Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies
Bibliographic record
Abstract
Technological advancements in wireless systems enable the advancement of traffic operations. When onboard equipment in connected vehicles transmits data to roadside sensors integrated with a traffic signal controller, green phases are redistributed to directly and indirectly reduce network delays. Despite the potential benefits of sensor technologies, the challenges associated with identifying optimal sensor locations for multiple time stages throughout a day with uncertain demand patterns has received little attention. In this paper, we focus on proactively reducing the network delay by controlling traffic signals through an optimized sensor deployment. The framework is based on portable sensors that may be repositioned within day and day to day to new locations such that delay savings over multiple time stages will be maximized. To tackle this multiperiod stochastic problem, dynamic models are proposed, considering the future sensor locations given budget constraints on the sensor costs and relocation costs, and the effect of control is tested on various demand profiles and penetration rates of an urban transportation network. A subproblem decomposed by Lagrangian relaxation enhanced with valid cuts has a better bound, and a variable neighborhood search algorithm quickly finds solutions. Two dynamic models that constrain flexible and restricted relocation, respectively, present higher savings compared to the stationary model without sensor relocation. The flexible relocation model guarantees higher savings than the restricted model by achieving the same maximum savings with fewer sensors. The gap between two dynamic models decreases when more sensors are available.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".