Optimizing Urban Mobility: A Comparative Analysis of Taxi Demand Prediction Models
Bibliographic record
Abstract
Urban mobility optimization is crucial in managing transportation systems efficiently.This study addresses a broad research area of urban mobility by focusing on taxi demand prediction, a key component of the transportation ecosystem.The specific problem addressed in this research is the need for accurate and efficient taxi demand prediction, especially in large, dynamic urban environments.Existing solutions, including basic time series approaches like simple moving averages and exponential weighted moving averages, while valuable, have limitations in handling the intricacies of urban taxi demand patterns.In this study, we employed a combination of data preprocessing techniques, advanced regression models, and Fourier features to predict taxi demand in dynamic urban environments.The data preprocessing techniques included data cleaning, normalization, and feature engineering.The advanced regression models used in this study were Random Forest and XGBoost, which were trained and tested using NYC taxi datasets.The Fourier features were used to capture the periodicity of the taxi demand patterns.These models are demonstrated to outperform standard solutions, effectively achieving the targeted mean absolute percentage error (MAPE) of less than 12%.Evaluation of the solution revealed its effectiveness in reducing the prediction error by more than 1%, thus highlighting the positive results of this research.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".