Automated class identification of modes of travel in shared spaces: a case study from India
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a classification approach for road‐user modes of travel. The classification does not assume well organized, and lane disciplined traffic. Instead, it relies on specific characteristics intrinsic for each road‐user to predict the corresponding class. The classification relies on extracting the geometric and movement characteristics of road‐users. As such, it is possible to classify road‐users in shared space facilities and sites with high level of non‐compliance. The classification is a multi‐step procedure. First, movement features are used to discriminate between motorized and non‐motorized road‐users. Then, complementary features based on road‐user geometry are added to differentiate between vehicles, rickshaws, powered two‐wheelers, and buses. Experiments are performed on a video data set from a shared facility in New Delhi, India. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 90 percent. By considering the movement attributes, the approach is tolerant to considerable variations in road‐user physical details which often arises from choices of camera positions and partial occlusions. The research is part of the long‐term goal to develop an automated video‐based road safety and data collection system for developing countries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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