Taxi Dispatch and AEV Management in AEV Taxi Services
Bibliographic record
Abstract
Internet-based taxi service not only facilitates passenger travel but also effectively improves the utilization of transportation resources. Autonomous electric vehicle (AEV), as a future-oriented form of transportation, is more environmentally friendly and intelligent as it does not require a driver and uses green energy to fulfill the trip. Using idle AEVs for taxi service is an effective way to realize smart city transportation in the future. With the aim of supporting AEVs to provide taxi services, AEV Taxi Management and Dispatching Module (ATMDM), is proposed to support AEV management and scheduling in the AEV Taxi Service (ATS) system. By efficiently maintaining AEV status and information, ATMDM is able to realize the management of multiple AEVs as well as can also provide taxi service by matching orders with appropriate AEVs and providing route information based on the received trip requests. Compared with traditional taxi dispatching solutions, ATMDM fully considers the operational characteristics of AEV with better adaptability, which shows insight for the future means of transportation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".