Managing the road safety risks of last mile deliveries: Do telematics have a role to play?
Bibliographic record
Abstract
OBJECTIVE: The research aimed to understand the impact of telematics in reducing traffic violations and crashes among drivers working in parcel delivery and the perceived utility, effectiveness, and acceptability of telematics among drivers using them. The objective was to carry out an online survey among drivers who have telematics versus drivers who do not and compare their behaviors in terms of risks and violations controlling for key demographics an exposure. METHODS: An online anonymous survey was conducted of 780 home delivery drivers of which 430 used telematics in 2022. The survey was conducted by a fieldwork company and participants were compensated for their time. Univariate and multivariate analysis was conducted on the data. RESULTS: Telematics users did not report less crashes than drivers who did not use telematics. Most drivers, irrespective of telematics use agreed that the time pressure of delivery work increased speed limit violations and unsafe or hazardous parking. Multivariate analysis indicated that damage collisions for all drivers were associated with factors such as, driving medium to large vans, hazardous behavior related to parking, and having penalties for violations, especially related to speed. Delivery drivers, while generally facing pressure to speed and take risks to meet delivery schedules, did perceive telematics as a tool for safety enhancement and information dissemination, as well as for the management of performance. Non-telematics users viewed it with skepticism. CONCLUSIONS: Telematics alone may not ensure the safety of last-mile deliveries and could potentially increase the pressure of deliveries. This research underscores the need for a holistic approach to road safety in the home delivery sector, combining telematics technology with a proactive safety culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".