Carsharing adoption dynamics considering service type and area expansions with insights from a Montreal case study
Bibliographic record
Abstract
Carsharing operators (CSOs) are adapting their service over time to meet changing demands and grow their market share. Service areas are enlarged and, in some cities, “dual-mode settings” evolve, incorporating free-floating carsharing (FFcs) as a new service alongside existing station-based carsharing (SBcs). This paper proposes a methodology to discuss adoption dynamics in such a context, specifically focusing on the impact of existing services and service extensions on the adoption of the new service. We propose a framework, comprising of two parts: a potential market assessment and an adoption model. The potential market assessment focuses on establishing the relationships between the local population, carsharing memberships and Points of Interest (POIs) within the given service area. The adoption model then describes the likelihood of consumers adopting the FFcs service. By combining these two models, the effects of service extensions can be assessed. We evaluate the framework using a nearly six year dataset from Communauto, Montreal. The first 35 months of data are set as training data, while the subsequent 33 months are used for validation of predictive performance. Results demonstrate that the proposed model accurately predicts adoption dynamics. Prior experience of SBcs and initial information spread are found to be key parameters for demand prediction determining early adoption peaks and, due to follower effects, also impact long-term demand. Additionally, we quantify the importance of covering residential areas and points of interests in the service area, highlighting the synergy effects of service area expansions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".