Traveller Recurrence and Inter- versus Intratraveller Speed Variability: Analysis with Bluetooth Data
Bibliographic record
Abstract
This paper proposes a linear mixed model of route speed distributions that separates the variability into an intertraveller component, consistent across days and time intervals for each recurrent traveller, and an intratraveller component representing uncertainty. The intratraveller variability corresponds to travel time uncertainty, while the total variability is typically captured by empirical measurements and used in travel time reliability assessments. The intratraveller and the total variability differ if there are systematic differences in speed between different recurrent travellers. The paper also investigates to what degree vehicles traversing a route during the morning or evening peak over multiple days are recurrent travellers. Using data from Bluetooth and Wifi sensors on 26 routes in Stockholm, Sweden, over a three-month period, we find that the traveller recurrence is higher towards the city in the morning peak and out from the city in the afternoon. Model estimation results show that the relative intratraveller variability is also significantly higher in the commute direction (towards the city in the morning and out from the city in the afternoon) and on routes with high congestion levels. The relations revealed in this paper may be used to estimate the relevant intratraveller variance based on the total variance and readily available route attributes. Without this correction, the costs associated with travel time variability may be overestimated.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".