Bluetooth Sensors Data Versus GPS-Based Data, Measuring Travel Time Reliability on Freight Transportation Corridors in the City of Calgary, Alberta, A Comparative Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».