Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices
Bibliographic record
Abstract
This article presents a methodology for identifying travelers' transportation modes by tracking Global Positioning System (GPS)-equipped mobile devices in the traffic stream. Various mobile phone service providers have location-based services (LBS) that track the locations of their mobile phones. One major concern in using mobile phones for traffic monitoring is that the phones are not necessarily in passenger vehicles. The mobile device can be in a car, bus, or other modes that have distinct speed and acceleration profiles. In addition, querying the mobile device has monetary cost implications, and the higher the number of location queries from the server the higher the associated cost. This article focuses on the feasibility of using the characteristics of the trail of GPS data stream to identify the mode on which the mobile device is located. Currently available LBS in Toronto can only provide GPS data once every 5 min. Because of the sampling limitation, a GPS data logger is used to collect the trip data and the logged data is sampled at varying frequencies as if they are coming from the mobile phones. The analysis is conducted using neural networks (NNs) to determine the transportation mode. The analysis also examines the impact of varying sampling rates (number of pings per unit time) and monitoring duration (time length of data trail) on mode classification accuracy. In total, 60 h of GPS data were collected while traveling on various transportation modes throughout the Greater Toronto Area. Results confirm the potential of neural networks to successfully detect transportation modes from GPS data, both in peak and nonpeak periods. The results indicate that higher sampling frequency and longer monitoring duration result in higher mode detection rates. In addition, the route-specific neural networks perform better than the universal neural networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".