Smart transportation planning: Data, models, and algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By developing cities and increasing population, smart transportation becomes an essential component of modern societies. Extensive research activities using machine learning techniques and several industrial needs have paved the way for the emerging field of smart transportation. This paper presents data, methods, and models that are essential for intelligent planning of transportation. In particular, the current data sources for gathering information to control or forecast traffic are described, connected Vehicles (CVs) that bring smart and green transportation to modern life is also discussed. Clustering Analysis as an effective unsupervised machine learning method in trip distribution and generation and traffic zone division is discussed in the paper. Various machine learning techniques and models that use time series prediction are introduced in this paper including ARIMA, Kalman filtering, Holt winters'Exponential smoothing, Random walk, KNN Algorithm, and Deep Learning. Finally, a discussion on the main advantages and drawbacks of these models, as well as the business adoption of the forecasting models are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it