Improving Cyclists’ Safety Using Intelligent Situational Awareness System
Bibliographic record
Abstract
According to the World Health Organization (WHO), over 1.35 million people died in road traffic-related accidents worldwide in 2020 of which 41,000 are related to the cyclists. Bike safety is one of the most serious issues facing urban riders. According to Statistics Canada, this number represents 1654 cyclist deaths in Canada, an average of 74 deaths each year from 2006 to 2017. Cyclists are a critical component in traffic collisions, where they face a greater risk of serious injury or death. As a result, they are classified as vulnerable road users. To avoid this, the need for intelligent transportation systems (ITSs) that increase susceptible cyclists’ awareness of their surroundings is becoming apparent. As a result, we proposed a situational awareness system as part of ITS to enhance bike safety through the employment of three layers of applications: (1) the users tier (mobile application), (2) the virtual private server (VPS) and processing system, and (3) the database management system (DMS). These decision support systems (DSSs) improve vulnerable road users’ situational awareness by identifying high-risk regions for cyclists or motorcyclists using static and dynamic data and then notifying vulnerable road users. The suggested situational awareness system collects and integrates incoming data, prioritizes criteria, and notifies users based on a static hot-spot map produced from accident locations and dynamic data, such as traffic flow, weather conditions, and the user’s speed. The developed work made use of both single threading (for requests from less than 1000 users) and multi-threading (for requests from more than 1000 users), resulting in a highly scalable system based on an open source platform for higher numbers of requests.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".