Railway passenger train delay prediction via neural network model
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.256
- Threshold uncertainty score
- 0.487
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.197 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
SUMMARY The aim of this paper is to present an artificial neural network model with high accuracy to predict the delay of passenger trains in Iranian Railways. In the proposed model, we use three different methods to define inputs including normalized real number, binary coding, and binary set encoding inputs. One of the great challenges of using neural network is how to design a superior network for a specific task. To find an appropriate architecture, three different strategies called quick method, dynamic method, and multiple method are investigated. To prevent the proposed model from overfitting in modeling, according to cross validation, we divide existing passenger train delays data set into three subsets called training set, validation set, and testing set. To evaluate the proposed model, we compare the results of three different data input methods and three different architectures with each other and with some common prediction methods such as decision tree and multinomial logistic regression. For comparing different neural networks, we consider training time and accuracy of neural networks on test data set and network size. In addition, for comparing neural networks with other well‐known prediction methods, we consider training time and the accuracy of neural network on test data sets. To make a fair comparison among all models, we sketch a time‐accuracy graph. The results revealed that the proposed model has higher accuracy. Copyright © 2012 John Wiley & Sons, Ltd.
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.
The record
- Venue
- Journal of Advanced Transportation
- Topic
- Railway Systems and Energy Efficiency
- Field
- Engineering
- Canadian institutions
- not available
- Funders
- not available
- Keywords
- OverfittingComputer scienceArtificial neural networkTest setArtificial intelligenceMachine learningSet (abstract data type)TrainData miningTest dataData setTime delay neural network
- Has abstract in OpenAlex
- yes