Decoding the first-and-last mile: Analyzing the transit connecting bike share GPS routes in Hamilton, Ontario with spatiotemporal distance decay
Bibliographic record
Abstract
The well-known first-and-last mile (FM/LM) problem has long plagued traditional fixed-route public transit systems. Recently, emerging shared micromobility services, such as bike share, have been gaining popularity and quickly developing worldwide. These services present the potential to complement public transit by serving as an FM/LM solution. This study leverages Hamilton Bike Share Global Positioning System routes data and employs a novel spatiotemporal distance decay with built environment adjustment method to quantify whether bike share trips are FM/LM trips on a continuous scale ranging from 0 to 1. This approach provides a more detailed usage pattern at the road level and a more realistic identification of FM/LM trips compared to the commonly used buffer zone approach applied to origin and destination pair trip data. Subsequently, we examine the spatiotemporal patterns and trip attributes of FM/LM trips and how they differ from all bike share trips. The findings reveal distinct differences in temporal, spatial, and trip attributes between FM/LM and all bike share trips. We found that bike share trips are frequently used by riders to cover the distance between transit stops and their workplaces. Additionally, FM trips are generally more challenging compared to LM trips due to a significantly higher number of left and U-turns. Policy recommendations are proposed to enhance wayfinding, expand cycling infrastructure, and promote integrated bike share and transit usage. This research highlights the potential of bike share systems to address the FM/LM problem and provides empirical evidence to inform future urban transportation planning and policy development. It also offers a methodological framework for identifying FM/LM trips using individual trip data such as origin and destination pairs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".