Dynamic Pricing on Round-Trip Carsharing Services: Travel Behavior and Equity Impact Analysis through an Agent-Based Simulation
Bibliographic record
Abstract
Carsharing companies can customize their service by adopting different pricing schemes and offers with the goal of increasing service attractivity, fleet usage and profits. Dynamic pricing strategies can be designed to adjust and balance cars’ availability temporally and spatially; they may pose some questions regarding customer equity, since such measures could impact their activities and mode choice. In this paper, we develop an experimental design for conducting an explorative analysis of how availability-based and time-based dynamic pricing schemes impact demand and supply performances. The strategy is simulated in the open-source agent-based software MATSim and compared to a fixed pricing policy scheme. Two spatial distributions of the value of time for the population of Berlin are applied (radially and coaxially) to systematically analyze agents’ behavior response to these pricing policies. Results confirm that when dynamic pricing is applied people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation. Overall, the strategy applied in this study appears to be unfair, since it hinders low income groups from using the carsharing service.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".