Autonomous Vehicles: Understanding Adoption Potential in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
Many Autonomous Vehicle (AV) researchers have done surveys and interviews to assess the relationships between some population or land use characteristics and people’s intention to adopt private AVs (PAVs) and shared AVs (SAVs). Their findings provide clues on where in the urban area, there exists higher or lower PAV or SAV adoption potential. However, no scholar has created a model to index or score the PAV and SAV adoption potential and map it for a region. This thesis addresses these gaps. Through the literature review, variables that are strongly associated with a PAV or SAV adoption, and their index weights are identified. Then, using ArcMap 10.5.1, the PAV and SAV adoption potential is mapped out at the census tract level in the study area: the Greater Toronto and Hamilton Area (GTHA). Findings are then generated to inform planning and policy development. \n\tSome highlights of the findings are as follows. Frist, The areas with high PAV adoption potential tends to be in the inner suburb, while the areas with low PAV adoption potential are often in the central city (Toronto). All of the areas with high SAV adoption potential are in the central city (Toronto), and most of the areas with low SAV adoption potential are in the outer suburb. Second, each of the four types of areas has some special land use characteristics. Third, in the park-and-ride and kiss-and-ride service areas of the GO train stations in the GTHA overall, there is discernably lower PAV adoption potential, and obviously higher SAV adoption potential. However, the overall potential of PAV and SAV adoption varies from line to line, and from station to station. Last but not least, changing the price of SAVs would unlikely change the PAV and SAV adoption potential in an area. \n\tAll of the findings expand the understanding of planners and policy makers in identifying areas with high or low PAV or SAV adoption potential. Knowing these locations and their characteristics would help planners and policy makers develop their plans and policies on PAVs and SAVs. The findings on the GO train services provide some background knowledge for Metrolinx staff to prepare a redesign of its parking spaces for GO train passengers, and the use of SAVs to help its passengers to access its GO train stations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".