Advancing Transportation Routing Decisions Using Riemannian Manifold Surfaces
Bibliographic record
Abstract
We consider several real-world driving factors such as the time spent at traffic signs (e.g., yield signs and stop signs), speed limits, and the topology of the surface to develop realistic and accurate routing solutions. Though these factors increase the complexity of modeling, they provide the flexibility to evaluate the routing solutions from different perspectives: cost, distance, and time, to name a few. First, we develop a set of algorithms based on the Riemannian manifold surface (RMS) to factor in the Earth’s curvature to calculate distances. Second, we present a multiobjective, nonlinear, mixed-integer model (MINLP) that minimizes the distance traveled, time traveled, traveling costs, and time spent on traffic signs to design and evaluate the routes where the waiting times associated with traffic lights, stop signs, and yield signs are stochastic. Finally, we apply MINLP and RMS-based algorithms to a set of real-life and short- and long-distance transportation problems and analyze the results from computational experiments and discrete event simulations. We show that our approaches are on par with the state-of-the-art application, Google Maps, and yield realistic routing solutions that generate significant cost savings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 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".