Recommending Profitable Taxi Travel Routes based on Big Taxi Trajectory Data
Bibliographic record
Abstract
Recommending potential profitable routes to reduce the cruising distance of taxis is an active research topic. This thesis first introduces a temporal probability grid network generated from taxi trajectories, where each grid has been assigned two temporal properties: probability and capacity. Two profitable route recommendation algorithms are proposed: the Shortest Expected Cruising Route (SECR) and Adaptive Shortest Expected Cruising Route (ASECR) algorithms. The ASECR algorithm is an extension of SECR that updates profitable routes constantly, and renews the temporal probability grid network dynamically. To handle large amounts of trajectory data and improve the efficiency of constantly updating the routes, a new data structure kdS-tree with a MapReduce model is proposed. Case studies compare the time and distance performances of the ASECR and SECR algorithms with two other methods, i.e. the LCP method and the baseline method. These comparisons demonstrate the effectiveness and efficiency of the two proposed algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".