Optimal roadside units location for path flow reconstruction in a connected vehicle environment
Bibliographic record
Abstract
The path flow reconstruction problem is used to determine the minimum set of links that must be equipped with traffic monitoring devices to identify vehicle paths in a road network. This study addresses the path flow reconstruction problem in a connected vehicles (CVs) environment. Unlike traditional sensors that can observe both CVs and non-connected vehicles (NCVs), CV-enabled infrastructures, known as roadside units (RSUs), can only identify CVs on roads through vehicle to infrastructure (V2I) communications. They can, however, provide critical traffic information, including traces of the historical trajectories of CVs and possibly the desired path to a destination, thereby inferring partial information on links that are not directly covered by RSUs. RSUs have an “area” rather than a “point” coverage capability. This allows them to simultaneously monitor more than one link. We mathematically developed four variant formulations for the path flow reconstruction problem to optimally locate either a network’s RSU or a mix of the network’s RSUs and automatic vehicle identification (AVI) sensors. The first two models assume 100% market penetration of CVs and the first model determines the links that should be directly covered by RSUs in a road network. While the desired path to a destination is assumed to be unknown. This model determines an upper bound for the number of RSUs required to fully reconstruct path flows by using each RSU to directly cover a link. To consider the coverage and range of the RSU (where RSU can cover more than one link) and to minimize the total cost, Model II optimizes the locations of traditional AVI sensors and RSUs. This allows the model to capitalize on the RSUs’ area and indirect coverage features to fully reconstruct path flow in a road network. Model III considers the gradual deployment of CVs and thus the prevailing mixed traffic environment consisting of both CVs and NCVs. Accordingly, this model relaxes the assumption of a 100% penetration rate of CVs and maximizes the path flow information gain subject to a budget constraint in a mixed traffic environment. Finally, Model IV explores the infrastructure to infrastructure (I2I) communication capability among RSUs, further maximizing the traffic flow information gain of CVs while guaranteeing full path flow reconstruction. The results suggest that fewer RSUs than AVI sensors are required to reach full path flow reconstruction in a road network. The level of unique path flow information obtained from RSUs is also considerably higher than what can be obtained from AVI sensors. It is demonstrated that, in mixed traffic conditions, the coverage range of RSUs and their cost compared to AVI sensors can significantly affect the deployment of either type of sensing devices for maximized path flow information gain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".