Impacts of the Introduction of an Express Transit Service in Waterloo Region
Bibliographic record
Abstract
For more than a century, public transportation has played a significant role in society. Transit \nagencies, like other service industries, are intent on improving their quality of service so as to \nincrease transit ridership and attract passengers from other modes. In recent years \ntransportation technologies have been improved which increase safety, mobility for people \nand goods, and reduce Green House Gas (GHG) emissions. An evaluation of the impacts of \nthese operational and technological advancements is required for transit agencies to capture \nthe potential benefits for their systems. \nThe Region Municipality of Waterloo (RMOW), a mid-size region in Ontario has \nimplemented an express transit service (iXpress) in Sept, 2005. The service has longer \ndistances between stops and incorporates advanced technologies. The goal is to increase \ntransit ridership and, as a result, to reduce GHG emissions. \nThis research has been conducted to study the iXpress service and to develop several \nmethods to determine the impacts of high speed transit service on passenger attraction, \noperational efficiency, and regional air quality. In this research, the change in total cost of \ntravel between origin destination pairs is correlated to changes in observed ridership. \nFurther, several surveys were conducted in the RMOW to evaluate the travel pattern changes \nof residents who switched from other modes to iXpress. Based on fuel consumption data, a \nmodel of GHG emissions as a function of route and vehicle characteristics has been \ndeveloped to capture the operational impacts of a new iXpress service. \nThe iXpress service of Grand River Transit (GRT) has been successful in attracting riders \ndespite delays in technology implementation. The cost analysis presented in this research \nshows that the introduction of iXpress resulted in approximately 30% reduction in overall \ncost of travel by transit. As a result, ridership (boardings) has increased by 11% and 46% in the northern and southern sections of the iXpress service area respectively, while accounting \nfor overall growth in the system. An analysis of travel patterns and mode shifts suggest that \ntravelers switching from auto mode to iXpress have resulted in annualized reduction of \napproximately 530 tonnes of GHG. A fuel consumption analysis indicates that buses on the \niXpress route have an average fuel consumption rate of 0.54 L/km while, buses serving local \nroute consumes fuel of a rate of 0.62 L/km.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".