Incorporating Scenic View, Slope, and Crime Rate into Route Choices
Bibliographic record
Abstract
With Global Positioning System (GPS) devices, drivers are now more confident in exploring routes out of the ordinary. More portable forms of commercial GPS navigators (GPS-embedded cell phones, MP3 players, and watches) are also available for pedestrians and bicyclists. Most route guidance applications minimize travel distance and time, which are important factors, but are not the only navigational criteria of interest to users, especially in urban and city environments. With the aid of advanced features of geographic information systems (GISs), new geospatial factors such as the three-dimensional (3-D) nature of the roads and crime rates can be included in the route guidance for broader applications. For instance, 3-D GISs can generate information on visible scenery along a given route (for tourists) or the slopes of the consecutive road segments (for pedestrians and bicyclists). In addition, pedestrians and bicyclists can opt to avoid high-crime areas. In the future, this concept of incorporating new geospatial information can be extended, for example, for computing low-elevation areas that are susceptible to flooding and hilly regions with heavy traffic. This paper presents methods of incorporating 3-D features of the roads and geospatial crime rate information for route guidance purposes. It is found that the 3-D nature of the roads and crime rate–related information can result in considerably different route choices.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".