A Geospatial Analysis of E-Scooter Trip Production in Calgary, Alberta
Bibliographic record
Abstract
The research presented in this thesis explores how land-use and transportation infrastructure characteristics as well as weather variation and socio-demographics influence the production of shared, dockless E-Scooter trips in the City of Calgary, over a four year period between 2019 and 2022, during their pilot program through the estimation of a Zonal Based Trip Generation Regression Model using the Negative Binomial Spatial Linear Feedback Model incorporating autoregressive terms.The model uses daily Origin Destination E-Scooter trip counts collected by the City of Calgary and regresses this against obtained point of interest and land-use data, public transit, transportation network data, socio-demographic and historical weather data to describe Calgary's transportation landscape and identify potential trip attractors, influences and also assert if spatial interactions between nearby locations also impact E-Scooter travel.Findings from the conducted analysis show that land-use characteristics and transportation infrastructure are strongly associated with shared, dockless E-Scooter travel.Consequently, the models highlight the prevalence of positive autocorrelation, indicating the presence of spatial interactions between neighbouring units.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".