Adjusting Dwell Time for Paratransit Services
Bibliographic record
Abstract
Paratransit (door-to-door public transit services for people with disabilities) is a key element of the public transit system. This type of service can be very costly to operate, yet it is essential for social inclusion. The aim of this study was to develop a quantitative approach to estimate paratransit dwell times and improve trip scheduling. Dwell time is defined as the time required for a vehicle to stop to board or alight passengers. Data collected by the paratransit department of the Société de transport de Montréal (STM), the Montreal, Canada, public transit agency, between September 2014 and May 2018 was used to estimate a dwell time model. Over 5 million data points were analyzed using a multiple linear regression model. The model takes into consideration the type of vehicle used, passenger characteristics (ambulatory or wheelchair passenger, support person), the activity performed at the stop (boarding or alighting), the stop location, the time, day and month the trip took place, and the type of place (residential or non-residential) served. The results reveal all these variables have a significant impact on dwell times. Using these results, a method was developed to improve estimated dwell times in STM’s paratransit scheduling system. The new method was implemented on August 1, 2018. The difference between planned and actual travel times was measured, before and after the implementation of the new method. The results show the on-time performance of the service was improved which helped optimize routes and reduce associated operational costs.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".