Parking Volume Forecast of Railway Station Garages Based on Passenger Behaviour Analysis Using the LSTM Network
Bibliographic record
Abstract
Parking volume forecast is an indispensable part of the parking guidance and information system (PGIS), which is an important component of the intelligent transportation system (ITS). The parking volume forecast of railway stations’ garages will provide information support for garages’ management and will also be a great convenience for car passengers. Parking garages of railway stations serve passengers to arrive or depart stations by car, and their arrival or departure behaviours definitely affect parking volumes. The study results showed that different parking behaviours have different characteristics of the parking duration category. Therefore, passenger behaviour analysis based on parking duration category analysis and time series similarity measures was introduced into the forecast model in this research. Also, a novel parking volume forecast model based on the long short-term memory (LSTM) is proposed. In this paper, the parking volume data of public parking garages of Hongqiao Railway Station in Shanghai of China is used to verify the model, and the proposed model makes it possible for the accurate and real-time prediction of parking volumes which are divided into different parking duration categories. Compared with the ungrouped data model and the conventional forecast model, the proposed parking volume forecast model based on passenger behaviours with the LSTM network achieves a better performance and provides more accurate prediction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".