Studying Regional and Cross Border Freight Movement Activities with Truck GPS Big Data
Notice bibliographique
Résumé
This dissertation utilizes an existing GPS data source to create and analyze a dataset of processed truck trips. The original data was generated for the purpose of fleet management by GPS transponders installed on Canadian owned trucks. These vehicles provide a critical service by fulfilling the economic need to move goods from one location to another. This thesis subsequently re-purposes the GPS pings as a form of opportunistic data to enrich the current state of knowledge regarding freight movement patterns. The first sections of this thesis are dedicated towards understanding the GPS data and devising processing methods needed to convert raw data into a suitable dataset of truck trips. Due to the nature of the topic, a geographic perspective was integral to this work to properly mine the data for useful information. For example, a new application of entropy based on the variety and distribution of carriers stopping at a location was created to assist with the classification of stop events. The data processing resulted in an approximate sample size of 245,000 trips per month from September 2012 to December 2014 and the month of March 2016. The volume of data and level of detail provides information that has not been available to date, which includes trip origins and destinations, associated industry, observed routes, and border crossing time/location if the trip was international. The processed trips derived from GPS data are applied towards a better understanding of inter-regional and cross-border truck movements. This area is underrepresented due to the difficulties in obtaining long-haul trip data where trucks move through multiple jurisdictions. These difficulties are compounded for international trips since the study area spans multiple nations. The processed truck trips are utilized to identify the spatial patterns of truck movements at specific border crossings between Canada and the U.S. including the Ambassador Bridge, Blue Water Bridge, and Peace Bridge. The choice of border crossing is also investigated using a specific case study of trucks travelling between Toronto, Ontario, and Chicago, Illinois. Finally, the observed trips from origin to destination allows for an analysis of delays at single locations (the border crossing) as well as their impact on the total trip. These applications represent a small part of the full potential that passive GPS data can provide after sufficient processing is applied. It is the hope of this author that these efforts can contribute towards the state of practice in transportation as GPS data becomes increasingly available to researchers. The work presented in this thesis illustrates how such GPS data can be used as a viable source to fill in gaps in knowledge. While traditional data collection techniques will remain a necessary facet of transportation research in the foreseeable future, information generated passively by users every day provides a new source of data that is characteristically large (in terms of volume and spatio-temporal coverage) and cost-effective.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».