TraClus-DL: A Desire Line Clustering Framework to Identify Demand Corridors
Bibliographic record
Abstract
Ideally, transportation networks should follow transportation demand in an unprocessed form as represented by desire lines (direct line between origin and destination points). To move from an individual to a collective scale, optimal individual paths need to be aggregated into efficient corridors. Conventionally, corridors are delimited based on existing transportation supply and networks. Hence, the structure of these networks is often the result of a trade-off between operational, administrative, and urban constraints. Confronting networks with unprocessed demand may help to diagnose the efficiency of transportation supply and how much it deviates from optimal corridors. TraClus is a general purpose framework for identifying zones with numerous movements given linear or non-linear trajectory data. In this paper the authors propose a framework called Trajectory Clustering for Desire Lines (TraClus-DL) inspired by TraClus but more specialized for the identification of demand corridors from origin-destination information, using characteristics such as spatial location, angles between lines and sampling weights. The functionality of TraClus-DL as a diagnostic tool for transportation supply is assessed and tested using a data set from the 2008 Origin-Destination travel survey conducted in 2008 in the Montreal area. The sensitivity of results with respect to parameter settings is evaluated. The paper demonstrates TraClus-DL’s adaptability for transportation planning and for decision making processes where a reference unit is required to evaluate projects. With intuitive and metric parameters and with exhaustive outputs TraClus-DL offer more possibilities to conduct deep analyses on corridor unit. Results are short demand corridors reflecting Wardrop's second principle where the collective total distance is optimised.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".