An Open Source Tool to Extract Traffic Data from Google Maps: Limitations and Challenges
Bibliographic record
Abstract
Road traffic modelling, analysis, and prediction require accurate and preprocessed spatiotemporal traffic data including measurements like traffic speed and count. Many existing and emerging surveillance systems are currently used to facilitate traffic data collection. Google Maps is a web mapping service that leverages GPS crowdsourcing to retrieve accurate traffic data verified by both the research community and industry. Google Maps facilitates APIs to provide access to this data with a paid subscription. Google Maps also make this traffic data publicly available through their web interface, but with limited features and requires further pre-processing. All existing tools to facilitate these publicly available traffic data through the Google Maps web interface is either lack essential functionalities or are proprietary. We have developed an open-source web-based data scraper tool to extract and export available traffic data from the Google Maps web interface in multiple usable formats. The tool provides a user-friendly interface that enables users to visually mark the locations of interests and to flexibly determine the required periods for data collections. Performance evaluation shows that the tool can retrieve traffic data from Google Maps in a linear time complexity with no significant computational overhead. Limitations and challenges to develop such tools are also investigated.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".