Explainable Machine Learning for Spatio-Temporal Demand Forecasting in Autonomous Vehicle Fleets
Bibliographic record
Abstract
As autonomous vehicle (AV) fleets become increasingly viable for urban transport, intelligent systems capable of forecasting mobility demand are critical for optimizing fleet utility. However, most existing approaches assume uniform citywide demand or rely on static zones, which limits their effectiveness in dynamic, heterogeneous environments. This paper addresses this gap by proposing a modular and explainable spatio-temporal forecasting framework for short-term ride demand prediction in clustered urban zones. Using the Uber NYC rides dataset, we evaluate five forecasting models: Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Prophet, Temporal Convolutional Networks (TCN), and Chronos-T5, in the task of predicting hourly demand across six spatial clusters. XGBoost has demonstrated the best overall performance in both accuracy and inference time, with SHAP analysis revealing strong short-term temporal dependencies as key predictive signals. The framework is scalable, explainable, and adaptable to other urban contexts, offering a foundation for real-time AV fleet repositioning and shared mobility optimization in smart cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".