Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system
Bibliographic record
Abstract
This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".