A machine learning framework for public transport ridership estimation using multi-source data fusion with low-cost bluetooth data
Bibliographic record
Abstract
Accurate information about demand volumes at certain locations within public transport networks is critical to making informed decision by transportation planners. Traditional manual counts to collect volumes, while accurate, are costly and labour intensive. Existing automatic passenger counting systems also face limitations in terms of cost, accuracy, or compatibility. This paper proposes a multi-source data fusion framework to improve the ca-pability of passenger counting using a low-cost Bluetooth sensor. The frameworks combine otherwise independent and unrelated raw Bluetooth counts, novel drone data and freely available General Transit Feed Specification - Real-Time and ferry schedule data into a unified and comparable format. The proposed framework leverages the advance capabilities of various machine learning models, K-Nearest Neighbour, XGBoost and Random Forest to estimate ridership of public transport vehicles in an area affected by nearby ferry operations. The results demonstrate the models generated using the framework achieve high accuracy and low errors when compared to the ground truth of manual counts. Machine learning model vastly outperform standard Linear Regression model with a R2 value of 0.86 compared to 0.62. Models incorporating variables develop from the framework significantly outperform those that rely solely on Bluetooth data (R2 of 0.86 vs -0.49). Notably, the framework is still able to draw similar conclusion when utilizing the drone counts as the ground truth which expose the model with significantly more data points then manual counts. However, discrepancy between manual count and drone count highlight the need for further validation to enhance the reliability of this approach. Nevertheless, the framework highlights the value of multi-sensor data fusion as a necessary enhancement to improve the utility and accuracy of the low-cost Bluetooth count.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".