Modeling Destination Choice Behavior of the Dockless Bike Sharing Service Users
Bibliographic record
Abstract
This study investigates trip-level destination choice behavior of users of the dockless bike sharing service (DBS). A random parameter latent segmentation-based logit (RPLSL) model is developed utilizing the DBS users’ trip itinerary data for Kelowna, Canada. The RPLSL model captures multi-dimensional heterogeneity such as inter-segment and intra-segment heterogeneity. The model is developed at a micro-spatial resolution which is defined as the bicycle analysis zone. One of the key features of this study is to test the interdependencies between the origin and destination of a trip using their built environment attributes. Model results suggest that segment 1 is more likely to include trips originating from the urban neighborhoods; whereas, segment 2 includes trips originating from the suburban neighborhoods. The parameter estimation results reveal that DBS trips are more likely to be destined to locations with longer length of cycle tracks, higher employment density, and that are closer to the Central Business District and bus stops (i.e., within 500 m). The model confirms multi-layer heterogeneity. For instance, trips originating from the urban areas in segment 1 are more likely to be destined to destinations within 500 m of the designated bike return sites (i.e., havens). In contrast, shorter trips originating in suburban areas in segment 2 show a negative relationship. Interestingly, a bike-friendly environment might increase the attractiveness of destinations closer to havens, even for the trips originating in suburban areas. The findings of this study will assist in developing policies and infrastructure investment decision making at the destination locations to promote DBS usage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".