Design, Implementation and Testing of a New Multi-Sensor Mobile Device as a Tool for Cycling Data Collection in Highly Congested Urban Streets
Bibliographic record
Abstract
The number of bicycle riders in New York City (NYC) has been increasing steadily in the past few years. These numbers include private and shared bicycles. The NYC bicycle network has been expanded to accommodate the needs of the increasing number of riders. Although the new infrastructure has reduced the number of cyclists killed or seriously injured (KSI) in some areas, in other areas similar improvements were not observed. A data-driven approach to study the possible effects of this type of infrastructure inconsistency on the variation of the number of bicycle crashes from one region to another in the city is the primary motivation of this paper. A highly portable and inexpensive sensing device for measuring the distance between a bicycle and lateral objects is designed and developed from scratch. The developed mobile sensing device can also map bicycle trajectories to highlight critical segments where the safe distance from passing vehicles is not respected. This mobile device is powered by a portable power source and it is comprised of two ultrasonic sensors, a Global Positioning System (GPS) receiver, and a real-time clock (RTC). The sensor is secured inside a custom design 3D printed case. The case can be easily attached to any bicycle including shared bikes for testing. The final prototype is entirely functional and used to collect sample data to demonstrate its effectiveness to address safety-related problems mentioned above. Finally, a dashboard is created to display collected key information. This key information can be used by researches and agencies for a better understanding of the factors contributing to the safety of bicycle routes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".