Demand for shared mobility to replace private mobility using connected\n and automated vehicles
Bibliographic record
Abstract
We examine how introduction of Shared Connected and Automated vehicles\n(SCAVs) as a new mobility mode could affect travel demand, welfare, as well as\ntraffic congestion in the network. To do so, we adapt an agent-based day-to-day\nadjustment process and develop a central dispatching system, which is\nimplemented on an in-house traffic microsimulator. We consider a two-sided\nmarket in which demand and SCAV fleet size change endogenously. For dispatching\nSCAV fleet size, we take changing traffic conditions into account. There are\ntwo available transport modes: private Connected Automated Vehicles (CAVs) and\nSCAVs. The designed system is applied on downtown Toronto network using real\ndata. The results show that demand of SCAVs goes up by 43 per cent over seven\nstudy days from 670 trips on the first day to 959 trips on the seventh day.\nWhereas, there is a 10 per cent reduction in private CAV demand from 2807 trips\nto 2518 trips during the same duration. Moreover, total travel time of the\nnetwork goes down by seven per cent indicating that traffic congestion was\nreduced in the network.\n
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".