Log Analysis of Trip Calculation on a Transit Website
Bibliographic record
Abstract
The Montreal Transit Commission (MTC) and the Laval Transit Commission (LTC) put in place a transit user information system a few years ago which is accessible through their website. The functions of the system (calculation of transit paths, calculation of service accessibility, schedules) are used daily by thousands of Internet surfers. Of course, these systems have a dual function: in addition to providing information to transit users, they inform the organization about the mobility behaviors and trip patterns of these users. Moreover, examination of the logs of the localization search features of these websites makes it possible to enrich the subjacent geographical information system (GIS). This article emphasizes the potential of the analysis and use of trip calculators as tools in transit planning. First, it describes the fundamentals that should form the basis of transit websites in order to support planning: the Totally Disaggregate Approach (TDA) and Transportation Object-Oriented Modeling (TOOM). Then, it describes the system architecture and major functionalities of the trip calculator. Following presentation of some useful statistics, the focus shifts to an examination of the space-time characteristics of the website declarations and possible resections with origin-destination survey data. Finally, the paper lists the informational problems arising related to the search for georeferences in such an information system for the transit user. This paper introduces a new planning tool: the Transit User Information Website. In the coming years, it will be possible to use this site to conduct on-line transportation class surveys, with instant validation. The embedded GIS in this kind of tool will also put it in a better position to integrate the new technologies that are developed for Intelligent Transportation Systems (ITS).
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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".