Transportation Mode Recognition based on Cellular Network Data
Bibliographic record
Abstract
A variety of modern technologies leveraging ubiquitous mobile phones have addressed the transportation mode recognition problem, which is the identification of how users move about (walking, cycling, driving a car, taking a bus, etc). It has found applications in areas such as smart city transportation, greenhouse emission calculation, and context-aware mobile assistants. To date, significant work has been devoted to the recognition of mobility modes from the GPS and motion sensor data available on smartphones. However, these approaches often require users to install a special mobile application on their smartphone to collect the sensor data, they are power inefficient and privacy intrusive. Also, bus and car modes are similar in terms of motion due to the same roads, traffic regulations and speed, which makes it challenging to accurately distinguish between the two modes. In this research, we handle these issues by offering a user-independent system that distinguishes three forms of locomotion-walk, bus, and car-solely based on mobile data (3G and 4G) of a smartphone. The system was developed using data collected in Makkah city, Kingdom of Saudi Arabia. Using statistical classification and boosting techniques, we successfully achieved an accuracy of 82%, and with post-processing step we achieved an accuracy of 99%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.002 |
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".