Planning of Road Network Monitoring Using GPS and GIS
Bibliographic record
Abstract
Road network monitoring is an activity conducted daily by the Ministry of Transport of Quebec. The complete network must be monitored every two weeks. In this setting, the usual objective in arc routing of minimizing the total travel distance is irrelevant. The vehicles are equipped with GPS locating devices to monitor events and trace routes. Since most planned routes are not completed because of events on the network, there is a need to continuously re-plan and re-schedule routes. The paper developed a methodology to achieve this task by gathering data from the GPS trace, matching it to the planned routes within a GIS and then use mathematical algorithms to propose a new schedule with new routes. The interurban road network studied consists in a hierarchy of three classes of roads that have different monitoring standards. The paper tested three different methods using different objectives depending on the operators' needs. Results show that the method that implies rescheduling based on assignment and reconstruction of routes with an arc-adding method gives the best coverage for each class of road. Nowadays, more transport operators have devices like GPS to locate and manage their fleet of vehicles. The integration of such technologies to GIS and to OR models for arc routing in day to day operations is however a challenging task. This article addresses both the data processing issues and the optimization issues in order to design an efficient decision support system appropriate for the highly dynamic nature of the problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".