Urban Arterial Route Travel Time Prediction Using Connected Vehicle Trajectories by Integrating Cloud and Edge Resources
Bibliographic record
Abstract
In recent years, vehicle trajectory data has become increasingly available from connected vehicles (CVs). CVs, acting as mobile sensors, can cover almost every intersection and provide enriched traffic information. In this context, this study proposed a novel statistical model-based method to predict arterial travel time using CV trajectories. The queue clearing time during green time is assumed to follow a Gamma distribution, and a maximized log-likelihood estimation (MLLE) is utilized to calculate related parameters. A hierarchical framework is further developed to improve both prediction accuracy and efficiency. First, the cloud (i.e., Traffic Management Center [TMC]) estimates the CV penetration rate (PR) and calibrates necessary model parameters offline. Then, the network edge (i.e., Mobile Edge Computing [MEC]), conducts the prediction online. A route in the City of Edmonton, Canada, is simulated to test the proposed method. The simulated CVs' trajectories are collected to estimate the PR and the cycle-by-cycle queue length at intersections. After that, the MEC at each intersection conducts travel time prediction with the parameters obtained from the TMC. The results achieved a low Root Mean Square Error (RMSE) of travel time prediction, averaging 0.9 minutes. Besides, the running time for a one-hour online prediction only costs 2.3 seconds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".