Bayesian spatial modeling for speeding likelihood using floating car trajectories
Bibliographic record
Abstract
Speeding likelihood is usually used to measure drivers' propensity of committing speeding. Albeit some studies have analyzed speeding likelihood, most of them are inadequate in considering spatial effects when analyzing speeding behaviors on urban road networks. This study aims to fill this knowledge gap by modeling speeding likelihood with spatial models and then evaluate the influence of contributing factors. The percent of speeding observations (PSO) is adopted to represent the speeding likelihood. The speeding behaviors and PSO of each floating car (i.e., taxi) are extracted from the GPS trajectories in Chengdu, China. PSO is modeled by several Bayesian beta general linear models with spatial effects, namely the beta model, beta logit-normal model, beta intrinsic conditional autoregressive (ICAR) model, beta Besag-York-Mollié (BYM) model, and beta BYM2 model. Results show that the beta BYM2 model performs better than other models in terms of data-fitting. According to the estimates from the beta BYM2, spatial correlation is the main reason for the model variability. The roads with more lanes and roads linked by elevated roads are found to increase the speeding likelihood, while higher speed limits, intersection density, traffic congestion, and roadside parking are associated with lower speeding likelihood. These findings provide valuable insights for designing effective anti-speeding countermeasures on urban road networks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".