustainable transportation system.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".