Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification
Bibliographic record
Abstract
Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".