Assessing the Costs for Hybrid versus Regular Transit Buses
Bibliographic record
Abstract
Fuel costs are a significant portion of transit agency budgets. Hybrid buses offer an attractive option and have the potential to reduce operating costs for agencies significantly. Hybrid technology has been available in the transit market for some time. As of 2009, there are more than 1,200 hybrid buses in regular service in North America in more than 40 transit agencies (Transport Canada 2011). The majority of these buses are regular 40 ft buses, although some smaller (20 ft) shuttle buses and larger articulated (60 ft) buses are also in service. The transit agency in New York, New York has approximately 1,000 hybrid vehicles as of 2009 (Maynard 2009) and Toronto, Canada has approximately 33 percent (Transport Canada 2011). The main reasons agencies consider hybrid transit vehicles are fuel savings and reduced emissions. Hybrid electric buses offer an attractive option and have the potential to reduce operating costs for transit agencies significantly. Wayne et al. (2009) estimated that use of diesel-electric hybrid buses in 15 percent of the US transit fleet could reduce fuel consumption by 50.7 million gallons of diesel annually. However, purchase of hybrid transit buses requires a significant investment. In addition, early estimates of cost savings may not have materialized to the extent transit agencies expected. Other costs, such as the cost of replacing batteries and reduced maintenance, are also issues that have not been substantiated with independent studies. To justify the expenditure, agencies require more quantitative information about the likely fuel economy, maintenance, and other costs for hybrid buses. This technical brief summarizes information about the costs and benefits that have been attributed to use of hybrid transit buses as found in the literature. Results from a demonstration project that compared fuel economy and emissions for 12 hybrid buses and 7 control buses for the transit agency for Ames, Iowa and Iowa State University, CyRide, were also included.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".