Dynamic O-D travel time estimation using an artificial neural network
Bibliographic record
Abstract
Although the minimum O-D (origin-destination) travel time path in a dynamic traffic network can be calculated using standard minimum path algorithms there are a number of transportation applications which require a quick estimate of this information. These applications include such areas as real time vehicle dispatching systems where potential routes between a large number of origins and destinations have to be continually updated for a variety of vehicles throughout the day. The objective of this paper is to demonstrate the feasibility of using an artificial neural network (ANN) to estimate the O-D travel time in a dynamic traffic network. Three feedforward neural networks were developed to model the travel time behavior during different time periods of the day: AM peak, PM peak and off peak. These ANN models were subsequently trained and tested using a network from the City of Edmonton, Alberta. A comparison of the ANN model with a traditional statistical model is then presented. Lastly, the computational efficiency of the proposed ANN model compared to two shortest path algorithms is demonstrated. The statistical results show that the ANN models are appropriate for estimating dynamic O-D travel times and are significantly faster than the exact minimum path algorithms.
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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 | 0.000 | 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 | 0.000 |
| 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".