Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya
Bibliographic record
Abstract
Urban traffic congestion has become a global problem and has garnered special importance in recent years in the transportation sector, especially in taxi markets. To unlock the potential of Internet of Vehicles (IoV) in Intelligent Transportation Systems (ITS), it was vital to make efficiency measurements. In this study, Distance Formula was built to calculate distances by GPS data based on mathematical equations, and Motorcade-Sharing (MS) Model was proposed to improve the efficiency of collaborative vehicles. The experimental data of 2191 GPS-equipped taxis in Sanya of China was adopted to make comparisons between original results and modelled results. Measurement results showed that MS Model had 10.54% more leisure taxis, reduced 5 overdriving taxis, and saved 33.73% running distance in total compared to the original. This indicated that the application of MS Model could not only alleviate urban traffic congestion but also optimize urban taxi markets, and it has a bright future in the field of taxi and other collaborative vehicles. Future directions could be improving MS Model and expanding data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".