Demand Density Forecasting in Mobility-on-Demand Systems Through Recurrent Mixture Density Networks
Bibliographic record
Abstract
Demand forecasting is one of the essential issues in the Mobility-on-Demand (MoD) systems. Most deep learning-based models address this issue by point prediction, which neglects stochasticity in the forecasting result. In this paper, we propose a novel deep learning-based model, the tailored recurrent mixture density network (RMDN), to forecast the demand density in the MoD systems. The tailored RMDN integrates the time-dependent sequence of historical mobility demand information with the temporal features to forecast the short-term demand density. Unlike the point prediction in the existing work, the forecasting result by tailored RMDN benefits from the predicted parameters. Namely, the predicted weights, means, and variances can be utilized to parameterize a Gaussian mixture model that can denote any shape of demand distribution. We then conduct a group of numerical experiments on the New York yellow taxi trip record data. The validation results show that by integrating the temporal features in MoD data, the tailored RMDN model can significantly improve the demand density forecasting results compared to the statistical time-series prediction model ARIMA. In particular, the tailored RMDN model outperforms the ARIMA model up to 51.3% in terms of the log-likelihood values. In addition, we observe that the tailored RMDN is tremendously superior to ARIMA in handling the high volatility in MoD demand.
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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 | 0.001 |
| 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".