From Fixed Routes to Flexible Rides: Feasibility of On-Demand Transit Alternatives for Suburban Networks and A Case Study on Mississauga
Bibliographic record
Abstract
Public transit systems in suburban areas often face persistent challenges due to low and dispersed demand, which limits the efficiency and effectiveness of fixed-route transit (FRT). On-demand transit (ODT) has emerged as a flexible alternative, offering potential improvements in service quality and accessibility, but raising important questions regarding cost-effectiveness, operational scalability, and community impacts. This thesis develops and applies a simulation-based analytical framework to compare FRT and ODT across three suburban zones within the City of Mississauga: Meadowvale, Airport Corporate Centre, and Clarkson–Port Credit. Using data from the Transportation Tomorrow Survey (TTS), Automatic Passenger Counts (APC), and General Transit Feed Specification (GTFS), underutilized routes were identified and selected as candidates for replacement by ODT. Alternative mobility scenarios—FRT, ODT operated by the transit agency, and ODT operated by a transportation network company (TNC)—were modeled using PTV Visum, supported by zone-based origin–destination matrices and trip request generation. The evaluation focused on three dimensions: cost efficiency, level of service, and community impacts. Results show that ODT consistently outperforms FRT in terms of passenger experience, with lower journey times, primarily due to reduced wait times. However, this improvement comes with higher fleet requirements and increased vehicle-kilometers travelled, particularly under TNC-based models. Cost outcomes varied significantly across zones: ODT proved less costly than FRT in Meadowvale, comparable in the Airport Corporate Centre, but more expensive in Clarkson–Port Credit due to larger zone size and higher demand. Supplementary analyses further revealed that uniform service deployment across zones favors ODT by MiWay at current demand levels, while demand sensitivity experiments indicate a threshold beyond which FRT becomes more cost-effective. Overall, the findings highlight that ODT can complement or replace FRT in specific suburban contexts, but its financial and operational viability is highly context-dependent.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".