ORIGIN-DESTINATION TRAVEL SURVEY SOFTWARE: COGNITIVE AND TECHNOLOGICAL ASSISTANCE
Bibliographic record
Abstract
This paper presents a description of recently developed travel survey software which has been used in major mobility data acquisition surveys in the Greater Montreal Area (GMA). Mobility information obtained in such surveys forms the basis of critical technical activities such as transportation network planning, travel demand analysis and modeling, impact analysis and information dissemination to planners and network users. Classical uses of mobility data by transportation professionals are described. Contemporary issues are then presented, with a view to increasing interest in instruments which support renewed uses of mobility data. Large-scale travel surveys have played an important role in the planning culture of Montreal's transportation professionals for almost thirty years now. This culture has evolved at the pace of technological and methodological developments, and has constructed a large body of knowledge concerning residents' travel behavior over time. By carrying out a retrospective study of this evolution, we are able to define typical states of a technical era, called technological and cognitive spaces. Current applied knowledge and issues are a consequence of a succession of these spaces in time. In the GMA, the construction of detailed knowledge on mobility behavior has been based on observed trip information from travel surveys. These surveys serve as the interface for information transfer between users and planners. Concerns about mobility data quality, standards, effectiveness and relevance, as well as the availability of information technology, have led to the conception of tools to assist in data acquisition, validation and structuring, as well as uses of derived information. This subject merits a demonstration: evolving features and functions are illustrated through three types of Origin-Destination (O-D) survey software, specifically, the following: (1) onboard bys route O-D survey; (2) telephone-interviewed monthly pass O-D survey; and (3) large-scale regional telephone-interviewed household O-D survey. The relevance of the continuous large-scale telephone-interviewed household O-D survey has finally been addressed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".