Exploring and Modeling the Level of Service of Public Transit in Urban Areas: An Application to the Greater Toronto and Hamilton Area (GTHA), Canada
Bibliographic record
Abstract
The design of policies for increasing public transit ridership is integral for strategies leading to sustainable transportation in large metropolitan areas. Assessing the availability of public transit (i.e. supply) as a viable mode of transportation can help in the design of such policies. In this respect, this study examines transit service intensity at the census tract level by assembling and analyzing a suitable GIS database for the Greater Toronto and Hamilton Area (GTHA). This research utilizes an improved version of the 'Local Index of Transit Availability' (LITA), which derives service levels based on the coverage, capacity, and frequency of the transit system. Transit service levels as measured by LITA, are linked to a number of socio-economic and spatial characteristics via a simultaneous auto-regressive (SAR) model. Results indicate that the core areas of municipalities were not necessarily well serviced by public transit. Suburban peripheral tracts and those adjacent to the shoreline were characterized by average transit service at best, and tracts adjacent to municipal borders indicated discontinuity in transit service. Furthermore, previous studies often overlooked the impact of spatial effects by utilizing the conventional OLS regression modeling technique. The use of the SAR model in this study corrected for that and enhanced the overall explanatory power of the modeled data. The estimation results indicate that variables such as population density, income, percentage of recent immigrants, percentage of young adults and percentage of elderly population are key variables to explain transit availability in the GTHA.
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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 | 0.001 |
| 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".