Employing Geographic Information Systems in Analyzing Pedestrian Accessibility to Public Bus Stops in Halifax
Bibliographic record
Abstract
In this work, we analyzed the locations of existing bus stops in the Halifax Regional Municipality (HRM) area and then calculated the walking distance in time in order to understand the accessibility for pedestrians. In doing so, we employed geographic information systems (e.g., spatial and network analysis tools) to generate accessibility models from bus stops at 5, 10, 15, and 20 min of walking distance. After generating the service area of bus stops on maps, we overlaid socioeconomic variables (i.e., age group, income, and bus-stop accessibility) to better understand the HRM public transport network. We found that population density was an important consideration in providing the number of bus stops in specific communities, which may be related to the facilities offered by the urban hierarchy. Furthermore, we established a relationship between transit stops and accessibility for people in age groups of between 0 and 19 (e.g., school-going children) and 65+ (i.e., the older population) so as to obtain an understanding of the time required for them to access the bus stops on foot. Overall, 77% of the population in the HRM was served by public transport within 5 min of walking; however, for the 65+ cohort, this number was higher (82%). A significant amount of the young population (23%) was served over longer distances than 10′ of walking. We summarize that our findings are critical for planners, practitioners, and researchers in order for them to understand the present transit system in place based on accessibility to the bus stops within walking distance.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".