Using crowd-sourced traffic data and open-source tools for urban congestion analysis
Bibliographic record
Abstract
• Quantitative Measurement of Congestion: The study uses crowd-sourced data to quantify urban transport congestion in Al Ain, UAE. • Identification of Open-Source Tools: Key open-source tools for congestion modeling are identified for effective management and policy-making. • Implications for Various Sectors: The study explores the impact of congestion data on health, environment, economics, and social services. • Integration of Traffic Information: Insights into merging traffic info with spatial data highlight congestion's broader urban impacts. • Limitations and Considerations: Limitations in crowd-sourced data include missing road details and vehicle classifications crucial for studies. Traffic congestion in urban areas poses significant challenges to city dwellers and consultants advising government. This study explores innovative methods to monitor and control traffic congestion, focusing on Al Ain city in the United Arab Emirates. Using the R Programming language and harnessing crowdsourced traffic information from HERE and Google Maps, the research delves into spatial data analysis. The methodology employed in this study builds on the previously applied congestion modeling methods for cities like Windsor, Toronto, and New York. The study focuses on Al Ain, addressing the scarcity of crowdsourced information-based congestion modeling research in the Middle East. The study details how to obtain and deploy crowdsourced traffic data, speed and jam factors, for a comprehensive visualization of the urban traffic congestion. For example, in the case of Al Ain, analysis showed an average traffic speed of 43 km per hour in Al Ain, where infrastructure could otherwise allow an average traffic speed of up to 51 km per hour under free flow conditions. The study findings highlight how traffic conditions, rather than speed limits, cause traffic flow disruptions in the city, which can inform traffic regulations. The study’s high-confidence real-time data emphasizes the reliability of crowdsourced traffic flow data. This research demonstrates the applicability of open-source traffic information for congestion modeling in the UAE, and establishes a replicable methodology for other urban areas worldwide, contributing significantly to the modeling methods.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".