A Crowdsourcing Technique For Road Surface Monitoring Using Smarthphone Sensors
Bibliographic record
Abstract
Road surface monitoring is a key factor in providing safe road infrastructure for road users. As a result, road surface condition monitoring aims to detect road surface anomalies such potholes, cracks and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road rehabilitation and maintenance. Several studies have been developed to utilize smartphone sensors (e.g., Global Positioning system (GPS) and accelerometers) mounted on a moving vehicle to collect and process the data to monitor and tag roadway surface defects. Geotagged images or videos from the roadways have also been used to detect the road surface anomalies. However, existing studies are limited to identifying roadway anomalies mainly from a single source or lack the utility of combined and integrated multi-sensors in terms of accuracy and functionality. Therefore, low-cost, more efficient pavement evaluation technologies and a centralized information system are necessary to provide the most up-to-date information about the road status due to the dynamic changes on the road surface This information will assist transportation authorities to monitor and enhance the road surface condition. In this research, a probabilistic-based crowdsourcing technique is developed to detect road surface anomalies from smartphone sensors such as linear accelerometers, gyroscopes and GPS to integrate multiple detections accurately. All case studies from the proposed detection approach showed an approximate 80% detection accuracy (from a single survey) which supports the inclusiveness of the detection approach. In addition, the results of the proposed probabilistic-based integration approach indicated that the detection accuracy can be further improved by 5 to 20% with multiple detections conducted by the same vehicle along the same road segments. Finally, the development of the web-based Geographic Information System (GIS) platform would facilitate the real-time and active monitoring of road surface anomalies and offer further improvement of road surface quality control in large cities like Toronto.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".