A smart solution to smart cities: An advanced traveler general information system
Bibliographic record
Abstract
Urban travel generally have significant adverse health and environmental impacts. Research studies found that transport users are becoming more aware of the necessity to reduce carbon emissions, however, they are not willing to change their travel behavior mainly because of the inability to perceive the associated social and private costs. In practice, fuel consumption and emissions costs have a minor effect, if any, on travel-choice behavior. The fundamental reasons are that drivers do not perceive fuel consumption costs as out-of-pocket costs and they usually do not have intelligible information about the substantial health-related implications of emissions. These costs can be perceived using an information communication system, called Advanced Traveler Information System (ATGIS). The system calculates, estimates and provides information regarding effects to different user groups. By raising the driver awareness regarding the social and environmental consequences of travel, a carefully designed scheme can provide considerable benefits for large metropolitan areas, where small positive changes in travel behavior could save millions of dollars in time, fuel and emissions costs. In this talk, I examine the influence of offering such information on individuals??? travel decisions examining real-life driving patterns and a survey we conducted in Montreal, Canada. We found that urban travelers are generally unaware of the energy and environmental footprints of their travel. Over 80% are unable to estimate their fuel consumption, GHG social costs and healthrelated air pollution costs across different travel modes. A personalized information system could fundamentally influence travel decisions especially route choices. This research thread will also play an important role in efforts to estimate transport-related greenhouse gas (GHG) emissions from major metropolitan areas and to calculate annual national GHG inventory to meet UNFCCC obligations. Moreover, it would be beneficial to provincial governments and/ or environmental agencies, as it indicates the feasibility of a low cost energy management system that saves energy, preserves environment and induces sustainable energy consumption decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".