Optimal electric bus frequency setting combining LSTM prediction and two-layer planning
Bibliographic record
Abstract
As an environmentally friendly and ef icient public transport, the optimization of the operating frequency of electric buses is of great signi icance for improving passenger satisfaction and reducing operating costs.This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning.First, LSTM neural network is utilized to predict the passenger low of electric buses.Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting.Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting.The inbound and outbound passenger low data of the 5th station of 363 electric bus in Q city are used for practical veri ication.The prediction results of the LSTM model on inbound and outbound passenger low on weekdays and natural days are basically consistent with the actual values.The optimal frequency of 62 trips was solved using genetic algorithm.The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is veri ied under the scenario of thousands of passenger lows.From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and twolayer planning model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".