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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".