Passenger Safety in Ride-Sharing Services
Bibliographic record
Abstract
With the rise of ride-sharing services available in the world, it has ease users to commute with app based services and cashless transactions. The introduction of ride-sharing companies in the start gave the users the liberty to use one application and account to be used universally. However, with the rise of such services, one question that does ring a bell is the passenger safety, especially in countries with loose law controls. This paper focuses on the aspect of passenger safety in ride-sharing services. There have been reports of harassment, assault and robbing passengers on these rides. However, no strict measures could be taken because of the company not having full control over the driver, vehicle and ride. Poor feedback system has also added fuel to the fire. As much as they are filling the gap of better services across the world, there is a dire need of having possible security measures to make sure that rider safety is ensured from the start of the journey till he/she reaches the destination. Different suggestions that have been given include the introduction of mandatory dash cams in the rides through which the rider can put a live transmission of his/her ride on social media or YouTube, in order to have more eyes. Moreover, an introduction of watchdog network, which can volunteer to monitor rides, can also keep an eye on the transmission and can contact lawmakers in case of emergency. A distress alarm on the app can be made available, which can report discomfort of the rider or suspicious behaviour of the driver. Other measures like keeping indoor lights on during after-dark hours, display of ride sharing company’s sticker on front and rear of the car and introduction of passenger insurance add-on in the ride type can also enhance security of the rider.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".