Crowdsourcing Regional Coverage Balancing Method Based on Transfer Learning in Taxi Service
Bibliographic record
Abstract
With the in-depth study of taxi services, mobile crowdsourcing has become an emerging paradigm for solving location-based assignment tasks. However, only considering the task completion and regional coverage balance may reduce user’s satisfaction. Therefore, designing effective methods that not only address coverage balance but also take into account user’s satisfaction is a problem that needs to be addressed. We investigate the problem of regional coverage balance with users’ satisfaction and propose a service selection method based on transfer learning. This method comprises user’s trajectory prediction and service’s reputation to recommend services to users. In the user’s trajectory prediction part, an incentive mechanism is considered to ensure an even distribution of taxi services in each sub-region. Taking the service providers’ moving as the input, we design an adaptive ant colony algorithm that incorporates transfer learning to provide users with the optimal services. We validate the effectiveness of this method with datasets collected from the real world and compare the performance with existing regional coverage balance strategies. The experimental results show that user’s satisfaction increases by 53%, and the imbalance reduces by 24%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".