Studying Regional and Cross Border Freight Movement Activities with Truck GPS Big Data
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".