Self-Attention based encoder-Decoder for multistep human density prediction
Bibliographic record
Abstract
Multistep Human Density Prediction (MHDP) is an emerging challenge in urban mobility with lots of applications in several domains such as Smart Cities, Edge Computing and Epidemiology Modeling. The basic goal is to estimate the density of people gathered in a set of urban Regions of Interests (ROIs) or Points of Interests (POIs) in a forecast horizon of different granularities. Accordingly, this paper aims to contribute and go beyond the existing literature on human density prediction by proposing an innovative time series Deep Learning (DL) model and a geospatial feature preprocessing technique. Specifically, our research aim is to develop a highly-accurate MHDP model leveraging jointly the temporal and spatial components of mobility data. In the beginning, we compare 29 baseline and state-of-the-art methods grouped into six categories and we find that the statistical time series and Deep Learning Encoders-Decoders (ED) that we propose are highly accurate outperforming the other models based on a real and a synthetic mobility dataset. Our model achieves an average of 28.88 Mean Absolute Error (MAE) and 87.58 Root Mean Squared Error (RMSE) with 200,000 pedestrians per day distributed in multiple regions of interest in a 30 minutes time-window at different granularities. In addition, the geospatial feature transformation increases 4% further the RMSE of the proposed model compared to the state of the art solutions. Hence, this work provides an efficient and at the same time general applicable MHDP model that can benefit the planning and decision-making of many major urban mobility applications.
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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 | 0.006 | 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 | 0.002 | 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) | 0.001 | 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 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".