Bus Network Microsimulation with General Transit Feed Specification and Tap-in-Only Smart Card Data
Bibliographic record
Abstract
Simulation models are vital instruments for properly evaluating proposed interventions on the public transport network. Because of data constraints, traditional planning models have been based on simplified representations of supply and demand. The goal of this paper is to demonstrate the feasibility of constructing a highly detailed public transit microsimulation model using general transit feed specification (GTFS) (complete planned service in the form of schedules transmitted to the public) and smart card data collected from a surface bus network. Since the validation protocol is tap-in only and the validation equipment by default provides no location information, algorithms are developed to derive boarding and alighting locations by using the recorded times of the fare validations. The GTFS schedule data are transformed into network format. The smart card data and GTFS data generate, respectively, highly detailed representations of transit demand and supply that are ideally suited for analysis using a powerful microsimulation tool (in this case, TRANSIMS). The results of these procedures demonstrate the feasibility of using smart card data and GTFS data as the basis for simulation of a surface transit network and offer the potential to dramatically increase the precision and versatility of public transport planning.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".