An Android Smartphone Application for Collecting, Sharing, and Predicting Border Crossing Wait Time
Bibliographic record
Abstract
This paper introduces an Android smartphone application called the Toronto Buffalo Border Wait Time (TBBW) app, designed to collect, share and predict waiting time at the three Niagara Frontier border crossings, namely the Lewiston-Queenston Bridge, the Rainbow Bridge, and the Peace Bridge. The innovative app offers the user three types of waiting time estimates: (1) current waiting times collected at the crossings; (2) historical waiting times; and (3) future waiting time predicted for the next 15 minutes and updated every five minutes. For the current waiting time, the app can provide both the data collected by border crossing authorities as well as user-reported or “crowd-sourcing” data shared by the community of the app’s users. Reporting of the data could be done either manually or automatically through a GPS tracking function provided by the smartphone. For the historical waiting time, the app provides statistical charts and tables to help users choose the crossing with the likely shortest wait time. Future waiting times are predicted by a real-time stepwise traffic delay prediction model which consists of a short-term traffic volume forecasting model and a multi-server queueing model. To validate the prediction functionality of the app, its predictions were compared against real-world delay measurements for the entire month of May, 2014. The comparison showed that the model offered predictions with a mean absolute difference of 9.22 minutes. When considering only delays that are greater than 10 minutes, the model has a mean absolute difference of only 6.95 minutes. The ability to integrate officially reported delay estimates with crowd-sourcing data, and the ability to provide future border wait times clearly distinguish the TBBW app from others on the market.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".