Trajectory‐Based Safety Analysis of Electric Battery Taxis
Bibliographic record
Abstract
This paper utilizes trajectory data of electric taxis in Shenzhen to first study the driving characteristics of electric taxis from different indicators such as travel/driving speed, overspeed ratio, and overspeed amplitude. A combination weighting VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) evaluation method based on Nash equilibrium is constructed to comprehensively assess the operation safety of electric taxis in various spatiotemporal scenarios. The results show that the average driving speed and overspeed amplitude of battery electric taxis is 39.14 km/h and 22.78%, which is 1.27 and 1.72 times that of fuel taxis. The average overspeed ratio, average acceleration and deceleration and total acceleration and deceleration frequency are all more than twice those of fuel taxis. The average acceleration, deceleration, and idle time ratios of battery electric taxis are 0.27, 0.24 and 0.43, respectively, with a constant speed time ratio of only 0.06. The proposed combination weighting VIKOR evaluation method outperforms other evaluation methods in comprehensively considering data discreteness and correlation. Electric taxis have the highest operational safety on arterial roads, while the safety on minor arterial roads and local roads is relatively poor, especially on weekends. In all spatiotemporal scenarios, the overall operation safety of electric taxis is lower than that of fuel taxis. The research results can provide theoretical support for the formulation of effective measures and policies to reduce dangerous driving behavior of electric taxis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".