A fuzzy intervening opportunity model to predict home-based shopping trips
Bibliographic record
Abstract
The Intervening Opportunity Model (IOM) is a powerful analytical and probabilistic model used to model trip distributions. Although compared to other models this model uses a powerful analytical level to model distribution of inessential trips, the basic limitation in this model is the presumption that trip makers are fully aware of all available opportunities in the area and their trip lengths to all possible destinations and that they scrupulously evaluate all the destinations during their decision making process. This assumption fails to hold true consistently, especially in big cities. This paper has attempted to present and test a new method to omit the limitations inherent in a conventional intervening opportunity model and maximize its compatibility with the common destination choice pattern of home-based shopping trips. The overall idea is that for the main parameters of the intervening opportunity model (i.e., the opportunity and destination ranking variables) some coefficients and weights may be considered to calibrate the model once these weights and coefficients are calculated and included. Since the factors relevant to the knowledge of the trip makers about the opportunities at destinations (the market knowledge factor) and also the knowledge on the accessibility of the shopping destinations (accessibility factor) are both fuzzy concepts, the authors examined and tested the idea of applying fuzzy logic in the development of an intervening opportunity model. The positive result of the study is called a Fuzzy Intervening Opportunity Model (FIOM). The fuzzy intervening opportunity model is presented in three groups with a significant goodness of fit with the observed data.
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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) | 0.001 | 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 | 0.000 |
| 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".