Quantifying Measurement Error in Arterial Travel Times Measured by Bluetooth Detectors
Bibliographic record
Abstract
Travel time is viewed by travelers and road managers as one of the key indicators of quality of service. Bluetooth detectors have recently emerged as a viable technology for the acquisition of vehicle travel times. Several studies have described the application of the technology to freeways and compared the measured travel times with the travel times obtained through other technologies. This paper examines the application of Bluetooth detectors to the acquisition of arterial travel times. The arterial environment is substantially more challenging than the freeway environment because of the frequent interruptions in the traffic flow caused by traffic signals. This paper examines the magnitude of errors in detection time and travel time measurement. It is not feasible to use field data to examine the measurement error because the error is inherent within the observations and cannot be separated. Consequently, a simulation framework is proposed to synthesize measurement errors for a range of arterial traffic conditions. The results show that the mean travel time error is essentially zero for all traffic conditions. However, the variance of the error varies as a function of the traffic conditions. Through multiple regression, the standard deviation of the travel time measurement error is modeled, and it is shown that under some conditions, the 95% confidence interval of this error may reach 25% of the true mean travel time. These results can be used to assess Bluetooth detector deployment plans and provide more insight into the reliability of arterial travel time measurements obtained from Bluetooth detectors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".