A Game Theoretic Solution for the Territory Sharing Problem in Social Taxi Networks
Bibliographic record
Abstract
Recent years have witnessed a surge of new methods by which commuters are accessing transportation modals. The use of social taxi networks is one of the most promising door-to-door transportation methods. In social taxi networks, commuters use their smart devices to contact social taxi service providers based on their geographical proximity and the listed fare prices. These taxis can be traditional taxis or privately owned vehicles. However, with the success of this new car hailing mechanism, there is an emergence of a number of problems. One of these problems is the territory allocation problem. i.e., the majority of social taxi drivers choose areas in which the most likely number of customers will be present. Therefore, they negatively impact each other's profit by offering a high supply of services. This paper develops a cooperative territory allocation approach such that, through negotiation between service providers, can reduce the conflict between taxi drivers. Game theory is used to formulate the territory sharing problem, which can be solved using bargaining-based solution model. The solution model is designed to correspond to a no regret game for which the outcome corresponds to a coarse correlated equilibrium. Simulation work results are provided to support the findings in this paper.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".