Dynamic OD estimation using three phase traffic flow theory
Bibliographic record
Abstract
Abstract Advanced Transportation Management and Information Systems (ATMIS) can use dynamic origin–destination (OD) demand models to make short‐term predictions regarding developments in traffic states. However, existing dynamic OD prediction models do not achieve this reliably for two main reasons. First, this is a bi‐level system that consists of a traffic flow process at the lower level and a dynamic OD process at the upper level. Due to the inherent non‐convexity of bi‐level systems, it is difficult to guarantee that any calculated solution is globally optimal. In this paper, we propose a new traffic flow model that uses real‐time traffic data, such as traffic flows, speed and occupancy, collected from vehicle detectors, to address the difficulties that arise in existing bi‐level programming formulations. Second, in order to estimate a dynamic OD demand between on and off‐ramps on the freeways, a traffic flow model is needed to estimate the proportion of traffic moving between them. In this paper, we present a dynamic traffic estimation model based on Kerner's 1 three‐phase traffic theory, which represents the complexity of traffic phenomena based on phase transitions between free‐flow, synchronized flow and moving jam phases, and on their complex nonlinear spatio‐temporal features. The present model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We show how a genetic algorithm can be used to solve this to estimate dynamic OD flows and the associated link, on and off‐ramp flows during each time interval using traffic data collected from vehicle detection systems implemented on Korean freeways. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".