Bluetooth Sensors Data Versus GPS-Based Data, Measuring Travel Time Reliability on Freight Transportation Corridors in the City of Calgary, Alberta, A Comparative Study
Bibliographic record
Abstract
Presently most transportation departments use inductive loops, traffic cameras or stationary sensors to measure travel time and speeds, and thus to estimate travel time reliability. Although, these traditional systems are proven techniques of collecting traffic data, they each also have a couple of shortcomings including systems installation costs, applicable data processing fee, annual maintenance cost, data accuracy, calibration, and validation. Alternatively, Floating Car Data, known as FCD, has introduced an effortless method for traffic data collection and consequently traffic performance measurement since the past decade. In this work the authors did a comparative study between two techniques: Bluetooth sensors data vs. global positioning system (GPS)-based data, for estimating travel time reliability along two major goods movement corridors in the city of Calgary, Alberta. As a trucking hub, Calgary plays a major role in providing a safe, efficient, and connected goods movement network in the province, and nationwide. On one hand, the authors used the output of BluFax units, which operate by monitoring Bluetooth signals at several points along a roadway, to calculate travel time reliability. On the other hand, TomTom historical traffic data was extracted by running a series of customised queries using TomTom self-service web portal, called Traffic Stats which eventually generates a report containing custom area analysis, travel times, and speeds. Accordingly, the authors estimated travel time reliability based on the generated TomTom report and compared it to the results obtained from the BluFax traffic data. The important goods movement corridors were identified according to the percentage of traffic consisting of trucks on the primary goods movement corridors. To calculate travel time reliability, the authors applied the travel time buffer index approach developed by the Federal Highway Administration (FHWA). The methodology is somehow preferable since the calculated metrics are readily understandable by laypeople, including politician and the general public. The authors study results also demonstrated that the data provided by the Bluetooth technology meets the minimum sample size requirement and seems to be closest to the observed benchmarks. The authors concluded that applying the aforementioned technique could generate reliable travel time and speed data given the number of observations, and direct measurement of performance indicators from disaggregate data sources. The study also showed the inadequacy in terms of the number of TomTom Historical Traffic Data records on Canadian freeways and arterial roads; nonetheless, this inadequate traffic data still demonstrated somehow a reasonable accuracy for the travel time reliability study on a heavily used arterial road. This can’t be interpreted as a general conclusion. Hence, a few more studies would need to be conducted to comprehensively verify the accuracy and the adequacy of TomTom historical traffic data for travel time and speed studies on Canadian roads.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| 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".