Microscopic Simulation of Transit Bus Emissions Under Transit Signal Priority and Alternative Technology
Bibliographic record
Abstract
The benefits of public transit in terms of reducing transportation-related greenhouse gas (GHG) and air pollutant emissions is a topic that needs little introduction. The literature swells with studies comparing per-passenger emissions in transit with emissions from single occupancy vehicles, stressing the importance of increasing transit ridership. We argue that increasing transit ridership is not sufficient to achieve meaningful emission reductions at a regional level. In Montreal, transit operations and technology are in dire need for improvement and additional reductions in transit emissions largely depend on the types of fuels used and driving conditions. We microsimulated the operations and emissions of transit buses along a busy transit corridor and quantified the effects of two different fuels (conventional diesel and compressed natural gas) as well as a set of driving conditions on emissions. Results indicate that compressed natural gas (CNG) reduces GHG emissions by 8-12% compared to conventional diesel. However, the benefits of switching from conventional diesel to CNG are less apparent when the road network is uncongested. As congestion levels rise, the emission reductions associated with CNG improve. We also investigated the effects of bus operations on emissions by applying several strategies such as transit signal priority (TSP), queue jumper lanes, and relocations of bus-stops. Results show that TSP alone can reduce emissions by 14% and when combined with improved technology; a reduction of 23% is achieved. The reduction benefits are even more apparent when other transit operational improvements are combined with TSP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".