Anomalous Trajectory Detection Using Masked Autoregressive Flow Considering Route Choice Probability
Bibliographic record
Abstract
Taxis play a critical role in public traffic systems, and they deliver myriad travelers with convenient service due to temporal-spatial availability. However, anomalous trajectories such as trip fraud often occur due to greedy drivers. In this study, we propose an anomalous trajectory detection method that incorporates Route Choice analysis into Masked Autoregressive Flow, named MAFRC-ATD. The MAFRC-ATD integrates data-driven and model-based methods. First, we divide the urban traffic network into small grids and represent subtrajectories with a sequence of grids. Second, based on the subtrajectories, we employ the MAFRC-ATD model to calculate the anomaly score of each trajectory. Third, according to the anomaly score, we can identify the anomalous trajectories and distinguish between intentionally and unintentionally anomalous. Finally, we evaluate our method with a real-world dataset in Porto, Portugal. The experiment demonstrates that the MAFRC-ATD can effectively discover anomalous trajectories and can identify the unintentional detours due to traffic congestion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".