Graph-Based Dynamic Modeling and Traffic Prediction of Urban Road Network
Bibliographic record
Abstract
Based on various on-road sensor observations, dynamic modeling and analysis of urban road networks becomes an important task of an intelligent transportation system. The major difficulty of this task is that traffic states vary dynamically in both spatial and temporal domains. In this paper, we propose a novel urban road network modeling approach. A road network is described by a weighted undirected graph composed of vertices and edges denoting, respectively, traffic intersections and their pairwise connections. Given the topology of the network, an effective weight estimation algorithm is proposed to extract spatial correlations among adjacent traffic intersections from physical sensor observations. Graph weights can be regularly updated to capture the dynamic essence of traffic states over time. On the basis of weight estimation, we further develop a dynamic spatio-temporal traffic prediction model by using the spatio-temporal autoregressive integrated moving average (STARIMA) model. The effectiveness of the proposed graph-based STARIMA is validated by a series of numerical experiments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".