Data-Driven Methods and Challenges for Intelligent Transportation Systems in Smart Cities
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
As the Internet of Things (IoT) technology is seeing rapid advancements, the concept of creating smart cities is gaining huge popularity. One of the prominent sectors that can benefit from the rise in IoT technology and pave the way for smart cities is Intelligent Transportation Sys-tems (ITS). Data-driven approaches reliant on advancements in machine learning have gained wide popularity in the field of ITS. Such meth-ods facilitate solutions for problems in numerous ITS areas. This article aims to provide an analysis of some of the most notable works in four ITS categories: prediction and forecasting, detection, recognition, and safety. Different studies across these areas are reviewed, underlining the importance of data to ITS while focusing on the different architectures and technologies like machine learning used to advance ITS. Moreover, this article highlights the set of challenges faced by each area and proposes a potential solution for the main challenge.
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 | 0.000 |
| 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 it