Environmental and Energy Implications of Emerging Technologies and Trends in Road Transport
Bibliographic record
Abstract
This dissertation investigates the environmental impacts of emerging transportation technologies and trends, including automated vehicles (AVs), electric vehicles, transportation network companies (TNCs), and off-peak delivery (OPD) programs in urban settings. It identifies and quantifies the potential shifts in greenhouse gas (GHG) and air pollutant emissions attributable to these innovations. Revealed travel survey data collected in the Greater Toronto and Hamilton Area (GTHA) is used in parallel with a mixed integer linear programming (MILP) optimization model to quantify the kilometers traveled associated with various forms of AV deployment. Research results reveal that privately owned automated vehicles (PAVs) could affect public transit ridership and significantly reduce household vehicle ownership, but might increase total vehicle kilometers traveled (VKT) and GHG emissions, underscoring the need for policies that limit unoccupied vehicle travel and encourage shared vehicle use. An analysis of electric shared automated vehicles (SAVs) presents an intricate balance between vehicle ownership and sharing, increased mileage, and GHG emissions. Using another MILP model, the study underlines the significance of eco-charging—optimizing charging schedules to align with low-emission periods of the electricity grid—as a viable strategy for reducing the operational emissions of electric SAVs. However, it cautions that the gains from reduced vehicle ownership and eco-charging might be offset by the additional vehicle mileage, particularly with higher levels of vehicle sharing. A quantification of GHG emissions from TNC operations using comprehensive real-world data, reveals how strategies such as reducing deadheading, promoting vehicle pooling, and accelerating electrification could play a role in limiting the environmental impact of ride-hailing services. Finally, an examination of OPD programs' environmental impacts through regional travel demand simulations in the GTHA, demonstrates that while such initiatives can reduce vehicular hours traveled (VHT), they might inadvertently increase VKT due to changes in freight routing and induced passenger demand, with a varied impact on GHG and air pollutants emissions. By providing a nuanced understanding of the environmental impacts of these emerging technologies and trends, this dissertation offers valuable insights for policymakers and transportation planners aiming to promote sustainable transportation in the face of evolving urban transport dynamics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".