Guest Editorial Introduction to the Special Issue on AI-Empowered Trajectory Analytics in Intelligent Transportation Systems
Bibliographic record
Abstract
With the rapid growth of location sensing in the Internet of Things (IoT) and Internet of Vehicles (IoV) techniques, trajectory data has been generated that can be used to describe diversity and characteristics of moving objects. The analysis and management of trajectory patterns has become an important issue in recent decades, as it supports efficient strategies and decisions based on discovered patterns and knowledge from the mobility behavior of customers or citizens in many fields and applications (e.g., smart city, intelligent transportation, location-based services, health management, etc.). Since the recent development in AI, it is possible to use AI-based techniques to analyze trajectory data at an unprecedented scale to address applicable issues of effectiveness, efficiency, accuracy, and privacy in Intelligent Transportation Systems (ITS), therefore, it is a highly competitive area to propose innovative methods, principles, procedures, techniques, frameworks, theories, and applications to address the aforementioned challenges of trajectory data in ITS. This Special Issue is intended to provide a forum for all researchers from academia and industry to share their original, creative, innovative, cutting-edge insights, theories, ideas, and developments for the analysis of trajectory data using AI-empowered techniques in ITS. In this special issue, we received 52 submissions, and finally accepted 17 articles to be published in the special issue. Below is a brief introduction to each of them.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".