Encapsulating and Visualizing Disaggregated Origin–Destination Desire Lines to Identify Demand Corridors
Bibliographic record
Abstract
Origin–destination (O-D) data contain relevant information on the spatial structure of transportation demand; the data reveal the raw travel demand, notwithstanding transportation supply or networks. Visualizing an O-D matrix to identify demand corridors for small sets of data can be simple. In contrast, with the increasing complexity of travel paths, the amount of data collected is large and contains more information that needs to be analyzed. This process is challenging with significant conceptual and imaginary barriers. Spatial aggregation methods are usually used to reduce the complexity of O-D data. Such treatment, especially in an urban context, reduces the richness of data substantially. An approach is proposed to visualize an O-D matrix and to identify major corridors; this approach aims to cluster desire lines—the shortest and most direct trajectory between an origin and destination—so that the main concentrations of flow can be identified. The clustering of desire lines instead of O-D points allows the identification of what can be labeled raw demand corridors. Demand corridors may be used to identify collective trajectories, to evaluate transportation networks, and to propose strategies more adapted to transportation demand. The notion of corridors was clarified, and various applications of observed O-D sets from a survey in Montreal, Canada, were examined to identify major demand corridors. Results showed that demand collective corridors could be used as planning or decision-making tools. These corridors allow planners to encapsulate travel patterns, an approach that can assess the performance of current networks, identify potential development axes, or allow evaluation of corridor projects by comparing them with a reference unit.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".