Trip Attraction Rate Estimation Using Open Data Sources for Smart Transportation Planning
Bibliographic record
Abstract
Trip Attraction Rate (TAR) serves as a load factor utilized in aggregate transportation modeling. Conventional estimation consumes a lot of time and manpower for field surveys. Meanwhile, open data sources, such as Google Maps, offer an alternative approach to conducting a survey. The popular times feature from Google Maps can estimate coffee shop TAR in the city of Bandung. This study focuses on coffee shops in Bandung City. Data was collected using web scraping techniques on Google Maps and produced a sample size of 377 data points after the validity and reliability processes. Subsequently, multiple linear regression was employed to estimate TAR. The findings reveal that variables like building area and distance to public transportation hubs influence TAR simultaneously. Moreover, our approach could also distinguish TAR between weekdays (0.13 people/m2/hour) and weekends (0.14 people/m2/hour) in Bandung City. This result challenges the usage of Institute of Transportation Engineers (ITE) standards, which is often due to limitations of time and workforce in conducting surveys for TAR estimation. The difference in values indicates the need to estimate specific TAR for each city in Indonesia rather than relying on ITE values, and the proposed approach to using open data sources will shorten the estimation process.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".